|Title||Body||Technical Expertise Required||Cost||Additional Information|
|Boyle’s Laws in a Networked World: How the future of science lies in understanding our past|
|Consider the compatibility of the data you are integrating|
The integration of multiple data sets from different sources requires that they be compatible. Methods used to create the data should be considered early in the process, to avoid problems later during attempts to integrate data sets. Note that just because data can be integrated does not necessarily mean that they should be, or that the final product can meet the needs of the study. Where possible, clearly state situations or conditions where it is and is not appropriate to use your data, and provide information (such as software used and good metadata) to make integration easier.
|Document steps used in data processing|
Different types of new data may be created in the course of a project, for instance visualizations, plots, statistical outputs, a new dataset created by integrating multiple datasets, etc. Whenever possible, document your workflow (the process used to clean, analyze and visualize data) noting what data products are created at each step. Depending on the nature of the project, this might be as a computer script, or it may be notes in a text file documenting the process you used (i.e. process metadata). If workflows are preserved along with data products, they can be executed and enable the data product to be reproduced.
|Provenance and DataONE: Facilitating Reproducible Science|
|The data flood: Implications for data stewardship and the culture of discovery|
|The Open Science Framework: Increasing Reproducibility Across the Entire Research Lifecycle|
|Understand the geospatial parameters of multiple data sources|
Understand the input geospatial data parameters, including scale, map projection, geographic datum, and resolution, when integrating data from multiple sources. Care should be taken to ensure that the geospatial parameters of the source datasets can be legitimately combined. If working with raster data, consider the data type of the raster cell values as well as if the raster data represent discrete or continuous values. If working with vector data, consider feature representation (e.g., points, polygons, lines). It may be necessary to re-project your source data into one common projection appropriate to your intended analysis. Data product quality degradation or loss of data product utility can result when combining geospatial data that contain incompatible geospatial parameters. Spatial analysis of a dataset created from combining data having considerably different scales or map projections may result in erroneous results.
Document the geospatial parameters of any output dataset derived from combining multiple data products. Include this information in the final data product's metadata as part of the product's provenance or origin.