TitleType of PublicationYear of PublicationAuthorsJournal TitleAbstractDOIIssuePaginationVolume
GeoQuery: Integrating HPC systems and public web-based geospatial data toolsJournal Article2019S. Goodman; A. BenYishay; Z. Lv; D. RunfolaComputers & Geosciences

Interdisciplinary use of geospatial data requires the integration of data from a breadth of sources, and frequently involves the harmonization of different methods of sampling, measurement, and technical data types. These integrative efforts are often inhibited by fundamental geocomputational challenges, including a lack of memory efficient or parallel processing approaches to traditional methods such as zonal statistics. GeoQuery (geoquery.org) is a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome these challenges. Through an online interface, GeoQuery users can request geospatial data - which spans categories including geophysical, environmental and social measurements - to be aggregated to user-selected units of analysis (e.g., subnational administrative boundaries). Once a request has been processed, users are provided with permanent links to access their customized data and documentation. Datasets made available through GeoQuery are reviewed, prepared, and provisioned by geospatial data specialists, with processing routines tailored for each dataset. The code used and steps taken while preparing datasets and processing user requests are publicly available, ensuring transparency and replicability of all data and processes. By mediating the complexities of working with geospatial data, GeoQuery reduces the barriers to entry and the related costs of incorporating geospatial data into research across disciplines. This paper presents the technology and methods used by GeoQuery to process and manage geospatial data and user requests.

https://doi.org/10.1016/j.cageo.2018.10.009103 - 112122
An open source web application for distributed geospatial data explorationJournal Article2019P.A. Curry; N. Moosdorf190014 - 6
Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literatureJournal Article2019G. Muñoz; D. Kissling; E. van LoonBiodiversity Data Journal10.3897/BDJ.7.e28737e287377
DEIMS-SDR – A web portal to document research sites and their associated dataJournal Article2019C. Wohner; J. Peterseil; D. Poursanidis; T. Kliment; M. Wilson; M. Mirtl; N. ChrysoulakisEcological Informatics

Climate change and other drivers are affecting ecosystems around the globe. In order to enable a better understanding of ecosystem functioning and to develop mitigation and adaptation strategies in response to environmental change, a broad range of information, including in-situ observations of both biotic and abiotic parameters, needs to be considered. Access to sufficient and well documented in-situ data from long term observations is therefore one of the key requirements for modelling and assessing the effects of global change on ecosystems. Usually, such data is generated by multiple providers; often not openly available and with improper documentation. In this regard, metadata plays an important role in aiding the findability, accessibility and reusability of data as well as enabling reproducibility of the results leading to management decisions. This metadata needs to include information on the observation location and the research context. For this purpose we developed the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a research and monitoring site registry (https://www.deims.org/) that not only makes it possible to describe in-situ observation or experimentation sites, generating persistent, unique and resolvable identifiers for each site, but also to document associated data linked to each site. This article describes the system architecture and illustrates the linkage of contextual information to observational data. The aim of DEIMS-SDR is to be a globally comprehensive site catalogue describing a wide range of sites, providing a wealth of information, including each site's location, ecosystems, facilities, measured parameters and research themes and enabling that standardised information to be openly available.

https://doi.org/10.1016/j.ecoinf.2019.01.005
Influence of body size, topography, food availability and tree-fall gaps on space use by yellow-footed tortoises (Chelonoidis denticulatus) in Central AmazoniaJournal Article2019A.S. Tavares; T.Queiroz Morcatty; J. Zuanon; W.E. MagnussonPLOS ONE

{Habitat selection and extension of the area used by a given species may vary during different phases of its life and are often determined by the distribution and availability of resources throughout the landscape, such as food, breeding sites, and shelters. In this study, we assessed the influence of body size on the areas used by 21 individuals of the yellow-footed tortoises (Chelonoidis denticulatus) from January to June 2017 in a dense rain forest area in Central Amazonia. We also investigated whether individuals selected different ranges of terrain slope, elevation, areas with high food availability, or areas with treefall gaps that could be used for shelter or thermoregulation. We monitored tortoise movements using thread-bobbins, and sampled terrain characteristics, availability of potential food resources and forest gaps along the routes used by the tortoises. We also measured the same variables in plots distributed systematically throughout the study area to evaluate resource availability. Tortoises used an average area of 1.56 ha (SD = 1.51

10.1371/journal.pone.02118691-1814
Lightweight data management with dtoolJournal Article2019T.S.G. Olsson; M. Hartley PeerJ https://doi.org/10.7717/peerj.6562e65627
Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories.Journal Article2019M. Wu; F. Psomopoulos; S.J. Khalsa; A. de WaardData Science Journal http://doi.org/10.5334/dsj-2019-003118
Collaborative Research: Predicting post-wildfire sedimentation of reservoirs: probabilistic modeling of debris flow generation and downstream sediment routingReport2019B. MurphyFunded Research Records
Establishing a Research Data Management Service on a Health Sciences CampusJournal Article2019K. Vela; N. ShinJournal of eScience Librarianshiphttps://doi.org/10.7191/jeslib.2019.114618
Biodiversity data integration–The significance of data resolution and domainJournal Article2019C. König; P. Weigelt; J. Schrader; A. Taylor; J. Kattge; H. KreftPLOS Biology

This Essay highlights data resolution as central property of biodiversity data that affects the precision and representativeness of macroecological inferences. It also discusses ways to maximize synergies among data types and showcases the potential of cross-resolution, cross-domain data integration.

10.1371/journal.pbio.30001831-1617
A two-tiered curriculum to improve data management practices for researchersJournal Article2019K.B. Read; C. Larson; C. Gillespie; S.Young Oh; A. SurkisPLOS ONE

Background Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher’s results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities. Materials and methods We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers’ satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class. Results and discussion The online curriculum increased librarians’ self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.

10.1371/journal.pone.02155091-1414
Ecological Data Should Not Be So Hard to Find and ReuseJournal Article2019T. Poisot; A. Bruneau; A. Gonzalez; D. Gravel; P. Peres-NetoTrends in Ecology & Evolution

Drawing upon the data deposited in publicly shared archives has the potential to transform the way we conduct ecological research. For this transformation to happen, we argue that data need to be more interoperable and easier to discover. One way to achieve these goals is to adopt domain-specific data representations.

https://doi.org/10.1016/j.tree.2019.04.005
The Global Lake Ecological Observatory NetworkBook Chapter2018P.C. Hanson; K.C. Weathers; H.A. Dugan; C. GriesEcological Informaticshttps://doi.org/10.1007/978-3-319-59928-1_19415-433
Functional Requirements for Research Data RepositoriesJournal Article2018S. KimInternational Journal of Knowledge Content Development & Technology18
Cyberinfrastructure for Digital Libraries and Archives: Integrating Data Management, Analysis, and PublicationConference Paper2018W. Xu; M. Esteva; J. TreloganProceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries10.1145/3197026.3200211
A network of monitoring networks for evaluating biodiversity conservation effectiveness in Brazilian protected areasJournal Article2018Fde Oliveir Roque; M. Uehara-Prado; F. Valente-Neto; J.Manuel Och Quintero; K.Torres Ribeiro; M.Bonifacio Martins; M.Gonçalves de Lima; F.L. Souza; E. Fischer; U.Lopes da Silva; F.Yoko Ishida; A. Gray-Spence; J.Onofre Per Pinto; D.Bandini Ribeiro; Cde Araujo Martins; P.Cyril Renaud; O. Pays; W.E. MagnussonPerspectives in Ecology and Conservation

The necessity to create national to global-scale biodiversity monitoring systems as part of assessing progress toward biodiversity agendas presents a challenge for signatory countries. This is a brief review of ongoing Brazilian national initiatives that would allow the construction of a general biomonitoring network scheme in protected areas; with additional focus on linking independent monitoring schemes. We discuss some key aspects needed to include monitoring schemes under a single framework that will lead to better evaluation of pressure–state–response indicators for managing biodiversity at several scales; and we point out the potential of embracing citizen science and participatory monitoring to quantify some aspects within those schemes.

https://doi.org/10.1016/j.pecon.2018.10.003177 - 18516
Addressing Biological Informatics Workforce Needs: A Report from the AIBS CouncilJournal Article2018T.M. Beardsley; R.E. Gropp; J.M. VerdierBioScience10.1093/biosci/biy116847-85368
Integrating Data Science Tools into a Graduate Level Data Management CourseJournal Article2018P.E. Pascuzzi; M.R.Sapp NelsonJournal of eScience Librarianship10.7191/jeslib.2018.115237
Decoding plant communities across scalesJournal Article2018E.I. Damschen

Characteristics shared by different plant species can be used to decipher where they are found in nature. A new global analysis indicates that using this code to understand what shapes plant communities must consider spatial scale.

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Design Criteria of Korean LTER Data Platform Model for Full Life-cycle Data ManagementJournal Article2017T. Huh; G. Park; S. Ahn; S. Hwang; H. JungInternational Journal of Applied Engineering Research3336-34212
The International Long Term Ecological Research Network: a platform for collaborationJournal Article2017K. Vanderbilt; E. GaiserEcosphere10.1002/ecs2.16972e01697–n/a8
Citizen science can improve conservation science, natural resource management, and environmental protectionJournal Article2017D.C. McKinley; A.J. Miller-Rushing; H.L. Ballard; R. Bonney; H. Brown; S.C. Cook-Patton; D.M. Evans; R.A. French; J.K. Parrish; T.B. Phillips; S.F. Ryan; L.A. Shanley; J.L. Shirk; K.F. Stepenuck; J.F. Weltzin; A. Wiggins; O.D. Boyle; R.D. Briggs; S.F.Chapin III; D.A. Hewitt; P.W. Preuss; M.A. SoukupThe role of citizen science in biological conservation15 - 28208
The science of citizen science: Exploring barriers to use as a primary research toolJournal Article2017H.K. Burgess; L.B. DeBey; H.E. Froehlich; N. Schmidt; E.J. Theobald; A.K. Ettinger; J. HilleRisLambers; J. Tewksbury; J.K. ParrishThe role of citizen science in biological conservation113 - 120208
Identification and characterization of information-networks in long-tail data collectionsJournal Article2017M.M. Elag; P. Kumar; L. Marini; J.D. Myers; M. Hedstrom; B.A. PlaleEnvironmental Modelling & Softwarehttp://doi.org/10.1016/j.envsoft.2017.03.032100 - 11194
Becoming DataONE Tier-4 Member Node: Steps taken by the Nevada Research Data CenterConference Paper2017M. Hossain; H. Muñoz; R. Wu; E. Fritzinger; S.M. Dascalu; F.C. Harris2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP)10.1109/OPTIM.2017.7975117
Provenance and ReproducibilityBook Chapter2017F. Chirigati; J. FreireEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_80747-11–5
Scientific data from and for the citizenJournal Article2017S. Schade; C. Tsinaraki; E. RogliaFirst Monday

Powered by advances of technology, today’s Citizen Science projects cover a wide range of thematic areas and are carried out from local to global levels. This wealth of activities creates an abundance of data, for example, in the forms of observations submitted by mobile phones; readings of low-cost sensors; or more general information about peoples’ activities. The management and possible sharing of this data has become a research topic in its own right. We conducted a survey in the summer of 2015 in order to collectively analyze the state of play in Citizen Science. This paper summarizes our main findings related to data access, standardization and data preservation. We provide examples of good practices in each of these areas and outline actions to address identified challenges.

10.5210/fm.v22i8.784222
Data Quality in Mobile Sensing Datasets for Pervasive HealthcareBook Chapter2017N. Hernández; L.A. Castro; J. Favela; L. Michán; B. ArnrichHandbook of Large-Scale Distributed Computing in Smart Healthcare

Mobile sensing is becoming a popular approach for inferring patterns of activity and behavior to determine how they affect health and wellbeing. This data-driven approach has the potential to become a major tool in the field of epidemiology, aimed at determining the causes of disease in populations, as well as motivating behavior change. These sensing technologies are generating large datasets that demand significant processing and data management resources. Studies in mobile sensing for healthcare have motivated the creation of large, complex datasets with information opportunistically gathered from distributed sensors in mobile devices. In this chapter, we discuss some of the architectural challenges regarding data gathering in this distributed data-intensive environment such as the healthcare industry, as well as issues regarding the organization and sharing of the large amounts of data collected. Some of these issues include the heterogeneity of the devices, diversity of sensors used, and the need for data provenance when integrating datasets from diverse studies. We highlight that assessing data quality is of paramount importance for conducting longitudinal studies and building on historical knowledge as new data become available. Finally, we identify future research topics in the growing field of mobile sensing and its application to healthcare and wellbeing. We discuss aspects of data curation, data quality, and data provenance, and we provide suggestions on how these challenges could be addressed in the near future.

10.1007/978-3-319-58280-1_9217–238
Grassroots Professional Development via the New England Research Data Management RoundtablesJournal Article2017T.P. Atwood; P.B. Condon; J. Goldman; T. Hohenstein; C.V. Mills; Z.W. PainterJournal of eScience Librarianship https://doi.org/10.7191/jeslib.2017.111126
Albertsons Library Data Management Strategic Agenda Summer 2017 - Spring 2019Report2017A. Sherman; H. Grevatt; M. Davis; M. ArmstrongBoise State University ScholarWorkshttp://dx.doi.org/10.18122/B2K709
Software Application Profile: Opal and Mica: open-source software solutions for epidemiological data management, harmonization and disseminationJournal Article2017D. Doiron; Y. Marcon; I. Fortier; P. Burton; V. FerrettiInternational Journal of Epidemiology10.1093/ije/dyx180dyx180
Data Management Training Modules: An Initial Survey and Comparison ResultJournal Article2016C.Y. Hou; M. Mayernik
Environmental Virtual Observatories (EVOs): prospects for knowledge co-creation and resilience in the Information AgeJournal Article2016T. Karpouzoglou; Z. Zulkafli; S. Grainger; A. Dewulf; W. Buytaert; D.M. HannahCurrent Opinion in Environmental Sustainability10.1016/j.cosust.2015.07.01540–4818
Bringing Federated Identity to Grid ComputingConference Paper2016J. Teheran; D. Dykstra; M. AltunayProceedings of the 11th Annual Cyber and Information Security Research Conference10.1145/2897795.2897807
Addressing Scientific Rigor in Data Analytics Using Semantic WorkflowsBook Chapter2016J.S. Erickson; J. Sheehan; K.P. Bennett; D.L. McGuinnessProvenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings187 - 190
Big data for forecasting the impacts of global change on plant communitiesJournal Article2016J. Franklin; J.M. Serra-Diaz; A.D. Syphard; H.M. ReganGlobal Ecology and Biogeography10.1111/geb.12501n/a–n/a
Information management at the North Temperate Lakes Long-term Ecological Research site — Successful support of research in a large, diverse, and long running projectJournal Article2016C. Gries; M.R. Gahler; P.C. Hanson; T.K. Kratz; E.H. StanleyEcological Informaticshttp://dx.doi.org/10.1016/j.ecoinf.2016.08.007-
A Brief Tour through Provenance in Scientific Workflows and DatabasesReport2016B. LudäscherUniversity of Illinois Research and Tech Reports - Computer Science

Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.

A Scientific Data Management Infrastructure for Environmental Monitoring and ModellingConference Paper2016D. Henzen; M. Mueller; S. Jirka; I. Senner; T. Kaeseberg; J. Zhang; L. Bernard; P. Krebs
The environment ontology in 2016: bridging domains with increased scope, semantic density, and interoperationJournal Article2016P.Luigi Buttigieg; E. Pafilis; S. Lewis; M.P. Schildhauer; R.L. Walls; C.J. Mungall

The Environment Ontology (ENVO; http://www.environmentontology.org/ ), first described in 2013, is a resource and research target for the semantically controlled description of environmental entities. The ontology's initial aim was the representation of the biomes, environmental features, and environmental materials pertinent to genomic and microbiome-related investigations. However, the need for environmental semantics is common to a multitude of fields, and ENVO's use has steadily grown since its initial description. We have thus expanded, enhanced, and generalised the ontology to support its increasingly diverse applications.

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KNLTER Network: Facilitating Global Data-SharingJournal Article2016T. Huh; S. Ahn; D. Nam; H.K. JungInternational Journal of Database Theory and Application10.14257/ijdta2016.2016.9.229241-2509
DOIs and Other Persistent Identifiers in Research DataPresentation2016E. BarskyLibrary Staff Papers and Presentationshttp://dx.doi.org/10.14288/1.0319238
Data LifecycleBook Chapter2016W. Bishop; T.H. GrubesicGeographic Information: Organization, Access, and Use10.1007/978-3-319-22789-4_9169–186
How Do Scientists Determine Data Reusability?: A Quasi-experiment Think-aloud StudyConference Paper2016A.P. MurilloProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives Through Information & Technology
Comparing Internal and External Interoperability of Digital InfrastructuresConference Paper2016S. Sharma; S. SawyerProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives Through Information & Technology
Persistence statements: describing digital stickinessReport2016J. Kunze; S. Calvert; J. DeBarry; M. Hanlon; G. Janée; S. SweatCDL Staff Publications
The Next Decade of Big Data in Ecosystem ScienceJournal Article2016S.L. LaDeau; B.A. Han; E.J. Rosi-Marshall; K.C. WeathersEcosystems

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term `big data' implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.

10.1007/s10021-016-0075-y1–10
A Digital Repository and Execution Platform for Interactive Scholarly Publications in NeuroscienceJournal Article2016V. Hodge; M. Jessop; M. Fletcher; M. Weeks; A. Turner; T. Jackson; C. Ingram; L. Smith; J. Austin

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.

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Data sharing tools adopted by the European Biodiversity Observation Network ProjectJournal Article2016L. Smirnova; P. Mergen; Q.John Groom; A. De Wever; L. Penev; P. Stoev; I. Pe’er; V. Runnel; A.Garc&iacut Camacho; T. Vincent; D. Agosti; C. Arvanitidis; F.Javier Bon Bonet; H. SaarenmaaResearch Ideas and Outcomes

A fundamental constituent of a biodiversity observation network is the technological infrastructure that underpins it. The European Biodiversity Network project (EU BON) has been working with and improving upon pre-existing tools for data mobilization, sharing and description. This paper provides conceptual and practical advice for the use of these tools. We review tools for managing metadata, occurrence data, and ecological data and give detailed description of these tools, their capabilities and limitations. This is followed by recommendations on their deployment and possible future enhancements. This is done from the perspective of the needs of the biodiversity observation community with a view to the development of a unified user interface to these data – the European Biodiversity Portal (EBP). We described the steps taken to develop, adapt, deploy and test these tools. This document also gives an overview of the objectives that still need to be achieved and challenges to be addressed for the remainder of the project.

10.3897/rio.2.e9390e93902
The data life cycle applied to our own dataJournal Article2015A. Goben; R. RaszewskiJournal of the Medical Library Association : JMLA

Increased demand for data-driven decision making is driving the need for librarians to be facile with the data life cycle. This case study follows the migration of reference desk statistics from handwritten to digital format. This shift presented two opportunities: first, the availability of a nonsensitive data set to improve the librarians' understanding of data-management and statistical analysis skills, and second, the use of analytics to directly inform staffing decisions and departmental strategic goals. By working through each step of the data life cycle, library faculty explored data gathering, storage, sharing, and analysis questions.

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Facilitating open exchange of data and informationJournal Article2015J. Gallagher; J. Orcutt; P. Simpson; D. Wright; J. Pearlman; L. RaymondEarth Science Informatics10.1007/s12145-014-0202-21-19
Cataloguing to Facilitate Big Data AnalyticsConference Paper2015M.Kumar Singh; D.K. Singh
Data Information Literacy and Undergraduates: A Critical CompetencyJournal Article2015Y. ShorishCollege & Undergraduate Libraries10.1080/10691316.2015.100124697-10622
Complex Environmental Forcing across the Biogeographical Range of Coral PopulationsJournal Article2015E.B. Rivest; T.C. GouhierPLoS ONE10.1371/journal.pone.0121742310
Meeting the Needs of Data Management Training: The Federation of Earth Science Information Partners (ESIP) Data Management for Scientists Short CourseJournal Article2015C.Y. HouIssues in Science and Technology Librarianship
Open Data in Global Environmental Research: The Belmont Forum’s Open Data SurveyJournal Article2015B. Schmidt; B. Gemeinholzer; A. Treloar
Preservation challenges for geological data at state geological surveysJournal Article2015S. RamdeenGeoResJ213–2206
A Resource Centric Approach For Advancing Collaboration Through Hydrologic Data And Model SharingJournal Article2015D.G. Tarboton; R. Idaszak; J. Horsburgh; J. Heard; D. Ames; A. Couch; J. Goodall; L.E. Band; V. Merwade
Ten simple rules for experiments’ provenanceJournal Article2015T. KazicPLoS Comput Biol10.1371/journal.pcbi.1004384e100438411
Data Management Plans & The DMPToolJournal Article2015S. Sheehan
Outside the Four Corners: Exploring Non-Traditional Scholarly CommunicationJournal Article2015J. Burpee; B. Glushko; L. Goddard; I. Kehoe; P. MooreScholarly and Research Communication6
Fostering ecological data sharing: collaborations in the International Long Term Ecological Research NetworkJournal Article2015K.L. Vanderbilt; C.C. Lin; S.S. Lu; A.Rahman Kassim; H. He; X. Guo; I. San Gil; D. Blankman; J.H. PorterEcosphere10.1890/ES14-00281.11–186
Research Data Management Self-Education for Librarians: A WebliographyJournal Article2015A. Goben; R. RaszewskiIssues in Science and Technology Librarianship10.5062/F4348HCK
Analyzing the Data Management Environment in a Master's-level InstitutionJournal Article2015A. Stamatoplos; T. Neville; D. HenryThe Journal of Academic Librarianship10.1016/j.acalib.2015.11.004
Addressing global data sharing challengesJournal Article2015G.C. Alter; M. VardiganJournal of Empirical Research on Human Research Ethics10.1177/1556264615591561317–32310
It's Good to Share: Why Environmental Scientists’ Ethics Are Out of DateJournal Article2015P.A. Soranno; K.S. Cheruvelil; K.C. Elliott; G.M. MontgomeryBioScience

Although there have been many recent calls for increased data sharing, the majority of environmental scientists do not make their individual data sets publicly available in online repositories. Current data-sharing conversations are focused on overcoming the technological challenges associated with data sharing and the lack of rewards and incentives for individuals to share data. We argue that the most important conversation has yet to take place: There has not been a strong ethical impetus for sharing data within the current culture, behaviors, and practices of environmental scientists. In this article, we describe a critical shift that is happening in both society and the environmental science community that makes data sharing not just good but ethically obligatory. This is a shift toward the ethical value of promoting inclusivity within and beyond science. An essential element of a truly inclusionary and democratic approach to science is to share data through publicly accessible data sets.

10.1093/biosci/biu16969-7365
Making Data Management Accessible in the Undergraduate Chemistry CurriculumJournal Article2014B.A. Reisner; K.T.L. Vaughan; Y.L. ShorishJournal of Chemical Education10.1021/ed500099h111943-194691
Building the New England Collaborative Data Management CurriculumJournal Article2014D. Kafel; A.T. Creamer; E.R. MartinJournal of eScience Librarianship160-663
Gaining Traction in Research Data Management Support: A Case StudyJournal Article2014D.L. O'MalleyJournal of eScience Librarianship174-793
NSF Data Management PlansJournal Article2014T.P. Atwood
Ensuring research integrity The role of data management in current crisesJournal Article2014H. CoatesCollege & Research Libraries News598–60175
Using Rich Context and Data Exploration to Improve Engagement with Climate Data and Data Literacy: Bringing a Field Station into the College ClassroomJournal Article2014A.L. Ellwein; L.M. Hartley; S. Donovan; I. BillickJournal of Geoscience EducationJournal of Geoscience Education10.5408/13-0344578 - 58662
Managing & Sharing Your Research DataJournal Article2014K. Anderson
Data Management Quick GuidesJournal Article2014J.L. Thoegersen
A balancing act: The ideal and the realistic in developing Dryad’s preservation policyJournal Article2014S. Mannheimer; A. Yoon; J. Greenberg; E. Feinstein; R. ScherleFirst Monday10.5210/fm.v19i8.5415 19
Research Center Insights into Data Curation Education and CurriculumBook Chapter2014M.S. Mayernik; L. Davis; K. Kelly; B. Dattore; G. Strand; S.J. Worley; M. MarlinoTheory and Practice of Digital Libraries – TPDL 2013 Selected Workshops10.1007/978-3-319-08425-1_26239-248416
Emerging Data Management Roles for Health Librarians in Electronic Medical RecordsJournal Article2014M. Corbett; A. Deardorff; I. Kovar-GoughJournal of the Canadian Health Libraries Association / Journal de l'Association des bibliothèques de la santé du Canada

Objective: To examine current and developing data management roles and opportunities for health librariansto become involved in electronic medical record (EMR) initiatives. This paper focuses on the Canadian context but has implications farther afield. Methods: To accomplish a state-of-the-art review, searches were conducted in the library and information science databases (LISTA, LISA), biomedical databases (MEDLINE, CINAHL, EMBASE), and on the web for grey literature. Keywords included: clinical librarian, health science librarian, medical librarian, hospital librarian, medical informationist, electronic medical record, EMR, electronic health record, EHR, data management, data curation, health informatics, e-science, and e-science librarianship. MeSH subject headings used were: Medical Records Systems, Computerized/, Electronic Health Records/, and libraries/. Results: There is little evidence of Canadian health librarians’ current involvement in EMR initiatives, but examples from the United States indicate that health librarians’ participation is primarily in system implementation, creating links to the medical literature, and using EMRs to provide patient health information. Further roles for health librarians are emerging in this area as health librarians draw on their core competencies and learn from e-science librarianship to create new opportunities. Data management examples from e-science librarianship, such as building data dictionaries and data management plans and infrastructure, give further direction to health librarians’ involvement in EMRs. Conclusion: As EMRs gradually become more popular in Canada, Canadian health librarians should seek further opportunities for education and outreach to become more involved with these EMR initiatives.

55–59} doi = {10.5596/c14-02235
Data management lifecycle and software lifecycle management in the context of conducting scienceJournal Article2014C. Lenhardt; S. Ahalt; B. Blanton; L. Christopherson; R. IdaszakJournal of Open Research Software10.5334/jors.axe152
Harnessing the power of big data: infusing the scientific method with machine learning to transform ecologyJournal Article2014D.P.C. Peters; K.M. Havstad; J. Cushing; C. Tweedie; O. Fuentes; N. Villanueva-RosalesEcosphere10.1890/ES13-00359.11–155
Harnessing bits and bytes to transform ecology educationJournal Article2014R.D. Stevenson; K.M. Klemow; L.J. GrossFrontiers in Ecology and the Environment10.1890/1540-9295-12.5.306306–30712
Data management in astrobiology: Challenges and opportunities for an interdisciplinary communityJournal Article2014A.U. Aydinoglu; T. Suomela; J. MaloneAstrobiology10.1089/ast.2013.1127451–46114
Implementing a graduate-level data information literacy curriculum at Oregon State University: approach, outcomes and lessons learnedJournal Article2014A.L. Whitmire
Data Curation: A Study of Researcher Practices and NeedsJournal Article2014M. McLure; A.V. Level; C.L. Cranston; B. Oehlerts; M. Culbertsonportal: Libraries and the Academy139–16414
Measuring the value of research data: a citation analysis of oceanographic data setsJournal Article2014C.W. BelterPloS one10.1371/journal.pone.0092590e925909
Taking the pulse of a continent: expanding site-based research infrastructure for regional- to continental-scale ecologyJournal Article2014D.P.C. Peters; H.W. Loescher; M.D. SanClements; K.M. HavstadEcosphere10.1890/ES13-00295.11–235
Educating researchers for effective data managementJournal Article2014C. EakerBulletin of the American Society for Information Science and Technology10.1002/bult.2014.172040031445–4640
Completing the data life cycle: using information management in macrosystems ecology researchJournal Article2014J. Rüegg; C. Gries; B. Bond-Lamberty; G.J. Bowen; B.S. Felzer; N.E. McIntyre; P.A. Soranno; K.L. Vanderbilt; K.C. WeathersFrontiers in Ecology and the Environment10.1890/12037524–3012
Approaches to advance scientific understanding of macrosystems ecologyJournal Article2014O. Levy; B.A. Ball; B. Bond-Lamberty; K.S. Cheruvelil; A.O. Finley; N.R. Lottig; S.W. Punyasena; J. Xiao; J. Zhou; L.B. Buckley; C.T. Filstrup; T.H. Keitt; J.R. Kellner; A.K. Knapp; A.D. Richardson; D. Tcheng; M. Toomey; R. Vargas; J.W. Voordeckers; T. Wagner; J.W. WilliamsFrontiers in Ecology and the Environment10.1890/13001915–2312
A Workflow Model for Curating Research Data in the University of Minnesota Libraries: Report from the 2013 Data Curation PilotJournal Article2014L.R. Johnston
Tribute to Tinbergen: Public Engagement in EthologyJournal Article2014J. Hecht; C.B. CooperEthology10.1111/eth.12199207–214120
The eBird enterprise: An integrated approach to development and application of citizen scienceJournal Article2014B.L. Sullivan; J.L. Aycrigg; J.H. Barry; R.E. Bonney; N. Bruns; C.B. Cooper; T. Damoulas; A.A. Dhondt; T. Dietterich; A. Farnsworth; D. Fink; J.W. Fitzpatrick; T. Fredericks; J. Gerbracht; C. Gomes; W.M. Hochachka; M.J. Iliff; C. Lagoze; F.A. La Sorte; M. Merrifield; W. Morris; T.B. Phillips; M. Reynolds; A.D. Rodewald; K.V. Rosenberg; N.M. Trautmann; A. Wiggins; D.W. Winkler; W.K. Wong; C.L. Wood; J. Yu; S. KellingBiological Conservation

Abstract Citizen-science projects engage volunteers to gather or process data to address scientific questions. But citizen-science projects vary in their ability to contribute usefully for science, conservation, or public policy. eBird has evolved from a basic citizen-science project into a collective enterprise, taking a novel approach to citizen science by developing cooperative partnerships among experts in a wide range of fields: population and distributions, conservation biologists, quantitative ecologists, statisticians, computer scientists, \{GIS\} and informatics specialists, application developers, and data administrators. The goal is to increase data quantity through participant recruitment and engagement, but also to quantify and control for data quality issues such as observer variability, imperfect detection of species, and both spatial and temporal bias in data collection. Advances at the interface among ecology, statistics, and computer science allow us to create new species distribution models that provide accurate estimates across broad spatial and temporal scales with extremely detailed resolution. eBird data are openly available and used by a broad spectrum of students, teachers, scientists, NGOs, government agencies, land managers, and policy makers. Feedback from this broad data use community helps identify development priorities. As a result, eBird has become a major source of biodiversity data, increasing our knowledge of the dynamics of species distributions, and having a direct impact on the conservation of birds and their habitats.

http://dx.doi.org/10.1016/j.biocon.2013.11.00331 - 40169
Documenting, storing, and executing models in Ecology: A conceptual framework and real implementation in a global change monitoring programJournal Article2014F.J. Bonet; R. Pérez-Pérez; B.M. Benito; F.Suzart de Albuquerque; R. ZamoraEnvironmental Modelling & Software

Abstract Many of the best practices concerning the development of ecological models or analytic techniques published in the scientific literature are not fully available to modelers but rather are stored in scientists' digital or biological memories. We propose that it is time to address the problem of storing, documenting, and executing ecological models and analytical procedures. In this paper, we propose a conceptual framework to design and implement a web application that will help to meet this challenge. This tool will foster cooperation among scientists, enhancing the creation of relevant knowledge that could be transferred to environmental managers. We have implemented this conceptual framework in a tool called ModeleR. This is being used to document, share, and execute more than 200 models and analytical processes associated with a global change monitoring program that is being undertaken in the Sierra Nevada Mountains (south Spain). ModeleR uses the concept of scientific workflow to connect and execute different types of models and analytical processes. Finally, we have envisioned the creation of a federation of model repositories where models documented within a local repository could be linked and even executed by other researchers.

http://dx.doi.org/10.1016/j.envsoft.2013.10.027192 - 19952
Copyright, Open Data, and the Availability-Usability GapBook Chapter2014M. LevineResearch Data Management: Practical Strategies for Information Professionals129
Assimilating Digital Repositories Into the Active Research ProcessBook Chapter2014T. WaltersResearch Data Management: Practical Strategies for Information Professionals189–201
Where Technology and Policy CollideBook Chapter2014M. SmithResearch Data Management: Practical Strategies for Information Professionals45
A centralized tool for managing, archiving, and serving point-in-time data in ecological research laboratoriesJournal Article2014S.J.K. Mason; S.B. Cleveland; P. Llovet; C. Izurieta; G.C. PooleEnvironmental Modelling & Software

Abstract The recent proliferation of software tools that aid researchers in various phases of data tracking and analysis undoubtedly contribute to successful development of increasingly complex and data-intensive scientific investigations. However, the lack of fully integrated solutions to data acquisition and storage, quality assurance/control, visualization, and provenance tracking of heterogeneous temporal data streams collected at numerous geospatial locations continues to occupy a general problem area for scientists and data managers working in the environmental sciences. We present a new Service Oriented Architecture (SOA) that allows users to: 1) automate the process of pushing real-time data streams from networks of environmental sensors or other data sources to an electronic data archive; 2) to perform basic data management and quality control tasks; and 3) to publish any subset of the data to existing cyberinfrastructure platforms for global discovery and distribution via the World Wide Web. The approach outlined here supports management of: 1) repeated field observations, 2) data from laboratory analysis of field samples, 3) simulation results, and 4) derived values. We describe how the use of Hypertext Transfer Protocol (HTTP) Application Programming Interfaces (APIs) Representational State Transfer (REST) methods for data model objects and Resource Query Language (RQL) interfaces respond to a basic problem area in environmental modelling by enabling researchers to integrate an electronic data repository with existing workflows, simulation models, or third-party software.

http://dx.doi.org/10.1016/j.envsoft.2013.09.00859 - 6951
CILogon: A federated X.509 certification authority for cyberinfrastructure logonJournal Article2014J. Basney; T. Fleury; J. GaynorConcurrency and Computation: Practice and Experience10.1002/cpe.32652225–223926
Editorial: Coauthors gone bad; how to avoid publishing conflict and a proposed agreement for co-author teamsJournal Article2014R.B. Primack; J.A. Cigliano; E.C.M. ParsonsBiological Conservationhttp://dx.doi.org/10.1016/j.biocon.2014.06.003277 - 280176
Organizing phenological data resources to inform natural resource conservationJournal Article2014A.H. Rosemartin; T.M. Crimmins; C.A.F. Enquist; K.L. Gerst; J.L. Kellermann; E.E. Posthumus; E.G. Denny; P. Guertin; L. Marsh; J.F. Weltzin90 - 97173
ESA and Scientific Publishing—Past, Present, and Pathways to the FutureJournal Article2013S. Collins; D. Goldberg; J. Schimel; K. McCarterBulletin of the Ecological Society of America10.1890/0012-9623-94.1.414 - 1194
Workshop 1: Conference on Public Participation in Scientific Research 2012: An International, Interdisciplinary ConferenceJournal Article2013A. Miller-Rushing; S. BenzBulletin of the Ecological Society of America10.1890/0012-9623-94.1.1121112 - 11794
Chemical datuments as scientific enablersJournal Article2013H.S. RzepaJournal of Cheminformatics10.1186/1758-2946-5-6165
Data on WingsJournal Article2013H. RosnerScientific American10.1038/scientificamerican0213-68268 - 73308
Public Participation in Research Back in Vogue with Ascent of "Citizen Science"Magazine Article2013H. RosnerScientific AmericanFebruary
Verification of Semantic Web Service Annotations Using Ontology-Based PartitioningJournal Article2013K. Belhajjame; S. Embury; N. PatonIEEE Transactions on Services Computing10.1109/TSC.2013.41 - 1
How to Manage Data to Enhance Their Potential for Synthesis, Preservation, Sharing, and Reuse—A Great Lakes Case StudyJournal Article2013T.L. Kolb; A. Blukacz-Richards; A.M. Muir; R.M. Claramunt; M.A. Koops; W.W. Taylor; T.M. Sutton; M.T. Arts; E. BisselFisheries10.1080/03632415.2013.757975252 - 6438
Realities of Data Sharing Using the Genome Wars as Case Study - Historical Perspective and CommentaryJournal Article2013B.R. JasnyEPJ Data Science10.1140/epjds132
Publishing frontiers: The library rebootJournal Article2013R. MonasterskyNature10.1038/495430a7442495
The EcoData Retriever: Improving Access to Existing Ecological DataJournal Article2013B.D. Morris; E.P. WhitePLoS ONE

Ecological research relies increasingly on the use of previously collected data. Use of existing datasets allows questions to be addressed more quickly, more generally, and at larger scales than would otherwise be possible. As a result of large-scale data collection efforts, and an increasing emphasis on data publication by journals and funding agencies, a large and ever-increasing amount of ecological data is now publicly available via the internet. Most ecological datasets do not adhere to any agreed-upon standards in format, data structure or method of access. Some may be broken up across multiple files, stored in compressed archives, and violate basic principles of data structure. As a result acquiring and utilizing available datasets can be a time consuming and error prone process. The EcoData Retriever is an extensible software framework which automates the tasks of discovering, downloading, and reformatting ecological data files for storage in a local data file or relational database. The automation of these tasks saves significant time for researchers and substantially reduces the likelihood of errors resulting from manual data manipulation and unfamiliarity with the complexities of individual datasets.

10.1371/journal.pone.006584868
Big Data in Life Cycle AssessmentJournal Article2013J. Cooper; M. Noon; C. Jones; E. Kahn; P. Arbuckle10.1111/jiec.12069796–79917
Why, How, and Where We’re Going Next: A Multi-Institution Look at Data Management ServiceJournal Article2013K. DeardsResearch Data Management43
A Study on the Organizational Architecture and Standard System of the Data Sharing Network of Earth System Science in ChinaJournal Article2013J. Wang; J. Sun; Y. Zhu; Y. YangData Science Journal10.2481/dsj.13-03191-10112
CRIS - Computational research infrastructure for scienceConference Paper2013E.C. Dragut; P. Baker; J. Xu; M.I. Sarfraz; E. Bertino; A. Madhkour; R. Agarwal; A. Mahmood; S. HanInformation Reuse and Integration (IRI), 2013 IEEE 14th International Conference on

The challenges facing the scientific community are common and real: conduct relevant and verifiable research in a rapidly changing collaborative landscape with an ever increasing scale of data. It has come to a point where research activities cannot scale at the rate required without improved cyberinfrastructure (CI). In this paper we describe CRIS (The Computational Research Infrastructure for Science), with its primary tenets to provide an easy to use, scalable, and collaborative scientific data management and workflow cyberinfrastructure for scientists lacking extensive computational expertise. Some of the key features of CRIS are: 1) semantic definition of scientific data using domain vocabularies; 2) embedded provenance for all levels of research activity (data, workflows, tools etc.); 3) easy integration of existing heterogeneous data and computational tools on local or remote computers; 4) automatic data quality monitoring for syntactic and domain standards; and 5) shareable yet secure access to research data, computational tools and equipment. CRIS currently has a community of users in Agronomy, Biochemistry, Bioinformatics and Healthcare Engineering at Purdue University (cris.cyber.purdue.edu).

10.1109/IRI.2013.6642486
Toward a New Understanding of Virtual Research CollaborationsJournal Article2013A.U. AydinogluSAGE Open

Virtual research collaborations (VRCs) have become an important method of conducting scientific activity; however, they are often regarded and treated as traditional scientific collaborations. Their success is measured by scholarly productivity and adherence to budget by funding agencies, participating scientists, and scholars. VRCs operate in complex environments interacting with other complex systems. A holistic (or organicist) approach is needed to make sense of this complexity. For that purpose, this study proposes using a new perspective, namely, the complex adaptive systems theory that can provide a better understanding of a VRC’s potential creativity, adaptability, resilience, and probable success. The key concepts of complex systems (diversity, interaction, interdependency, feedback, emergence, and adaptation) utilized in organization studies are used to discuss the behaviors of VRCs, illustrated with real-life examples.

10.1177/21582440135072693
Gathering feedback from early-career faculty: Speaking with and surveying agricultural faculty members about research dataJournal Article2013S.C. WilliamsJournal of eScience Librarianship10.7191/jeslib.2013.104842
Making open data work for plant scientistsJournal Article2013S. Leonelli; N. Smirnoff; J. Moore; C. Cook; R. BastowJournal of Experimental Botany

Despite the clear demand for open data sharing, its implementation within plant science is still limited. This is, at least in part, because open data-sharing raises several unanswered questions and challenges to current research practices. In this commentary, some of the challenges encountered by plant researchers at the bench when generating, interpreting, and attempting to disseminate their data have been highlighted. The difficulties involved in sharing sequencing, transcriptomics, proteomics, and metabolomics data are reviewed. The benefits and drawbacks of three data-sharing venues currently available to plant scientists are identified and assessed: (i) journal publication; (ii) university repositories; and (iii) community and project-specific databases. It is concluded that community and project-specific databases are the most useful to researchers interested in effective data sharing, since these databases are explicitly created to meet the researchers’ needs, support extensive curation, and embody a heightened awareness of what it takes to make data reuseable by others. Such bottom-up and community-driven approaches need to be valued by the research community, supported by publishers, and provided with long-term sustainable support by funding bodies and government. At the same time, these databases need to be linked to generic databases where possible, in order to be discoverable to the majority of researchers and thus promote effective and efficient data sharing. As we look forward to a future that embraces open access to data and publications, it is essential that data policies, data curation, data integration, data infrastructure, and data funding are linked together so as to foster data access and research productivity.

10.1093/jxb/ert2734109-411764
Technological and Organisational Aspects of Global Research Data Infrastructures Towards Year 2020Journal Article2013F. Karagiannis; D. Keramida; Y. Ioannidis; E. Laure; D. Vitlacil; F. ShortData Science Journal10.2481/dsj.GRDI-001GRDI1-GRDI512
The DataBridgeJournal Article2013A. Rajasekar; H.C. Kum; M. Crosas; J. Crabtree; S. Sankaran; H. Lander; T. Carsey; G. King; J. ZhanSCiENCEpp–12
HydroServer Lite as an open source solution for archiving and sharing environmental data for independent university labsJournal Article2013L.G. Conner; D.P. Ames; R.A. GillEcological Informatics

Abstract Managing, archiving, and sharing large amounts of data are essential tasks in ecological laboratories, and detailed data management plans are now required by major funding agencies. Many independent research labs may lack the technical or financial resources needed to support some of the more comprehensive data management solutions that have become available. In this paper we describe an open-source solution to data management, archiving, and sharing that can be implemented and customized by someone with limited computer programming experience using free software and standardized web services. This software, HydroServer Lite, is a light-weight database and data management web-based application that integrates with and makes data available on a large data sharing network developed by the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI). The \{CUAHSI\} Hydrologic Information System facilitates data sharing through a network of local HydroServers that are registered with the central registry. Each HydroServer may contain a variety of ecological and climate data, stored in a standardized relational database model. Someone searching for data that are registered in the central registry can query the network by source, location, variable type, and dates. These data can be downloaded from the local HydroServer to a computer in an office or lab where they can be manipulated and analyzed without compromising the data in the archives. We offer this HydroServer Lite case study as a possible solution for independent research laboratories looking for a data management system that requires little technical expertise or initial cost to set up.

http://dx.doi.org/10.1016/j.ecoinf.2013.08.006171 - 17718
Virtual Research Environments: An Overview and a Research AgendaJournal Article2013L. Candela; D. Castelli; P. PaganoData Science Journal10.2481/dsj.GRDI-013GRDI75-GRDI8112
Data-sharing: everything on displayJournal Article2013R. Van NoordenNature10.1038/nj7461-243a243–245500
Oregon State University Libraries and Press Strategic Agenda for Research Data ServicesJournal Article2013S. Sutton; D. Barber; A.L. Whitmire
The Changing Roles of Repositories: Where We Are and Where We Are HeadedJournal Article2013K. Bjork; D. Isaak; K. Vyhnanek
Citizen science: amateur expertsJournal Article2013T. GuraNature10.1038/nj7444-259a259–261496
MetaShare: Constructing Actionable Data Management Plans through Formal SemanticsConference Paper2013L. Salayandia; A.Q. Gates; D. PenningtonResearch Data Management Implementations Workshop
How information science professionals add value in a scientific research centerJournal Article2013C. Eaker; A.K. Thomer; E. Johns; K. Siddell
Preserving privacy in shared provenance dataJournal Article2013P. Missier; J. Bryans; R. Danger; V. Curcin
SWETS North America Scholarship EssayJournal Article2013H.M. DavisAgainst the Grain10.7771/2380-176X.6421125
Earth Science Infrastructures Interoperability: The Brokering ApproachJournal Article2013S. Nativi; M. Craglia; J. PearlmanIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2013.22431131118-11296
A comparative evaluation of technical solutions for long-term data repositories in integrative biodiversity researchJournal Article2012K. Bach; D. Schäfer; N. Enke; B. Seeger; B. Gemeinholzer; J. BendixEcological Informatics

The current study investigates existing infrastructure, its technical solutions and implemented standards for data repositories related to integrative biodiversity research. The storage and reuse of complex biodiversity data in central databases are becoming increasingly important, particularly in attempts to cope with the impacts of environmental change on biodiversity and ecosystems. From the data side, the main challenge of biodiversity repositories is to deal with the highly interdisciplinary and heterogeneous character of standardized and unstandardized data and metadata covering information from genes to ecosystems. Furthermore, the technical improvements in data acquisition techniques produces ever larger data volumes, which represents a challenge for database structure and proper data exchange.

The current study is based on comprehensive in-depth interviews and an online survey addressing IT specialists involved in database development and operation. The results show that metadata are already well established, but that non-meta data still is largely unstandardized across various scientific communities. For example, only a third of all repositories in our investigation use internationally unified semantic standard checklists for taxonomy. The study also showed that database developers are mostly occupied with the implementation of state of the art technology and solving operational problems, leaving no time to implement user's requirements. One of the main reasons for this dissatisfying situation is the undersized and unreliable funding situation of most repositories, as reflected by the marginally small number of permanent IT staff members. We conclude that a sustainable data management system that fosters the future use and reuse of these valuable data resources requires the development of fewer, but more permanent data repositories using commonly accepted standards for their long-term data. This can only be accomplished through the consolidation of hitherto widely scattered small and non-permanent repositories.

doi:10.1016/j.ecoinf.2011.11.008
Value of long-term ecological studiesJournal Article2012D.B. Lindenmayer; G.E. Likens; A. Andersen; D. Bowman; M.C. Bull; E. Burns; C.R. Dickman; A.A. Hoffmann; D.A. Keith; M.J. Liddell; A.J. Lowe; D.J. Metcalfe; S.R. Phinn; J. Russell-Smith; N. Thurgate; G.M. WardleAustral Ecology

Long-term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence-based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long-term ecological studies to resource managers, policy makers and the general public. Long-term research will be especially important for tackling large-scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long-term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long-term ecological studies do not become a long-term deficiency. It is important to maintain publishing outlets for empirical field-based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long-term data sets. Funding schemes must be re-crafted to emphasize collaborative partnerships between field-based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.

10.1111/j.1442-9993.2011.02351.x
Advances in global change research require open science by individual researchersJournal Article2012E.M. Wolkovich; J. Regetz; M.I. O'ConnorGlobal Change Biology10.1111/j.1365-2486.2012.02693.xn/a - n/a
Beyond The Low Hanging Fruit: Data Services and Archiving at the University of New MexicoJournal Article2012R. Ollendorf; S. KochJournal of Digital InformationNo 1 (2012)13
Cyberinfrastructure for isotope analysis and modelingJournal Article2012G.J. Bowen; J.B. West; L. Zhao; G. Takahashi; C. Miller; T. Zhang

As the quantity and complexity of scientific data expand, accessible interfaces for data manipulation and analysis are needed to support broad and efficient data use. The Isoscapes Modeling, Analysis, and Prediction (IsoMAP; http://isomap.org) Web-based geographical information system (GIS) gateway is an example of such a resource. Recently launched with support from the U.S. National Science Foundation (NSF) Division of Biological Infrastructure, IsoMAP enables analysis and integration of diverse light stable isotope and environmental data by a broad-based user community. It provides an intuitive, spatial interface that streamlines data analysis, modeling, and exploration in research ranging from greenhouse gas biogeochemistry to food science.

1993
What's on the horizon for macroecology?Journal Article2012J. Beck; L. Ballesteros-Mejia; C.M. Buchmann; ürgen Dengler; S.A. Fritz; B. Gruber; C. Hof; F. Jansen; S. Knapp; H. Kreft; A.K. Schneider; M. Winter; C.F. DormannEcography10.1111/ecog.2012.35.issue-810.1111/j.1600-0587.2012.07364.x8673 - 68335
Enhancing integrated environmental modelling by designing resource-oriented interfacesJournal Article2012C. Granell; L. íaz; S. Schade; N. änder; ín HuertaEnvironmental Modelling & Software10.1016/j.envsoft.2012.04.013
Appreciating and Archiving Present-Day Naturalists’ Contributions to ScienceJournal Article2012T. Crimmins; M. CrimminsBioScience10.1525/bio.2012.62.6.36531 - 53262
The user's view on biodiversity data sharing — Investigating facts of acceptance and requirements to realize a sustainable use of research data —Journal Article2012N. Enke; A. Thessen; K. Bach; örg Bendix; B. Seeger; B. GemeinholzerEcological Informatics10.1016/j.ecoinf.2012.03.00425 - 3311
Long-Term Ecological Research in a Human-Dominated WorldJournal Article2012P.G. Robertson; S. Collins; D. Foster; N. Brokaw; H. Ducklow; T. Gragson; C. Gries; S.M. HamiltonBioScience10.1525/bio.2012.62.4.64342 - 35362
A Collaborative Framework for Data Management Services: The Experience of the University of CaliforniaJournal Article2012J. Starr; P. Willett; L. Federer; C. Horning; M. BergstromJournal of eScience Librarianship10.7191/jeslib.2012.10142109 - 1141
DataONE Opens Doors to Scientists across DisciplinesJournal Article2012J.P. CohnBioScience10.1525/bio.2012.62.11.16111004 - 100462
DMP Online and DMPTool: Different Strategies Towards a Shared GoalJournal Article2012A. Sallans; M. DonnellyInternational Journal of Digital Curation10.2218/ijdc.v7i2.23527
Hänsel und Gretel: The future of publishing wicked witch-freeJournal Article2012C.J. LortieIdeas in Ecology and Evolution10.4033/iee.2012.5b.19.f5
Advanced Technologies and Data Management Practices in Environmental Science: Lessons from AcademiaJournal Article2012R.R. Hernandez; M.S. Mayernik; M.L. Murphy-Mariscal; M.F. AllenBioScience

Environmental scientists are increasing their capitalization on advancements in technology, computation, and data management. However, the extent of that capitalization is unknown. We analyzed the survey responses of 434 graduate students to evaluate the understanding and use of such advances in the environmental sciences. Two-thirds of the students had not taken courses related to information science and the analysis of complex data. Seventy-four percent of the students reported no skill in programming languages or computational applications. Of the students who had completed research projects, 26% had created metadata for research data sets, and 29% had archived their data so that it was available online. One-third of these students used an environmental sensor. The results differed according to the students’ research status, degree type, and university type. Changes may be necessary in the curricula of university programs that seek to prepare environmental scientists for this technologically advanced and data-intensive age.

10.1525/bio.2012.62.12.81262
Efficient rescue of threatened biodiversity data using reBiND workflowsJournal Article2012A. Güntsch; D. Fichtmüller; A. Kirchhoff; W.G. BerendsohnPlant Biosystems - An International Journal Dealing with all Aspects of Plant Biology

Abstract Biodiversity data generated in the context of research projects often lack a strategy for long-term preservation and availability, and are therefore at risk of becoming outdated and finally lost. The reBiND project aims to develop an efficient and well-documented workflow for rescuing such data sets. The workflow consists of phases for data transformation into contemporary standards, data validation, storage in a native XML database, and data publishing in international biodiversity networks. It has been developed and tested using the example of collection and observational data but is flexible enough to be transferred to other data types and domains.

10.1080/11263504.2012.740086752-755146
Environmental Researchers’ Data Practices: An Exploratory Study in TurkeyBook Chapter2012S. Allard; A.U. AydınoğluE-Science and Information Management10.1007/978-3-642-33299-9_513-24317
Data Curation as a Form of Collaborative ResearchJournal Article2012S. Brandt
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