Foresight model inventory Version 1 Athanasios Petsakos1 and Gideon Kruseman2 1 Alliance Bioversity-CIAT 2 International Maize and Wheat Improvement Center (CIMMYT) December 2022 Foresight and metrics to accelerate food, land, and water system transformation initiative report The One CGIAR initiative on Foresight and metrics has an overall vision that national, regional, and global partners gain enhanced skills and access to state-of-the art foresight tools, data, and metrics, and contribute to and use foresight analysis to inform their decisions about policies and investments to transform food, land, and water systems in ways that improve nutrition, livelihoods, equity, climate adaptation and mitigation, and environmental outcomes. The goals and objectives are to provide the evidence and capacity needed to inform the complex choices that will shape the future of food, land and water systems, this initiative will, in close interaction with decision-makers at multiple levels: 1. Develop a common information base on major medium- and long-term future challenges and strategic opportunities at global and regional scales 2. Work with national partners on foresight analyses to inform policy and investment decisions, with special attention to climate variability, risk and resilience 3. Enhance access to and transparency of foresight tools and systems-relevant metrics 4. Enhance partners’ foresight capacity through collaborative research and structured training programs This reports series aims to share the insights and knowledge generated during the course of implementing the initiative. The reports have gone through a light review process at the initiative team leadership level and have been endorsed by the relevant work package leader. Acknowledgements This study was financially supported by the One CGIAR initiative on Foresight and Metrics for the Transformation of Food, Land and Water Systems (FMI). CGIAR is a global research partnership for a food-secure future, dedicated to transforming food, land, and water systems in a climate crisis. We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund: https://www.cgiar.org/funders/ . ii Preface Decision-makers in many regions and countries already use foresight tools and analysis to anticipate trends and evaluate policy trade-offs. However, development goals today span multiple Impact Areas with more possible trade-offs, and food, land, and water systems are also more integrated, with shared challenges and risks. As a result, the foresight tools and metrics needed to track trends and evaluate priorities have also become more complex. Many governments, researchers and development organizations face technical and institutional barriers to building and maintaining their foresight capabilities. Models and databases can be costly to develop, extend and maintain, and many government ministries and research institutions lack the mandate and/or expertise to develop their own tools and metrics that jointly cover food, land, and water systems. This leaves some countries, especially poorer countries, with inadequate tools and metrics to inform policy and investment decisions (World Bank, 2021). This means that those countries are not sufficiently able to evaluate the complex food, land, and water systems challenges and trade-offs they face; that the policy and investment choices they make may fall short of their potential benefits in terms of nutrition, poverty reduction, inclusion, climate adaptation and mitigation, and environmental impacts; and that their interests are not adequately captured in broader regional and global dialogues and decision-making processes. WP3 addresses these gaps by improving access to advanced tools, data, and metrics through greater harmonization, standardization, documentation, transparency, accessibility, and interoperability, as well as compliance with FAIR principles. Specifically, WP3 will (1) standardize and document CGIAR’s core foresight models that capture food, land and water systems in and beyond the focus countries, including IMPACT (global and national market models) and RIAPA (country economywide models) and their underlying biophysical and household models; (2) maintain the databases needed to update and operate the core foresight and biophysical models; (3) develop and document new and improved metrics covering key areas of FLW systems, including outcome metrics and associated models needed to evaluate policy and investment impacts, such as agrifood system GDP, employment, water, and greenhouse gas emissions; and (4) maintain and promote CGIAR’s Foresight Portal (website) that disseminates the above-mentioned tools, data, and metrics, and provides searchable databases containing the scenarios and results from CGIAR-led global, regional and national foresight studies (see WP1 and WP2). Besides providing access to material, the work package also strives to ensure that stakeholders, and partners can actively participate in the generation of models, tools, data and metrics that are made available through the foresight web portal. The outputs are geared towards the end of initiative outcomes in order to ensure that the innovations generated by the initiative contribute to desired impacts. This report is part of the documentation component of Work package 3 of the foresight and metrics initiative supporting the Documentation and improved access to standardized, regularly updated, and interoperable versions of CGIAR’s core foresight models, tools, and databases spanning food, land and water systems. December 2022 Gideon Kruseman (WP3 leader) iii Table of Contents Introduction .......................................................................................................................................1 List of models .....................................................................................................................................3 Description of the metadata schema ...................................................................................................6 Next steps ..........................................................................................................................................7 References .........................................................................................................................................8 Annex 1: full model and tool metadata description...............................................................................9 iv Introduction In WP3 of the One CGIAR initiative on foresight and metrics for the transformation of food, land, and water systems, we have a cluster of activities around documentation: Documentation and improved access to standardized, regularly updated, and interoperable versions of CGIAR’s core foresight models, tools, and databases spanning food, land and water systems . This constitutes output 3.1 of the initiative. As food, land and water systems become more interconnected and the tools and metrics needed to analyze them become more complex, it is difficult and costly for governments and researchers, especially in developing countries, to remain at the forefront of foresight analysis. WP3 reduces these barriers by making it easier to access and use CGIAR’s core foresight models, databases, and systems - level metrics. Figure 1: Theory of change related to the work package on: Enhancing access, transparency and use of tools, data, and metrics (Wiebe and Gotor, 2021). Making models and tools and their related data and metrics as well as the documentation describing the models and tools findable, accessible, interoperable and re-useable is a key element of the open research data assets policy of One CGIAR (CGIAR System Management Office, 2021). These FAIR principles (Wilkinson et al., 2016) do not only apply to data but also to models and tools. Tagging assets with rich metadata requires approaches that will allow models and tools and their related data to be or become interoperable. The tags for the metadata are constructed in such a way that they can be converted easily into an OIMS metadata file (Kruseman, 2022). The flexible and extensible OIMS metadata approach allows for the tagging of both models and tools, data and metrics, and their related documentation. As part of this effort, the team is compiling a list of key foresight models and tools. This work will continue over time and the current report highlights the initial inventory. This report is version 1 of the model 1 inventory. It is intended as a living document. In section 2 we present the initial list of models and tools. This list captures some of the key models used by the initiative team. In section 3 we provide the general description of the key metadata that is being attached to each of the models in the inventory. Having good metadata attached to the models will go a long way to enhancing use and reuse of models and their data. In section 4 we discuss the next steps that will lead to the second version of this inventory. 2 List of models We present the results of an initial attempt to identify models which are currently used, or which can be potentially used in foresight analysis. Some of the models identified have either been used as standalone tools or they belong to a broader integrated foresight modeling framework (e.g., the IMPACT suite of models). Some models included herein have not been used in foresight analysis before, but the y are listed nonetheless because of their capacity to provide niche inputs to other existing foresight models. The list includes models developed (i) by the CGIAR, (ii) in collaboration with the CGIAR, and (iii) outside the CGIAR. It is presented in table format, and it is sectioned by thematic field (e.g., economic models, crop models, etc.). Comments and useful information are provided for each model. Table 1 Initial list of key foresight models MODEL COMMENTS Global agricultural economic partial equilibrium model developed IMPACT by IFPRI. It is part of the F&M initiative model toolbox and used in WP1. National-level computable general equilibrium (CGE) model RIAPA developed by IFPRI. It is part of the F&M initiative model toolbox and used in WP2. Economic Global CGE model, developed by CGIAR partners. Used in GLOBE combination with IMPACT for general equilibrium analysis in WP1 of the F&M initiative. ADAM is an agrifood system data analysis modeling framework to ADAM generate new metrics from existing data sources by creating interoperable derived data sets Generic platform for crop growth simulation models. Many of the individual models in DSSAT have been developed in collaboration DSSAT with CGIAR scientists. It is part of the F&M initiative model toolbox and used in WP1 and WP2. Developed by FAO. Occasionally used by CGIAR but it does not AQUACROP currently belong to the F&M initiative model toolbox. Crop simulation Developed by CGIAR partners. Occasionally used by CGIAR but it APSIM does not currently belong to the F&M initiative model toolbox. Developed by CGIAR partners. Occasionally used by CGIAR but it WOFOST does not currently belong to the F&M initiative model toolbox. Rice crop growth model developed by IRRI. It does not currently ORYZA belong to the F&M initiative model toolbox. Livestock model developed by ILRI and linked to IMPACT. It is part Livestock-IMPACT Other of the F&M initiative model toolbox biophysical Fish catchment model developed by WorldFish and linked to Fish-IMPACT IMPACT. It is part of the F&M initiative model toolbox Spatial Allocation Model, developed by IFPRI and provides inputs SPAM to almost all CGIAR foresight models. It is part of the F&M initiative model toolbox. Land use Developed by FAO as a tool to determine the suitability of a crop ECOCROP for a specified environment. Occasionally used by CGIAR but it does not currently belong to the F&M initiative model toolbox. 3 Table 1 (continued) MODEL COMMENTS Hydrological model developed by IWMI and linked to IMPACT. It is Water-IMPACT part of the F&M initiative model toolbox Biodiversity model/database, developed outside the CGIAR. It does not currently belong to the F&M initiative model toolbox. Environmental / PREDICTS Potential use in foresight modeling for biodiversity with IMPACT other and/or RIAPA. Integrated modelling approach used in WP3 of the ClimBeR initiative. It does not currently belong to the F&M initiative model iFEED toolbox. Potential use in foresight modeling for linking with IMPACT and/or RIAPA. Ecosystem Services model developed outside the CGIAR. Has been used in the past by CGIAR scientists. It does not currently belong MESH to the F&M initiative model toolbox. Potential use in foresight modeling of supply of ecosystem services. Environmental impact assessment model that can generate livestock-driven land use change at landscape to national level CLEANED-R Developed outside the CGIAR. It has been used in the past by CGIAR scientists. Land use model developed outside the CGIAR but has appeared in LandSHIFT several applications. It has been used in the past by CGIAR scientists, and las been linked to IMPACT. General disease model included as a DSSAT module. Developed DSSAT/GDM outside the CGIAR but frequently used by CGIAR scientists. Envisioned to become part of the F&M initiative model toolbox. Model for invasive insect species, developed by CIP. It does not currently belong to the F&M initiative model toolbox. Potential use ILCYM in pest and disease modeling work under WP1 of the F&M initiative. Generic disease risk assessment (spread) model for rice, developed Pests and by IRRI. It does not currently belong to the F&M initiative model EPIRICE toolbox. Potential use in pest and disease modeling work under pathogens WP1 of the F&M initiative. Generic disease risk assessment (spread) model for wheat, developed outside the CGIAR. It does not currently belong to the EPIWHEAT F&M initiative model toolbox. Potential use in pest and disease modeling work under WP1 of the F&M initiative. Generic model to assess rice yield losses to pests. Developed by IRRI. It does not currently belong to the F&M initiative model RICEPEST toolbox. Potential use in pest and disease modeling work under WP1 of the F&M initiative. 4 Table 1 (continued) MODEL COMMENTS Generic model to assess wheat yield losses to pests. Developed outside the CGIAR. It does not currently belong to the F&M WHEATPEST initiative model toolbox. Potential use in pest and disease modeling work under WP1 of the F&M initiative. Generic risk assessment (spread) model for potato late blight, developed in collaboration with CGIAR scientists. It does not BLIGHTSIM currently belong to the F&M initiative model toolbox. Potential use in pest and disease modeling work under WP1 of the F&M initiative. 5 Description of the metadata schema Metadata falls into a number of distinct categories. There is the descriptive metadata that allows an asset to be found in a meaningful way. The CG-Core metadata schema (Devare, 2017; CGIAR, 2019)currently applied to both data assets and publications in open access is an example of descriptive metadata. OIMS (Kruseman, 2022) supports the CG-core metadata schema. Besides descriptive metadata there is the technical metadata that allows accessibility to the assets and the structural metadata that provides in depth information that is needed to ensure true interoperability. The OIMS metadata schema allows the tagging of all kinds of metadata in a flexible and extensible way and is therefore especially appropriate for evolving digital eco-systems such as the foresight portal under development by the One CGIAR initiative on Foresight and metrics for the transformation of food, land, and water systems. The metadata attributes with which the model database will be constructed can be broadly distinguished into the following categories: 1. General metadata information: Contains administrative information about the version of the metadata, including version number, changes from previous versions (e.g., attributes added or removed, etc.), as well as the individual contributors in designing the metadata scheme. 2. General model information: This short section provides high level information on the model, including its name and version, in addition to a description of changes between the current and the last version. Finally, it provides a link to the developers’ release notes. 3. Main model attributes: This is the largest section of the metadata schema. It contains general information about the described model, including its scientific domain, type, purpose, geographical and temporal scope and resolution, and other information about model administration (developer and current maintainer). Information about model software is also included 4. Model input and output data: General information about key parameters that are needed to run the model, and parameters that are generated as model outputs. Links to model database metadata is provided. 5. Model contributors: List of individuals who currently contribute to model development and maintenance, including affiliations and contact information. 6. Documentation: Key documentation about the model, including its user manual and links to model website where this documentation can be found. In the appendix (Annex 1) the complete metadata schema can be found. A template has been developed in an MS-EXCEL sheet to capture this metadata for each model or tool. 6 Next steps The metadata schema has been finalized in 2022 (see Annex 1 for the complete schema). For 2023 we plan to: 1. Send the excel file to different modeling teams 2. Transform the metadata entries into .json files and integrate with the foresight portal As the metadata is collected for the key models and tools, we will gather feedback from the users to identify if and where improvements are necessary. 7 References CGIAR (2019). CG Core Metadata Schema version 2. Available at: https://agriculturalsemantics.github.io/cg-core/cgcore.html [Accessed November 28, 2022]. CGIAR System Management Office (2021). CGIAR Open and FAIR Data Assets Policy. Montpellier, France Available at: https://hdl.handle.net/10568/113623. Devare, M. (2017). CG Core Metadata Schema and Application Profile - Beta Version 1.0. Montpellier, France doi:20.500.11766/4764. Kruseman, G. (2022). A Flexible, Extensible, Machine-Readable, Human-Intelligible, and Ontology- Agnostic Metadata Schema (OIMS). Front. Sustain. Food Syst. 6. doi:10.3389/fsufs.2022.767863. Wiebe, K., and Gotor, E. (2021). Initiative proposal for Foresight and Metrics to Accelerate Food, Land, and Water Systems Transformation. 76. Available at: https://www.cgiar.org/initiative/24-foresight- and-metrics-to-accelerate-inclusive-and-sustainable-agrifood-system-transformation/. Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018. Available at: http://dx.doi.org/10.1038/sdata.2016.18. World Bank (2021). World Development Report 2021 Data for Better Lives. Washington D.C., USA. 8 Annex 1: full model and tool metadata description The OIMS-compatible metadata schema highlighted in Table A1.1 below uses the key attributes of OIMS (Kruseman, 2022). This means that each metadata field has a name and a description. Furthermore , it is characterized by having a certain datatype. Fields can be either primitive or complex. A primitive field just has a value. A complex field is composed of multiple attributes with values that together form the value of that attribute. An example is a contributor. A contributor has a first name, a last name , and an affiliation along with other characteristics. If we stick with the example of contributors, we observe that there are certain attributes that can have more than one value. There can be multiple contributors to a data sets, model, or document. Moreover, an attribute has a certain status in terms of whether it is required, recommended or optional. 9 Table A1.1 OIMS-compatible metadata schema # attribute description data type multiple compound Status used in the portal model search engine Composite attributes in bold 1 GeneralModelInfo 1 ModelName Name of the model text FALSE FALSE Required 2 ModelVersion Current model version text FALSE FALSE Required 3 PreviousModelVersion Previous model version text FALSE FALSE Required 4 CurrentVersionReleaseNotes Link to release notes of current version URL FALSE FALSE Recommended 2.1 Metadata Date of the initial entrée of the model 5 InitialDate Date FALSE FALSE Required into the inventory 6 MetadataVersion Version of the metadata Compound FALSE TRUE Required Version number #.##.### (# main version 6.1 MetadataVersionNumber number, ## minor update, ### text FALSE FALSE Required typographical error corrections) 6.2 MetadataVersionDate Date of the metadata version Date FALSE FALSE Required Changes relative to earlier metadata 6.3 ChangeLog Compound TRUE TRUE Required versions 6.3.1 PrevMetadataVersionNumber Previous metadata version number text FALSE FALSE Required 6.3.2 Changes Changes in the metadata Compound TRUE TRUE Required 6.3.2.2 Attribute Name of the attribute text FALSE FALSE Required Is this a new attribute added to the 6.3.2.3 NewAttribute Boolean FALSE FALSE Required metadata 6.3.2.4 PreviousValue Previous value of the metadata attribute FALSE FALSE Required 2.2 MetadataContributor Who contributed to generating this 7 MetadataContributor Compound TRUE TRUE Required version of the metadata 7.1 LastName Last name of the metadata contributor text FALSE FALSE Required 7.2 FirstName First name of the metadata contributor text FALSE FALSE Required 7.3 Affiliation Affiliation of metadata contributor text FALSE FALSE Required 7.4 Email Email of metadata contributor Email address FALSE FALSE Optional 7.5 ID Persistent identifier of contributor Compound TRUE TRUE Optional 10 7.5.1 IDType Type of persistent identifier e.g. ORCID Controlled vocabulary FALSE FALSE Required 7.5.2 IDValue identifier text FALSE FALSE Required 3 MainModelTags Scientific domains: e.g., economics, crop 8 ScientificDomain Controlled vocabulary TRUE FALSE Required science, hydrology Any other domain not covered by 9 OtherScientificDomain text FALSE FALSE Required "ScientificDomain" Eg., Simulation, equilibrium, 10 ModelType Controlled vocabulary TRUE FALSE Required optimization, etc. Any other model type not covered by 11 OtherModelType text FALSE FALSE Required "ModelType" Is the model focused on global level, 12 GeographicScope Controlled vocabulary TRUE FALSE Required regional, national level Is the model generic, applicable to any 13 GeographicScope (generic) Boolean FALSE FALSE Required relevant geography? (Y/N) Can the model be applied in CGIAR target 14 GeographicScope (CGIAR) Boolean FALSE FALSE Required geographies (Y/N) In case of spatial analysis, what is spatial 15 SpatialResolution resolution of analysis (e.g. value in km x text TRUE FALSE Required km)? Minimum level/unit of analysis: e.g., 16 UnitOfAnalysis field, farm, sub-country region, country, Controlled vocabulary FALSE FALSE Required world regions, other (FPU) Any other category not included in 17 OtherUnitOfAnalysis text FALSE FALSE Required "UnitOfAnalysis" Is the model static, dynamic, and what 18 TemporalScope text FALSE FALSE Required time horizon? Minimum assumed time unit for model 19 TemporalResolution calculations: e.g., hour, day, month, year, Controlled vocabulary FALSE FALSE Required other 20 ModelOpenness To what degree is the model "open"? Compound FALSE FALSE Required 20.1 CodeOpenness Is the model’s code open? Controlled vocabulary FALSE FALSE Required If the code is openly available, where can 20.2 CodeRepository URL FALSE FALSE Required one access it? 11 20.3 DataOpenness Is the model’s database open? Controlled vocabulary FALSE FALSE Required If the database is openly available, where 20.4 DataRepository URL FALSE FALSE Required can one access it? How/where was it developed? (e.g., by 21 ModelDevelopment the CGIAR, in collaboration with the Controlled vocabulary FALSE FALSE Recommended CGIAR, outside the CGIAR) Model ownership/management 22 ModelAdmin Compound FALSE FALSE Recommended information Who manages the model (e.g., CGIAR, 22.1 ModelAdminCGIAR Controlled vocabulary FALSE FALSE Recommended CGIAR partner, other) Entity/ies owning/managing the model 22.2 ModelAdminEntity (e.g., CGIAR center X, university Y, text FALSE TRUE Recommended research institute Z) Details about its use in foresight analysis 23 CGIARForesightUse by the CGIAR (e.g., already part of the Controlled vocabulary TRUE TRUE Required toolbox, potential use, etc.) Brief summary of model objective and (possibly) structure. Example from MIDAS (CAPRI): "A global agro-economic model used to assess impacts on 24 PurposeStatement text FALSE FALSE optional agriculture of agricultural, trade and environmental policies. CAPRI provides results at a regional level and for economic and environmental variables." Type(s) of analysis the model is best used 25 UseCase text FALSE TRUE Required for Required if 26 ModelURL URL of the model or tool URL FALSE FALSE applicable 4 InputData General description of main data 27 KeyInputs Composite TRUE TRUE Required sources and parameters used as inputs Broad input data categories (e.g., 27.1 DataType macroeconomic, climatic, physiological, Controlled vocabulary FALSE FALSE Required etc.) 12 Any other category not included in 27.2 OtherDataType text FALSE FALSE Required "DataType" 27.3 SpatialData Is the data spatially explicit? (Y/N) Boolean FALSE FALSE Required Coordinate Reference system used in 27.4 CRS text FALSE FALSE Required case of spatial data Link to data files with input metadata 27.5 MetaDataSource URL FALSE FALSE Recommended information If not metadata file available, insert main 27.6 MainInputData input data using semicolons (e.g.: prices; text FALSE TRUE Recommended yields; GDP; rainfall) If not metadata file available, insert main 27.7 MainDataSources data sources using semicolons (e.g.: text FALSE TRUE Recommended FAOSTAT; Worldbank) 5 OutputData Main output variables (e.g., yields, 28 KeyOutputs Composite TRUE TRUE Required prices etc.) Broad output data categories (e.g., 28.1 DataType macroeconomic, climatic, physiological, Controlled vocabulary FALSE FALSE Required etc.) Any other category not included in 28.2 OtherDataType text FALSE FALSE Required "DataType" 28.3 SpatialData Is the data spatially explicit? (Y/N) Boolean FALSE FALSE Required Coordinate Reference system used in 28.4 CRS text FALSE FALSE Required case of spatial data Link to data files with output metadata 28.5 MetaDataSource URL FALSE FALSE Recommended information If not metadata file available, insert main 28.6 MainOutputData output data using semicolons (e.g.: URL FALSE TRUE Recommended prices; yields; GDP; rainfall) 6 UnderlyingSoftware Underlying software needed to run the 29 UnderlyingSoftware Compound TRUE TRUE Recommended model, e.g., GAMS, R 29.1 UnderlyingSotwareName Name of the underlying software text FALSE FALSE Required 13 Type of underlying software (e.g., 29.2 UnderlyingSotwareLicense Controlled vocabulary FALSE FALSE Recommended proprietary, freeware) 29.3 UnderlyingSoftwareURL URL of the underlying software URL FALSE FALSE Recommended 7 ModelContributors Who contributed to the model 30 ModelContributor Compound TRUE TRUE recommended development Last name of the documentation 30.1 LastName text FALSE FALSE Required contributor First name of the documentation 30.2 FirstName text FALSE FALSE Required contributor 30.3 Affiliation Affiliation of documentation contributor text FALSE FALSE Required 30.4 Email Email of documentation contributor Email address FALSE FALSE Optional Persistent identifier of model 30.5 ID Compound TRUE TRUE Optional contributor 30.5.1 IDType Type of persistent identifier e.g., ORCID Controlled vocabulary FALSE FALSE Required 30.5.2 IDValue identifier text FALSE FALSE Required 8.1 Documentation Relevant key documentation and 31 RelevantDocumentation Compound TRUE TRUE Recommended publications Documentation type using the 31.1 DocumentationType documentation categorization of WP 3 Controlled vocabulary FALSE FALSE Required CoA 3.1 Title of the document, publication, 31.2 Title Text FALSE FALSE Required report ID# of documentation contributors (corresponds to the # column of tab "8.2 31.3 DocumentationContributorsID# DocumentationContributors"). List Text TRUE FALSE Required contributors using semicolons (e.g., 1;2;3) 31.4 PublicationDate Date of publication Compound FALSE TRUE Required 31.4.1 PublicationYear Year of publication integer FALSE FALSE Required 31.4.2 PublicationMonth Month of publication integer FALSE FALSE Optional 31.5 Publisher Name of the publisher text FALSE FALSE Required 31.6 PersistentIdentifier Persistent identifier of the publication Compound TRUE TRUE Required 14 Type of persistent identifier {DOI; 31.6.1 PersistentIdentifierType Controlled vocabulary FALSE FALSE Required Handle; Other} 31.6.2 PersistentIdentifierValue Value of the persistentIdentifier text FALSE FALSE Recommended 31.6.3 PersistentIdentifierURL URL of the document URL FALSE FALSE Recommended 31.7 License License associated with the document Compound TRUE TRUE Required Required if 31.7.1 LicenseType Type of license or user agreement ControlledVocabulary FALSE FALSE applicable Required if 31.7.2 LicenseTypeOther If license is not in standard text FALSE FALSE applicable Text of the user agreement/non- Required if 31.7.3 UserAgreementText Text blob FALSE FALSE standard license applicable 31.7.4 LicenseURL URL of the license URL FALSE FALSE Optional 8.2 DocumentationContributors Who contributed to generating this 32 Contributor Compound TRUE TRUE Required document Last name of the documentation 32.1 LastName text FALSE FALSE Required contributor First name of the documentation 32.2 FirstName text FALSE FALSE Required contributor 32.3 Affiliation Affiliation of documentation contributor text FALSE FALSE Required 32.4 Email Email of documentation contributor Email address FALSE FALSE Optional Persistent identifier of documentation 32.5 ID Compound TRUE TRUE Optional contributor 32.5.1 IDType Type of persistent identifier e.g. ORCID Controlled vocabulary FALSE FALSE Required 32.5.2 IDValue identifier text FALSE FALSE Required 15