GODAN ACTION LEARNING PAPER

A survey of data standards for land and nutrition data

Valeria Pesce

Global Forum on Agricultural Research and Innovation (GFAR) Lisette Mey

Land Portal 30 April 2018 Pauline L’Hénaff

Open Data Institute (ODI) Carlos Tejo-Alonso

Land Portal GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

Executive summary

GODAN Action supports data users, producers and through statistics and surveys and elaborated through intermediaries to effectively engage with open data and projections. maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen On the other hand, the truly topic-specific data standards capacity, to promote common standards and best practice, are the data dictionaries, code lists and the classification and to improve how we measure impact. schemes used for each specific type of data. This explains why the majority of the data standards identified for these This report is a short accompanying document to the two thematic topics are value vocabularies, like code GODAN Action Map of Data Standards (http://vest. lists and classification schemes. However, beyond a few agrisemantics.org) which now includes two new sections formalised classifications, most of the standardisation dedicated to data standards relevant for the two thematic work in these areas is done through recommendations topics, which are the GODAN Action project focuses in and guidelines issued by authoritative bodies. its second year: land data and nutrition data. A more in-depth analysis of the data standards and Links to the two new sections are on the homepage of standardisation gaps in the areas of land data and nutrition the Map (http://vest.agrisemantics.org). The direct link data is provided in our full gap exploration report (Pesce to the land data section is http://vest.agrisemantics.org/ et al.; 2018). by-theme/7705/7705, while the direct link to the (mal) nutrition data section is http://vest.agrisemantics.org/ by-theme/7706/7706.

We refer to our original map of standards report (Pesce V, Kayumbi GW, Tennison J et al.; 2016) for the full description of the map, the organisation of the content (metadata and classifications) and the initial coverage. What we describe in detail here is the process followed to identify relevant data standards for the two new thematic topics.

As we did for weather data in the first year of the project, we framed our survey of data standards around: the types of data commonly used for land and nutrition; the standardisation practices that are currently in use for these types of data (from statistical data formats to code lists and classification schemes); the corresponding authoritative bodies to which all experts look to, as well as important projects and initiatives.

The result of this work is the identification of the most relevant data standards for these two topics (listed in chapter 2) and their inclusion in the Map (which can be found in the links above).

An initial overview of the data standards identified shows that the data formats most widely used for these types of data are the typical statistical formats (from tabular to SDMX). Indeed, malnutrition, land tenure and land

2 use are socio-economic dimensions, mainly measured GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

Contents

1 Coverage of data standards for land data and nutrition data 04

1.1 Methodology for identification and inclusion of data standards 04

1.2 General types of data relevant for both land data and nutrition data 04

1.2.1 Statistical data 04

1.2.2 Geospatial/geopolitical data 06

1.3 Land data 06

1.4 Nutrition data 10

1.5 Experts interviewed 13

1.6 Conclusions 13

2 List of data standards added to the map 14

2.1 List of statistical and global indicators data standards 14

2.2 List of data standards relevant for the thematic topic- land 15

2.3 List of data standards relevant for the thematic topic - nutrition 17

References 20

3 GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

1 Coverage of data standards for land data and nutrition data

1.1​ Methodology for code lists and the classification schemes used (see identification and inclusion of chapters 1.3 and 1.4). data standards In addition, all these types of datasets have some geospatial dimension, although in most cases more geopolitical and limited to area codes or country codes. We followed the same methodology as for weather data standards (Pesce, Tennison, Dodds and Zervas; 2018) Land cover data, the only type of data under these themes and based our identification and selection of relevant that doesn’t have a socio-economic dimension, is the data standards around: exception. It’s geospatial data with observational features describing the physical nature of the land cover, so it the types of data commonly used for land and • uses typical data formats and conventions of geospatial nutrition; and observation data (as seen in our weather data gap • the standardisation practices that are currently in analysis report) plus a few classifications schemes of use for these types of data (from statistical data land cover types (see chapter 1.3). formats to code lists and classification schemes); Therefore, statistical and geospatial data standards are • the corresponding authoritative bodies to which all relevant for the two thematic topics. experts look up as well as important projects and initiatives; 1.2.1 Statistical data

• Interviews with experts on the points above. National statistics provide many of the data necessary to contextualise land data and nutrition data: population, age, An important aspect of the identification of the relevant gender, occupation… Many international organisations types of data is what types of users are going to need and initiatives then aggregate these data to provide local, land data and nutrition data and therefore what specific regional and global overviews. Tha main actors for this types of data are needed for their purposes. type of data are of course national governments and then The identification of data types in the following chapters the regional and global agencies that have a mandate is based on the fact, on which interviewed experts (see to collect these data (like the EU or the UN agencies). chapter 1.5) agree, that the primary users of land and However, government official statistics don’t collect all the nutrition data are policy makers. nutrition and land data that are needed to create useful These data are needed also by infomediaries, but even information systems and products, like consumption then the final objective of using these data is to influence data, details about land use and perceived land tenure policy makers. security. For these types of data, a key instrument are ad-hoc surveys.

1.2 General types of data Such surveys are normally run by national governments relevant for both land data and or by international organisations in coordination with nutrition data national governments (see chapters 1.3 and 1.4 for the role of international organisations like FAO or WHO in coordinating and aggregating surveys), but also by Malnutrition, land tenure and land use are socio- dedicated initiatives and projects. In many cases, given economic dimensions, mainly measured through statistics the complexities of the methodology and of the execution and surveys and elaborated through projections. (in order to reach maximum coverage), these surveys So the data formats adopted are normally the typical are outsourced to specialised agencies. statistical formats (from tabular to SDMX), while the really 4 topic-specific data standards are the data dictionaries, GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

The reference body for socio-economic statistics and which are the inputs and outputs in the design surveys, especially for global aggregation of data and for and production of statistics. guidelines and standards, is the UN Statistics Division.1 In particular for household surveys, the UN working group These are general standards for the metadata and the called Inter-secretariat Working Group on Household model, but then the structure of the surveys depends of Surveys2 aims at fostering coordination and harmonisation course on the data to be collected and several institutions of household survey activities. (UN, World Bank, USDA) produce guidelines for specific types of data (land use, food consumption…). Another important initiative in this area is the International Household Survey Network (IHSN). The mission of the Indeed most of the standardisation work is done through IHSN is to improve the availability, accessibility, and recommendations and guidelines, partly because the quality of survey data within developing countries, and standardisation of the methodology is more important to encourage the analysis and use of this data by national than the standardisation of the variable names for further and international development decision makers, the reuse; variable names need to be accompanied by research community, and other stakeholders. One of specifications like methods, formulas etc. Examples are their objectives is “Availability of standards, tools, and the UN “Principles and Recommendations for Population 9 guidelines that would allow data producers to document, and Housing Censuses” or the USDA “Current Population disseminate, and preserve microdata according to Survey Food Security Supplement December 2016 10 international standards and best practices”.3 Microdata File User Notes”.

All these bodies more or less recommend the same data In reality, most surveys are conducted without using standards: standards for the metadata, while for the data they use tabular formats and some conventions and guidelines • For metadata: on variables and more rarely on variable names. Quite often a lot of harmonisation work is done by the global • The XML schema of the Data Documentation agencies (UN, World Bank) that aggregate the data and 4 Initiative (DDI) for the metadata, developed make them available again in a more standardised way, specifically for the documentation and e.g. using SDMX and DDI. cataloguing of microdata Other relevant standard lists that should be considered The Statistical Data and Metadata eXchange5 • are the lists of indicators developed by many agencies (SDMX), to represent statistical data in XML: for goals and objectives in their mandate, like the FAO this standard was developed jointly by BIS, food security indicators11 or the OECD indicators12 or the ECB, EUROSTAT, IMF, OECD, UN Global Health Observatory indicators.13

• Dublin Core Metadata Initiative (DCMI) Many of these lists of indicators were developed in the • For data: context of the Sustainable Development Goals (SDGs): the official list of SDG indicators includes a number of • ISO/IEC 111796, (formally known as the ISO/ land-related and nutrition-related indicators, like 1.4.2 IEC 11179 Metadata Registry (MDR) standard, Proportion of total adult population with secure tenure an international standard for representing rights to land, 2.4.1 Proportion of agricultural area metadata for an organisation in a metadata under productive and sustainable agriculture, 5.a.1 (a) registry. Proportion of total agricultural population with ownership or secure rights over agricultural land, 2.1.1 Prevalence • Generic Statistical Business Process Model of undernourishment, 2.1.2 Prevalence of moderate or 7 (GSBPM) , a means to describe statistics severe food insecurity or 2.2.2 Prevalence of malnutrition. production in a general and process-oriented way From the data standardisation point of view, an interesting experiment regarding the SDG goals and indicators is 8 • Generic Statistical Information Model (GSIM) the Sustainable Development Goals Interface Ontology which provides a set of standardised, (SDGIO),14 which “provides a semantic bridge between consistently described information objects, 1) the Sustainable Development Goals, their targets, and indicators and 2) the large array of entities they refer 5 GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

to” and provides the full list of indicators linked to the 1.3 Land data objectives in RDF15 and in CSV.16

More details about specific indicators for the land and There are different types of data related to land that the nutrition topics are provided in sections 1.3 and 1.4. are collected and aggregated for different purposes. In order to identify the types of data that are relevant, it’s 1.2.2 Geospatial/geopolitical data important to understand the purposes for which land data is collected and the socio-economic aspects that are the We covered geospatial data standards in our analysis most affected by it. of weather data standards17, as weather data has an Land as a factor in agricultural production is of immense intrinsic geospatial dimension. importance. Secure tenure rights to land, in particular, All the key types of data relevant for land and nutrition also are critical for farmers because it means they cannot be have some geospatial dimension, although in most cases deprived of the use and control of their land. Without risk (statistics and behavioural surveys) more geopolitical and of eviction, expulsion or exclusion from their farmland, limited to area codes or country codes. farmers are more inclined to make long-term investments in their land and have better access to financial markets The most used standard for these simple country and to be able to participate in agricultural innovations, but area code is the Standard Country or Area Codes for they are also - perhaps more importantly - free to use and Statistical Use18 (M49) of the United Nations Statistics control their land on which their well-being and human Division (UNSD), linked to the ISO 3166 alpha codes. dignity depend.

Country names are in a way less important as in most However, many farmers - especially in the global South cases country codes are used, but where country - do not have clear proof or data that demonstrates the names are used, besides the country names in the UN land their families have cultivated for generations, is theirs. Standard Country or Area Codes for Statistical Use, other Governments in the world struggle to keep complete standardised country names used are the ones from the records of the land in their territory, and many complicated, ISO 3166, from the FAO Geopolitical Ontology or from competing stakes come into play when land ownership the World Integrated Trade Solution (WITS). data is involved. Incomplete or inaccurate data on land can fuel conflict and may be of less value than none at Of all the types of data used for land and nutrition, the all. Besides data on land ownership, land cover and land one with the strongest geospatial dimension is land cover use data are also immensely important for agriculture data, which is geospatial data with observational features and nutrition. For example, having data on agricultural describing the physical nature of the land cover, so it productivity when a certain type of land is cultivated, and uses typical data formats and conventions of geospatial being able to determine the effectiveness of that type of and observation data (as seen in our weather data gap land use, is very valuable and lessons that many farmers analysis) plus a few classifications schemes of land cover can benefit from, if it were to be widely shared. types (see chapter 1.3).

The geospatial dimension of land cover data is normally Narrowing the scope: SDG indicators formalised according to standards created originally as ISO specifications and in some cases then translated in Data that relates to land comes in many forms and is some markup schema (like ISO 19136:2007 codified in used in many different ways; the examples above are just OpenGIS OGC Geography Markup Language).19 a few broad indications of the importance and possible impact of land data. While broadly speaking all data that have a spatial element to them relate to land in some form or another, the focus of this scoping exercise is on land data that relates to certain land-related indicators in the Sustainable Development Goals (SDGs). For the purpose of this research that focuses on the relationship of land with agriculture and nutrition, data that is of relevance to measure any following indicators will be taken into account: 6

1 https://unstats.un.org 7 https://statswiki.unece.org/display/GSBPM/ 10 https://www.ers.usda.gov/webdocs/ 2 https://unstats.un.org/iswghs/ Generic+Statistical+Business+Process+Model DataFiles/50764/technotes2016.pdf?v=42984 3 http://www.surveynetwork.org/ 8 https://statswiki.unece.org/display/gsim/ 11 http://www.fao.org/economic/ess/ess-fs/ 4 https://www.ddialliance.org/ Generic+Statistical+Information+Model ess-fadata/en/#.Wtz2Li5ubIU 5 http://sdmx.org/ 9 https://unstats.un.org/unsd/publication/SeriesM/ 12 https://data.oecd.org/searchresults/?r=+f/type/ 6 https://en.wikipedia.org/wiki/ISO/IEC_11179 SeriesM_67rev1E.pdf indicators 13 http://apps.who.int/gho/data/node.imr GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

• Indicator 1.4.2. - Proportion of total adult population properties, and comprises the registered details for each with secure tenure rights to land, with legally property” (FAO, 2003). “The land registry is, in effect, recognised documentation and who perceive their a database of the land rights as they relate to parcels rights to land as secure, by sex and by type of tenure; within a jurisdiction, typically in the form of titles or deeds” (Pichel, 2017). The Land Registry often does not contain • Indicator 2.4.1. - Proportion of agricultural area under up to date or accurate data, especially in countries in the productive and sustainable agriculture; global South. UN-Habitat figures show that more than • Indicator 5.a.1. - (a) Proportion of total agricultural 70% of the world’s land is unregistered. population with ownership or secure rights over Cadastral data agricultural land, by sex; (b) share of women among owners or rights-bearers of agricultural land, by type The International Union of Surveyors (FIG) defines of tenure. cadaster as “a parcel based, and up-to-date land information system containing a record of interests in For this purpose, we have identified the following types land (e.g. rights, restrictions and responsibilities). It usually of land data that are of relevance: includes a geometric description of land parcels linked Types of data to other records describing the nature of the interests, the ownership or control of those interests, and often • Land Tenure data the value of the parcel and its improvements.”21 Pichel highlights that, at its most simple form, the cadastre is • Land Registry Data a graphical representation, or map, of parcels within a • Cadastral Data jurisdiction based on a survey of the parcel boundaries (Pichel, 2017). Historically, nations have started and • Informal tenure data maintained their own cadastre systems and therefore created their own standard to document the information Land Use Data • required. Basic elements one will find in most cadastres • Land cover are i) land parcels (e.g. location, boundaries, coordinates), ii) land tenure (e.g. property rights, ownership, lease, Land tenure data use rights), and iii) land value (e.g. tax value, economic value). The website Cadastral Template has attempted to Land tenure “is the relationship, whether legally or highlight the different cadastral systems used in different customarily defined, among people, as individuals or countries. groups, with respect to land”.20 Generally land tenure rights Informal tenure data are classified as either ‘formal’ or ‘informal’. Formal rights to land are those that are explicitly acknowledged by the Besides formal tenure data that is usually held by state, and provide landowners the most protection against governments, many people - particularly the rural poor - eviction or encroachment of their land, whereas informal have informal rights to their land. Access to land for the rights to land lack official recognition and protection. rural poor is often based on custom.23 Customary land rights of local communities or indigenous peoples are Data on land, people and their rights is fundamental as usually created following their traditions and through the rights to land cannot be enforced or acknowledged, and ways in which community leaders assign land use rights they therefore generally do not exist, without data to prove to the community members. In an attempt to improve these rights. Formal tenure rights are often recorded in tenure security of individuals and communities and to some form of land registration and cadastre system, reduce the number of land conflicts and disputes, many traditionally maintained by state governments. governments have instituted land reforms, often as a Land Registry Data result of wider-range policies to correct historic injustices and to distribute land more equitably. Part of these land One form of land data that is traditionally maintained by reforms is the recognition of more diverse types of tenure state governments is the Land Register, which is defined than simply the formal tenure as recorded in land registries by the Food and Agricultural Organisation (FAO) of the and cadastral systems. There is no universal standard United Nations as the “definitive record of all registered as to which types of tenure exist, as these all have their 7

14 https://github.com/SDG-InterfaceOntology/sdgio 18 https://unstats.un.org/unsd/methodology/m49/ 15 https://raw.githubusercontent.com/SDG-InterfaceOntology/sdgio/master/ 19 http://portal.opengeospatial.org/files/?artifact_id=20509 imports/sdgio_indicator_values_import.ttl 20 Land Tenure and Rural Development, FAO Land Tenure Studies, No 3, Rome, 16 https://github.com/SDG-InterfaceOntology/sdgio/blob/master/docs/ 2002. SDG_ind.csv 21 http://www.fig.net/resources/publications/figpub/pub11/figpub11.asp 17 http://www.godan.info/documents/ 22 http://cadastraltemplate.org gap-exploration-report-weather-data-standards GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

origin within customary practices in local communities and • Cadasta Foundation39 naturally vary from country to country, and community 40 to community. However, the Global Land Tool Network24 • Land Portal (GLTN) - together with partners - has attempted to • OGC – Land Administration Domain Working Group categorise various types of tenure in their continuum of (DWG)41 land rights tool. This is used as an attempt to label the different types of tenure, oftentimes recording is done • International Hydrographic Organisation (IHO)42 simply by a free text description of the customary rights to the land. Land use Land use indicates the socioeconomic use of land (for Standardisation practices example, agriculture, forestry, recreation or residential The Land Administration Domain Model26 (LADM) is use). Land use shows how people use the landscape – the first widely accepted attempt to standardise land whether for development, conservation, or mixed uses. administration elements. LADM has proven to be the Land use data often include land use, land tenure, basis of many interventions of intermediaries either agricultural and irrigation practices.

to assist governments to gather land tenure data, or to In Europe and North America, one often sees land work with communities to map their land rights for their use data in the form of zoning (i.e. areas of land that own purposes. Interviews with various experts indicated can be used for residential, or commercial purposes). that LADM is widely used as the inspiration for their Particularly in the global South, land use data includes data collection model, but that every initiative that was classifications of land holdings, such as by the state or interviewed (ESRI27, Cadasta28, Social Tenure Domain privately. Pichel argues that ideally, this data would be Model29) uses a slightly different ‘core’ version of the expanded to include details on which lands are managed LADM to set up their own data model for collection and by various government agencies (forest department, dissemination. For instance, in particular the Social Tenure mining department, etc.), which lands are protected (i.e. Domain Model30 (STDM) is the model behind a software parks, forest reserves, wetlands, etc.) and which lands product and is based on LADM. can be utilised and how.

In terms of standardising the different types of tenure, Pichel goes on to highlight that in “emerging economies”, GLTN’s continuum of land rights31 is often agreed as even this rudimentary data is either undocumented (in standard of classifying people’s rights to land. a modern information technology driven system), or if document, not publicly available. There is often no clear With regards to formats, land registry data is usually in agreement, particularly in regards to lands occupied by tabular format and cadastral data is survey data. indigenous and/or customary communities when the rights 44 Authoritative institutions and initiatives for this type of are not recognised by the state.” data are: Main standardisation practices

• National governments Also for land use data, it is normal practice to use tabular • Centre for Spatial Data Infrastructures & Land formats, following some statistical conventions and Administration (CSDILA)32 specific guidelines issued by SEEA, FAO and WCA (see the list of initiatives below), with heavy use of classification • UN Statistical Division33 and Committee of Experts schemes for types of land use. on Global Geospatial Information Management (UN-GGIM)34 Authoritative institutions and initiatives managing this type of data and therefore data standards and guidelines on • ISO35 how to manage it are: • International Union of Surveyors (FIG)36 • FAO45 • Kadaster International37 • System of Environmental Economic Accounting (SEEA)46 • Global Land Tool Network38 8

23 Land Tenure and Rural Development, FAO Land Tenure Studies, No 3, Rome, 28 http://cadasta.org/ 2002. http://www.fao.org/DOCREP/005/Y4307E/y4307e05.htm 29 https://stdm.gltn.net/ 24 www.gltn.net 30 https://stdm.gltn.net/ 25 http://mirror.gltn.net/index.php/land-tools/gltn-land-tools/ 31 http://mirror.gltn.net/index.php/land-tools/gltn-land-tools/ continuum-of-land-rights continuum-of-land-rights 26 https://www.iso.org/standard/51206.html 32 www.csdila.unimelb.edu.au 27 https://www.esri.com/en-us/home GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

• (FAO takes care of the System of Environmental- a) Land cover which refers to the bio-physical coverage Economic Accounting for Agriculture, Forestry and of land (for example, crops, grass, broad-leaved forest, Fisheries (SEEA AFF)) or built-up area) • World Census Agriculture (WCA)47 b) Land use which indicates the socioeconomic use of land (for example, agriculture, forestry, recreation or These institutions/initiatives manage similar (and mapped) residential use).57 classifications, with a good level of reciprocal coordination: Land cover data show the biophysical surface of the • FAO Definitions and Classification of Land Use, earth, including forests, agricultural land, water, wetlands, 48 Agricultural Practices and Irrigation impervious surfaces and other land cover types. Land • Classification of Land Use in the SEEA Central cover can be determined by analysing satellite and aerial Framework (System of Environmental Economic imagery. Land cover maps provide information to help Accounting)49 land managers best understand the landscape, evaluate past decisions and gain insight into the possible effects • WCA (World Census Agriculture), in the section for of their proposed interventions. Land cover datasets play land use categories50 a significant role in a variety of geographical studies, such as natural resources management, global climate • Land Use classification in “Intergovernmental change detection, sustainable urban development, and 51 52 Panel on Climate Change” (IPCC) (section 1.2 earth system simulation. describes the different land use categories) Land cover data is different from land use data, which There are also slightly different versions of these is about how humans utilise the land, but naturally, land classifications, variants, used by specific institutions for cover is very important because it determines to a great their needs: for instance the FAO variant of the SEEA CF extent whether the land is suited for a certain type of use. Land Use Classes for agriculture, fisheries and forestry (the SEEA AFF53) is slightly different in Annex III. The primary purpose of land cover classification is to describe the structure and relationship of groups of similar These classification schemes, as mentioned above, objects.59 Land cover classification therefore requires the often include classifications of land tenure in addition to definition of land cover class boundaries; those boundaries land use, and land use includes zoning, allowed uses, should be clear, precise, possibly quantitative, and based agricultural and irrigation practices. upon a set of objective criteria.60

As we said in chapter 1.2.1 on statistical data, beyond Field surveys and analysis of remote sensing imagery are general statistical standards, the structure of the surveys two primary methods for capturing information on land depends on the data to be collected and several institutions cover. The origin of the land cover classification systems produce guidelines for specific types of data. Some goes back to the vegetation systematic classification examples for the area of land use surveys are the UN concept, which was defined as a classification system “Principles and Recommendations for Population and for plants in the mid-18th century. Housing Censuses”54, the UNICEF “Multiple Indicator Cluster Surveys (MICS) Survey Planning Tools”55, the Main standardisation practices guidelines of the Demographic and Health Surveys (DHS) program of the Inter-secretariat Working Group For land cover data, there is two kind of elements present: 56 on Household Surveys including “model datasets”. • A geometry figure (geospatial dimension) Land cover • A classification scheme for types of land cover. Agriculture, forestry, industry, transport, housing and The geospatial dimension of land cover data is normally other services use land as a natural and/or an economic formalised according to standards created originally as resource. Land Cover and Land Use data forms the basis ISO specifications and in some cases then translated in for spatial and territorial analyses. Land can be divided some markup schema (like ISO 19136:2007 codified in into two interlinked concepts: OpenGIS OGC Geography Markup Language).

9

33 https://unstats.un.org/ 40 https://www.landportal.org to Data”, Technical University of Munchen Thesis, 34 http://ggim.un.org/ 41 http://www.opengeospatial.org/projects/groups/ February 2017. 35 https://www.iso.org/ landadmin 44 F. Pichel, “How Open Data in Land Information 36 http://www.fig.net/ 42 https://www.iho.int/ Impacts Government Efficiency and Citizen Access 37 https://www.kadaster.nl/internationaal 43 F. Pichel, “How Open Data in Land Information to Data”, Technical University of Munchen Thesis, 38 http://gltn.net/ Impacts Government Efficiency and Citizen Access February 2017. 39 http://cadasta.org/ 45 http://www.fao.org GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

A number of land cover classification schemes have dataset that was more current, of known accuracy been developed to meet specific user requirements for and that had higher spatial resolution and greater land cover mapping exercises, independent of scale, internal consistency than any other existing datasets. nomenclature and quality. For many years, agencies at Thus its primary focus was on fast delivery of a global various governmental levels and institutions have been product for use in other IGBP initiatives.70 Finally a collecting land cover data, but most of them have worked classification of 17 items was released and there is a independently and without coordination. Currently, these translation to UN LCCS codes (version 2) prepared institutions/initiatives manage similar (and mapped) by Harold et al.71 classifications, trying to reach a good level of reciprocal coordination. For instance: • A work related is the ISO 19144-2:2012 “Land Cover Meta Language (LCML)”72 that allows • FAO Land Cover Classification System (LCCS) different land cover classification systems to be “is a comprehensive, standardised a priori described based on the physiognomic aspects. ISO classification system, designed to meet specific user 19144-2:2012 recognises that there exist a number requirements, and created for mapping exercises, of land cover classification systems. It provides a independent of the scale or means used to map. common reference structure for the comparison Any land cover identified anywhere in the world can and integration of data for any generic land cover be readily accommodated. The classification uses classification system, but does not intend to replace a set of independent diagnostic criteria that allow those classification systems. correlation with existing classifications and legends”. This classification system has been updated three 1.4 Nutrition data times, so there are 3 versions available: version 1, year 200061; version 2, year 200562; version 3, Nutrition is both “the process of providing or obtaining the year 201663 food necessary for health and growth” and “the branch of science that deals with nutrients and nutrition, particularly The land cover classification of the SEEA CF (a • in humans”.73 Under the first aspect, according to the statistical classification)64 currently is the international World Bank, nutrition “encompasses access to food standard to prepare the physical accounts for land at the household level, health services and a healthy cover. The SEEA Agriculture, Forestry and Fisheries environment and adequate child caring practices.”74 SEEA-AFF, which was adopted by the UNCEEA as an “Internationally Approved Methodological As such, data relevant for nutrition comes from many Document” in support of the SEEA CF, also applies different types of datasets: the land cover classification of the SEEA CF65. The SEEA CF (and the same in SEEA AFF) land cover • Food security classification includes 14 mutually exclusive and food security data; unambiguous land cover categories. FAO LCCS is • aligned with SEEA CF (there is an enormous number • demographic data of different land cover features that can be created with the LCCS approach, so for standardisation and • Health data harmonisation purposes across statistical data sets, population health data a classification comprising 14 classes has been • established). • Food consumption • The Land Cover Climate Change Initiative • consumption surveys (LC-CCI)66 offers another land cover classification, based on the UN LCCS (version 2).67 One interesting • demographic data point of this classification is the declaration of colors • geospatial data for each item of the classification.68 • market data • The International Global Biosphere Programme (IGBP) global land cover classification69 was • Food safety made to support a number of IGBP initiatives. Their stated objective was to provide a global land cover • safety/quality data 10

46 https://seea.un.org/ 50 http://www.fao.org/3/a-i4913e.pdf 54 https://unstats.un.org/unsd/publication/SeriesM/ 47 www.fao.org/world-census-agriculture/en/ 51 http://www.ipcc.ch/ipccreports/sres/land_use/ SeriesM_67rev1E.pdf 48 http://www.fao.org/fileadmin/templates/ess/ index.php?idp=38 55 http://mics.unicef.org/tools ess_test_folder/Definitions/Land_Use_Definitions_ 52 https://www.ipcc-nggip.iges.or.jp/public/2006gl/ 56 https://dhsprogram.com/data/Model-Datasets.cfm FAOSTAT.xlsx pdf/4_Volume4/V4_03_Ch3_Representation.pdf 57 http://www.fao.org/faostat/en/#data/RL/metadata 49 https://unstats.un.org/unsd/envaccounting/ 53 http://www.fao.org/economic/ess/environment/ 58 https://oceanservice.noaa.gov/facts/lclu.html seearev/ methodology/en/ GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

• crop treatment data; • Demographic data, census data (population, age, gender, income) • chemical data; • Population health data (childbirth, breastfeeding, • storage data; anthropometry, nutritional disorders) • packaging data; • Food consumption/dietary habits surveys • food tracking data • Government expenditure (on nutrition interventions) • Nutrition • Government legislation and policies • nutrients data Examples of sources used are: United Nations, Department • Other statistical data of Economic and Social Affairs, Population Division for population statistics (modelled estimates), World Bank for However, the GODAN Action project is focusing on a household surveys and GDP per capita data from OECD, specific aspect of the nutrition domain. UNICEF/WHO/World Bank Group Joint Child Malnutrition Estimates based on population surveys, UNICEF for Narrowing the scope: the Global Nutrition Report: national birth registrations, birth and breastfeeding data, malnutrition. household surveys and routine reporting systems, WHO The GODAN Action project has agreed to focus the health surveys, modelled estimates on nutritional disorders thematic topic on the data used by the Global Nutrition and deficiencies from the Global Health Observatory Data Report75 (GNR), an annual report on the status of nutrition Repository, FAO food balance sheets and surveys from globally against a number of indicators, produced for the different organisations (IFPRI SPEED, ILO, IDS, SUN) last 3 years by an Independent Expert Group guided by on government expenditure and implementation of rights. the Global Nutrition Report Stakeholder Group (consisting These data are used to assess the status and monitor of representatives of organisations such as UNICEF, progress against around 80 indicators. WHO, European Commission, Scaling Up Nutrition76, IFAD, World Food Programme, FAO, World Bank, Bill The breakdown of included indicators is: & Melinda Gates Foundation and Ministries of different countries). • Demographics: 5

“Global Nutrition Report datasets contain data for all the • Anthropometry: 20 indicators used in each year’s Global Nutrition Report. • Micronutrient status outcomes: 3 The data are compiled from secondary sources including the United Nations Children’s Fund (UNICEF), the World • Diet related risk factors for NCDs: 3 Health Organisation (WHO), and the World Bank, among many others that have contributed their data. Datasets • Determinants: 18 (Immediate 2; Underlying, 13 Basic, 3) broadly contain information on adult and child nutrition, economics and demography, nutrition intervention • Intervention coverage: 11 coverage, and policies and legislation in the nutrition sector.”77 • Financial Resources: 6 Looking more in depth at the data sources listed in the • Institutional/Legislative/Policy: 7 Technical note of the GNR “Nutrition Country Profile Indicators: Definitions and Sources”, the focus is clearly Plus indicators of overweight, obesity and nutrition-related Non-Communicable Diseases: mostly on food security and malnutrition, rather than the scientific aspects of nutrition. • Percent population urban The key types of data covered by the above GNR list are: • Percent population >65 years of age • Food security surveys (access to food, measured • Percent of under 5’s who are overweight also through some of the types of data below) • Adolescent obesity

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59 Sokal, R. Classification: Purposes, principles, 61 http://www.fao.org/docrep/003/x0596e/ cover classification; or Annex I, C. Land Cover progress, prospects. Science 1974, 185, x0596e00.htm Basic Rules and Classification (interim)) 1115–1123. 62 http://www.fao.org/docrep/008/y7220e/ 65 http://www.fao.org/faostat/en/#data/LC/ 60 https://pdfs.semanticscholar.org/ y7220e00.htm metadata e72b/4783e5860dac56d27aa28294837e4f9d6f08. 63 http://www.fao.org/3/a-i5232e.pdf 66 http://maps.elie.ucl.ac.be/CCI/viewer/download/ pdf 64 https://seea.un.org/sites/seea.un.org/files/ ESACCI-LC-Ph2-PUGv2_2.0.pdf seea_cf_final_en.pdf (Page 178, table 5.12 Land GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

• Adult overweight and obesity (x9) (M49) of the United Nations Statistics Division (UNSD) • Hypertension There are other types of lists that are not always • Raised fasting blood glucose immediately perceived as data standards but are indeed • Raised cholesterol levels (Dyslipidaemia) agreed-upon reference lists that allow to interpret and combine data in an agreed way to provide a correct view • High acquisition of calories of the status of food security and malnutrition: these are the authoritative lists of indicators developed by different • Calorie supply from fruit and vegetables agencies - and more often in coordination between • Income inequality agencies - and the numeric classifications to assess the severity of phenomena, like: Implementation of plans to manage diabetes and • 82 hypertension. • The GNR indicators themselves

83 Standardisation practices • The WHO Indicator Metadata Registry (IMR)

84 As mentioned above, data in the GNR are compiled from • The FAO food security indicators secondary sources. This means that the standardisation • The Integrated Food Security Phase Classification practices that we considered for compiling the list of (IPC) for classifying the severity and magnitude of relevant standards for the GNR are those in use in the data food insecurity85 management and statistics divisions of the organisations that provide the secondary data, in this case the UN, • The country classification by income level based on World Bank, WHO, UNICEF and the others mentioned 2016 GNI per capita from the World Bank in the list of sources. The World Bank also offers an API to access the data, As we observe above in chapter 1.2.1 on statistical data, a where the API response format, whether in XML or Json, lot of harmonisation work is done by these global agencies uses a local World Bank schema, while both the request that aggregate the data from primary sources (normally and the response use the UN the Standard Country or national statistics and surveys) and make them available Area Codes for Statistical Use (M49) and the WB country again in a more standardised way. classification by income level based on 2016 GNI per capita. The most common standards they use are: As we said in chapter 1.2.1 on statistical data, beyond • For the data format or schema: general statistical standards, the structure of the surveys • The XML schema of the Data Documentation depends on the data to be collected and several Initiative79 (DDI) for the metadata institutions produce guidelines for specific types of data. Some examples for the area of nutrition are the FAO • The Statistical Data and Metadata eXchange80 “Guidelines for assessing nutrition-related Knowledge, (SDMX), to represent statistical data in XML: Attitudes and Practices”86, the UNICEF “Multiple Indicator this standard was developed jointly by BIS, Cluster Surveys (MICS) Survey Planning Tools”87, the ECB, EUROSTAT, IMF, OECD, UN guidelines of the Demographic and Health Surveys (DHS) program of the Inter-secretariat Working Group Tabular format, normally Excel or CSV • on Household Surveys including “model datasets”88, the • For the data values: UN “Principles and Recommendations for Population and Housing Censuses”89, the USDA “Current Population • For food consumption: the UN Classification Survey Food Security Supplement December 2016 of Individual Consumption According to Microdata File User Notes”90 or the IHSN “Measuring Purpose81 (COICOP), which in group 01 has food consumption”91 guide. a sub-classification of food types • For country and area codes: the Standard Country or Area Codes for Statistical Use 12

67 https://unstats.un.org/unsd/envaccounting/ 70 http://www.fao.org/forestry/4031- 74 http://blogs.worldbank.org/health/ ceea/meetings/twelfth_meeting/UNCEEA_FAO_ 0b6287f13b0c2adb3352c5ded18e491fd.pdf nutrition-and-agriculture-bridging-gap LandCover.pdf 71 http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report%20 75 http://www.globalnutritionreport.org/ 68 http://maps.elie.ucl.ac.be/CCI/viewer/download/ Series/GOLD_43.pdf 76 http://scalingupnutrition.org CCI-LC_Maps_Legend.pdf 72 https://www.iso.org/standard/44342.html 77 http://www.globalnutritionreport.org/the-data/ 69 http://www.eomf.ou.edu/static/IGBP.pdf 73 Oxford dictionary dataset-and-metadata/ GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

1.5 Experts interviewed So the data formats adopted are normally the typical statistical formats (from tabular to SDMX), while the really In order to make this platform a reliable asset, we called topic-specific data standards are the data dictionaries, (and we’ll continue calling) on our networks of experts to code lists and the classification schemes used. These help us improve the map of standards and make it grow. defines on the one hand the required dimensions and variables (column names) for each type of statistic and Below is the list of experts who have contributed to the on the other hand the code lists or classification schemes identification of important initiatives and data standards to be used as values for specific variables or dimensions. for land data and nutrition data. Some of them had interviews with us or filled in our questionnaire containing Land cover data is different, it’s geospatial data with specific questions about common practices and existing observational features describing the physical nature standards. of the land cover, according to more or less agreed (or mapped) classifications. So it uses typical data formats Land data and conventions of geospatial and observation data (as seen in our weather data gap analysis) plus a few similar Francesco Nichola Tubiello (FAO) • classifications schemes of land cover types. Giulia Conchedda (FAO) • This brief overview explains why the majority of the data • Caterina Caracciolo (FAO) standards identified for these two thematic topics are value vocabularies, like code lists and classification schemes, • Tim Fella (ESRI) while data formats and schemas are not topic-specific but rather common statistical/observation standards that had Brent Jones (ESRI) • been already included in the Map. Much more work has • Frank Pichel (Cadasta Foundation) been done in these areas on the definition of the agreed values of measurements than on standardised variable • Marcello DeMaria (University of Reading) names (properties, schema elements).

Nutrition data Most of the standardisation work is done through recommendations and guidelines, partly because the Alan Stanley (IDS) • standardisation of the methodology is more important than • Jessica Meeker (IDS) the standardisation of the variable names for further reuse; variable names need to be accompanied by specifications • Catherine Leclercq (FAO Nutrition) like methods, formulas etc. 1.6 Conclusions A more in-depth analysis of the data standards and standardisation gaps in the areas of land data and nutrition Malnutrition, land tenure and land use are socio-economic data will be, extending this initial survey, is included in dimensions, mainly measured through statistics and our full gap analysis report (Pesce et al. 2018). surveys and elaborated through projections.

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78 https://www.globalnutritionreport.org/files/2017/11/ 81 https://unstats.un.org/unsd/iiss/ 84 http://www.fao.org/economic/ess/ess-fs/ Technical-note-Nutrition-Country-Profile-Indicators- Classification-of-Individual-Consumption-According- ess-fadata/en/#.Wtz2Li5ubIU Definitions-and-Sources.pdf to-Purpose-COICOP.ashx 85 http://www.ipcinfo.org/ 79 https://www.ddialliance.org/ 82 http://www.globalnutritionreport.org/2014/07/03/ 86 http://www.fao.org/docrep/019/i3545e/i3545e00. 80 http://sdmx.org/ indicators-for-country-profiles/ htm 83 http://apps.who.int/gho/data/node.wrapper. 87 http://mics.unicef.org/tools imr?x-id=1 88 https://dhsprogram.com/data/Model-Datasets.cfm GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

2 List of data standards added to the map

Based on the research above, below are the 63 data standards that we now (as of 30 April 2018) have in the map of data standards for the thematic topics Land and Nutrition.

Two search pages dedicated to these standards can be found on the map of data standards at:

Land data: http://vest.agrisemantics.org/by-theme/7705/7705

(Mal)nutrition data: http://vest.agrisemantics.org/by-theme/7706/7706 2.1 List of statistical and global indicators data standards

Total: 8 (in alphabetical order.)

Name Publisher Types of data 1 Core Public European Official records, Administration and legislation resources Service Commission Vocabulary 2 Data DDI Alliance Socio-economic data, Demographic and social statistics, Documentation Food consumption/dietary surveys, Health statistics, Initiative (DDI) Income data, Population data, Other statistical data 3 ISI Multilingual Other statistical data, Socio-economic data, Demographic Glossary of and social statistics, Food consumption/dietary surveys, Statistical Terms Health statistics, Income data, Population data 4 Statistical Data Bank for International Socio-economic data, Other statistical data, Demographic and Metadata Settlements, and social statistics, Food consumption/dietary surveys, eXchange European Central Health statistics, Income data, Population data Bank, eurostat, International Monetary Fund, OECD, United Nations, World Bank 5 Sustainable Socio-economic data, Other statistical data, Demographic Development and social statistics, Food consumption/dietary surveys, Goals Interface Health statistics, Income data, Population data Ontology 6 The International International Statistics Other statistical data, Socio-economic data, Demographic Statistical Institute Institute and social statistics, Food consumption/dietary surveys, Glossary Health statistics, Income data, Population data 7 The RDF Data W3C Datasets, Socio-economic data, Other statistical data, Cube Vocabulary Resource-specific metadata, Demographic and social statistics, Food consumption/dietary surveys, Health statistics, Income data, Population data 8 The Statistical Joannem Research Other statistical data, Socio-economic data, Demographic Core Vocabulary and social statistics, Food consumption/dietary surveys, 14 Health statistics, Income data, Population data

89 https://unstats.un.org/unsd/publication/SeriesM/SeriesM_67rev1E.pdf 90 https://www.ers.usda.gov/webdocs/DataFiles/50764/technotes2016.pdf?v=42984 91 http://www.ihsn.org/node/34 GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

2.2 List of data standards relevant for the thematic topic - land

Total: 26 (in alphabetical order)

Name Publisher Types of data 1 ACORD ACORD Administration and legislation resources, Land tenure data 2 Actuar Actuar Administration and legislation resources, Land tenure data 3 AgEcon AgEcon Administration and legislation resources, Land tenure data 4 AGTER AGTER Administration and legislation resources, Land tenure data 5 Amigos de la Tierra Amigos de la Tierra Administration and legislation resources, Land tenure data 6 Asian Farmers Asian Farmers Association Administration and legislation Association resources, Land tenure data 7 Cambodian League Cambodian League for the Promotion Administration and legislation for the Promotion and and Defense of Human Rights resources, Land tenure data Defense of Human Rights 8 CSDILA cadastral University of Melbourne, University Cadaster data, Land tenure data, template of Melbourne. Centre for Spatial Data Administration and legislation Infrastructures & Land Administration resources (CSDILA) 9 FAO Definitions and Food and Agriculture Organisation of Land use data, Socio-economic Classification of Land the United Nations (FAO) data, Farming practices/farm Use, Agricultural activities Practices and Irrigation 10 FAO Land Cover Food and Agriculture Organisation of Natural Resources, Earth and Classification System the United Nations (FAO) Environment data, Land cover data 11 Focus on Land in Africa Focus on Land in Africa Administration and legislation Tags resources, Land tenure data 12 Global Land Indicator Global Land Tool Network Administration and legislation Initiative resources, Land tenure data 13 International International Geosphere–Biosphere Natural Resources, Earth and Geosphere–Biosphere Programme (IGBP) Environment data, Land cover data Programme (IGBP) land cover classification 14 International Land International Land Coalition Administration and legislation Coalition resources, Land tenure data 15 ISO 19144-2:2012 International Standards Organisation Land cover data, Natural Geographic information Resources, Earth and Environment - Classification systems data -- Part 2: Land Cover Meta Language (LCML)

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Name Publisher Types of data 16 Land Cover CCI - Climate Change Initiative (CCI) Natural Resources, Earth and Product User Guide Environment data, Land cover data, Weather/meteorological data 17 Land Use categories Food and Agriculture Organisation of Administration and legislation in World Census of the United Nations (FAO) resources, Land tenure data, Agriculture Socio-economic data, Land use data, Farming practices/farm activities 18 LandVoc - the Linked The Land Portal Foundation Land tenure data, Administration Land Governance and legislation resources Thesaurus 19 Open Development Open Development Mekong Administration and legislation Mekong taxonomy resources, Land tenure data 20 Resource Equity Resource Equity Administration and legislation resources, Land tenure data, [Other/various types] 21 Rights and Resources Rights and Resources Institute Administration and legislation Institute resources, Land tenure data 22 SEEA CF/SEEA Statistical Commission of the United Land cover data, Natural AFF Land Cover Nations Resources, Earth and Environment Classification data 23 SEEA CF/SEEA AFF Statistical Commission of the United Socio-economic data, Land use Land Use Classification Nations data, Farming practices/farm activities 24 SO 19152:2012 International Standards Organisation Land tenure data, Administration Geographic information and legislation resources -- Land Administration Domain Model (LADM) 25 Social Tenure Domain Global Land Tool Network (GLTN) Administration and legislation Model (STDM) resources, Land tenure data, Informal land ownership data 26 Suelo Urbano (Habitat Habitat for Humanity LAC Administration and legislation for Humanity LAC) resources, Land tenure data

16 GODAN ACTION LEARNING PAPER •••• A SURVEY OF DATA STANDARDS FOR LAND AND NUTRITION DATA

2.3 List of data standards relevant for the thematic topic - nutrition

Total 29 (in alphabetical order)

Name Publisher Types of data 1 Codex Classification of Food and Agriculture Food research data, Food product Foods and Animal Feeds Organisation of the United data, Research and agronomic data, Nations (FAO), World Value chain data, Socio-economic data, Health Organisation, Codex Food consumption/dietary surveys, Alimentarius Commission Demographic and social statistics 2 Codex General Standard Food and Agriculture Socio-economic data, Value chain data, for Food Additives Organisation of the United Food additives, Food product data (GSFA) Food Additive Nations (FAO), World Index Health Organisation, Codex Alimentarius Commission 3 Codex General Standard Food and Agriculture Food product data, Value chain data, for Food Additives Organisation of the United Socio-economic data (GSFA) food category Nations (FAO), World system Health Organisation, Codex Alimentarius Commission 4 Data Documentation DDI Alliance Socio-economic data, Demographic Initiative (DDI) and social statistics, Food consumption/ dietary surveys, Health statistics, Income data, Population data, Other statistical data 5 eCert - XML for Sanitary United Nations, CEFACT Agronomic data, agricultural & Phytosanitary technologies, Food product data, Certificates Research and agronomic data, Value chain data, Socio-economic data 6 EFSA FoodEx 2 European Food Safety Authority Food research data, Food product (EFSA) data, Research and agronomic data, Value chain data, Socio-economic data, Food consumption/dietary surveys, Demographic and social statistics 7 EFSA Standard sample European Food Safety Authority Socio-economic data, Value chain data, description for food and (EFSA) Food product data, Food consumption/ feed dietary surveys, Demographic and social statistics 8 Electronic Business Organisation for the Food product data, Value chain data, using eXtensible Markup Advancement of Structured Socio-economic data Language Information Standards (OASIS), CEFACT, United Nations 9 FAO food security Food and Agriculture Food security, Socio-economic data indicators Organisation of the United Nations (FAO) 10 FAOSTAT Commodity Food and Agriculture Socio-economic data, Value chain data, List Organisation of the United Food product data Nations (FAO)

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Name Publisher Types of data 11 Food Product Ontology ITMO University Food nutrients, Food product data, Research and agronomic data, Value chain data, Socio-economic data 12 FOODON Food nutrients, Livestock research data, Plants/germplasm, Food research data, Research and agronomic data, Food consumption/dietary surveys, Food product data, Demographic and social statistics, Socio-economic data 13 INFOODS Standards for Food and Agriculture Food nutrients, Food product data, food composition data Organisation of the United Research and agronomic data, Value interchange Nations (FAO), United Nations chain data, Socio-economic data, Food University consumption/dietary surveys, Food research data, Demographic and social statistics 14 INFOODS Tagnames for Food and Agriculture Research and agronomic data, Food Food Components Organisation of the United nutrients, Socio-economic data, Value Nations (FAO), United Nations chain data, Food product data, Food University consumption/dietary surveys, Food research data, Demographic and social statistics 15 Integrated Food Security Integrated Food Security Phase Food security, Socio-economic data Phase Classification Classification (IPC) (IPC) 16 ISI Multilingual Glossary Other statistical data, Socio-economic of Statistical Terms data, Demographic and social statistics, Food consumption/dietary surveys, Health statistics, Income data, Population data 17 LanguaL Thesaurus - United States Food and Drug Research and agronomic data, Food International Framework Administration, Danish Food nutrients, Food product data, Food for Food Description Informatics research data, Value chain data, Socio- economic data, Food consumption/ dietary surveys, Demographic and social statistics 18 MESCO Meat Supply Food product data, Value chain data, Chain Ontology Socio-economic data 19 Open Food Facts Food LIRMM Food nutrients, Food product data, Ontology Research and agronomic data, Value chain data, Socio-economic data, Food consumption/dietary surveys, Food research data, Demographic and social statistics 20 SmartProducts Network SmartProducts project, Food nutrients, Food product data, of Ontologies: Food KNOWLEDGE MEDIA Research and agronomic data, Value ontologies INSTITUTE - THE OPEN chain data, Socio-economic data UNIVERSITY

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Name Publisher Types of data 21 Statistical Data and Bank for International Socio-economic data, Other statistical Metadata eXchange Settlements, European Central data, Demographic and social Bank, eurostat, International statistics, Food consumption/dietary Monetary Fund, OECD, United surveys, Health statistics, Income data, Nations, World Bank Population data 22 The Harmonised World Customs Organisation, Socio-economic data, Value chain data, Commodity Description United Nations Statistics Division Food product data and Coding System (UNSD) 23 The International International Statistics Institute Other statistical data, Socio-economic Statistical Institute data, Demographic and social Glossary statistics, Food consumption/dietary surveys, Health statistics, Income data, Population data 24 The RDF Data Cube W3C Datasets, Socio-economic data, Other Vocabulary statistical data, Resource-specific metadata, Demographic and social statistics, Food consumption/dietary surveys, Health statistics, Income data, Population data 25 The Statistical Core Joannem Research Other statistical data, Socio-economic Vocabulary data, Demographic and social statistics, Food consumption/dietary surveys, Health statistics, Income data, Population data 26 UN Central Product Department of Economic and Socio-economic data, Value chain data, Classification Social Affairs of the United Food product data Nations, United Nations Statistics Division (UNSD) 27 USDA National Nutrient USDA Nutrient Data Laboratory Food nutrients, Socio-economic data, Database for Standard (NDL), United States Department Value chain data, Food product data, Reference of Agriculture (USDA) Food research data, Research and agronomic data 28 What We Eat in America United States Department of Food nutrients, Food product data, Food (WWEIA) Agriculture (USDA) research data, Research and agronomic data, Value chain data, Socio-economic data 29 WHO Indicator Metadata World Health Organisation Health statistics, Demographic and Registry (IMR) (WHO) social statistics, Socio-economic data

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References

Pesce, V., Mey, L., L’Hénaff, P. and Tejo-Alonso C. (2018), Land Data and Nutrition Data in the Land Portal and the Global Nutrition Report: A Gap Analysis Report (available shortly on F1000 Research GODAN Gateway)

Pesce V, Tennison J, Mey L et al. A Map of Agri-food Data Standards [version 1; not peer reviewed]. F1000Research 2018, 7:177 (document) (doi: 10.7490/f1000research.1115260.1)

Pesce V, Tennison J, Dodds L and Zervas P. Weather data standards: a gap exploration report [version 1; not peer reviewed]. F1000Research 2018, 7:1163 (document) (doi: 10.7490/f1000research.1115856.1)

20 GODAN Action is supported by the UK Department for International Development (DFID), led by Wageningen Environmental Research with international partners AgroKnow, AidData, CTA, FAO, GFAR, IDS, Land Portal, and the ODI.

For more information visit the GODAN website www.godan.info/godan-action

Follow GODAN on Twitter: @godanSec

ORCID Identifiers:

Valeria Pesce https://orcid.org/0000-0003-3860-4304

Lisette Mey - not available

Pauline L’Hénaff - not available

Carlos Tejo-Alonso - not available

This GODAN Action publication is licensed under a Creative Commons Attribution 3.0 Unported License.