Closed matamadio closed 1 year ago
more clarity please @matamadio on:
Attributes were introduced or changed in the V schema
time reference (year): This was enabled in E/H already - has this been duplicated, or just extended to V/L?
* V schema trimmed a bit * Attributes were introduced or changed in the V schema
Before: mover.f_scoring
Required | Fields | Data type | Reference table | Description |
---|---|---|---|---|
* | geo_applicability | VARCHAR | ISO code(s) of countries to which the data applies | |
* | geographic_relevance_score | ENUM | mover.geographic_relevance_score_enum | How well the data applies to the countries of application |
Integrated into model table, applicability split into transferability at country level, and name of specific area if curve is based on local scale info (e.g. city level). Tranasferability notes instead of a fixed enum to allow better explanation.
Required | Field name | Values | Description |
---|---|---|---|
* | country_transferability | country_name | ISOa3 list of countries in which the model could be applied |
local_applicability | Area within a country or region to wich the model specifically applies | ||
transferability_notes | Details about applicability to different areas |
Required | Field name | Values | Description |
---|---|---|---|
analysis_details | Description of analysis that produced the function | ||
par_names | Parameters considered by the function | ||
im_name | Name of loss intensity measure | ||
im_units | Unit of loss intensity measure | ||
n_events | Number of events the function has been built on | ||
n_assets | Number of assets the function has been built on | ||
is_fit_good | BOOL | Is the fit good overall? | |
* | is_edp_thre | BOOL | Is the damage scale is associated to an engineered-design parameter threshold? |
* | is_dm_factor | BOOL | Is the damage scale is associated to a damage factor? |
* | is_casualties | BOOL | Is the damage scale is associated to a casualties estimation? |
* | is_downtime | BOOL | Is the damage scale is associated to a downtime estimation (e.g. service interruption)? |
time reference (year): This was enabled in E/H already - has this been duplicated, or just extended to V/L?
It had time_start, time_end and time_span. Added and extended time_year as a reference year for scenarios (e.g. "2020" for current, "2080" for scenario). Simpler alternative to time span.
Some initial feedback on the proposed MVP mostly from the perspective of more clearly aligning the core conceptual data model with other metadata standards.
In the JKAN schema, mapping to DCAT and the proposed datapackage format, the "contribution" table maps to a dataset which has some commonly defined metadata fields.
But in the proposed MVP we're using different names for those entities and those commonly defined properties. My suggestion would be to align the naming to clarify mapping between different serialisations and implementations.
So:
contribution
to dataset
distribution
rather than resource
for the data files associated with a dataset, to align with DCATdescription
rather than abstract
publisher
rather than organization
spatial
rather than geo_coverage
. geo_area
on Event Set also looks like it's essentially the same field, so ought to use the same name?title
rather than name
of a resource (again, semantically it's the same property I think?)category
or perhaps better theme
instead of component
to categorise the H, E, V, L datasets. In GeoDCAT, theme is used to refer to a controlled vocabulary like the broad INSPIRE Themes. That seems to match the top-level categories of H, E, V, L data pretty well?Some other comments/questions:
organization
/publisher
may need to be a reference to a table, rather than just a name?contribution
that would store a stable public identifier for the dataset. E.g. a UUID or URL, rather than a SQL table row. I think the same applies to other entities that might need to be referenced across datasets/projects, e.g. Event and Event Set. That will be needed to round-trip data through the database, support harvesting, etcmediatype
rather than format
to describe the format of a resource. mediatype
uses the IANA media type rather than a string, e.g. "text/csv" rather than "csv". This would avoid maintaining a separate list of format names? The only item in common.format
that doesn't map across is api
. DCAT separately defines "access url" (how to directly access a resource) from "access service" (a service that serves the dataset) publish
- this flag is there to support a catalogue implementation, so might be required in a SQL schema, but not necessary anywhere elseissued
(date the dataset was published) and modified
(date it was updated). These might be optional, but they are basic field used for open datasets and also if you're using the SQL database as a means to manage multiple datasets locally within a project. Suggested changes above in table format for my own convenience
RDL v0.1 | RDL v0.1.1 | Comments/Notes |
---|---|---|
contribution | dataset | |
resource | distribution | |
abstract | description | |
organization | publisher | |
geo_coverage | spatial | geo_area on Event Set looks like it's essentially the same field so ought to use the same name? |
name | title | |
component | category (or theme?) | Categorizes H,E,V,L datasets |
Thanks @ldodds for the review. I agree on most alignment changes, with some comments and questions.
The one misalignment I see with current MVP is that in the same contribution/dataset page, we currently put different datasets that are described by the same general attributes, but also have some peculiar attributes. I am still not sure if it is better to keep them in the same page for search/download convenience or split them for metadata clarity.
E.g. http://jkan.riskdatalibrary.org/datasets/exp-ssd-all/ >> 3 different resource downloads, each one from a different source. Easier to find and download them altogether since they are related, but needs to rely on multiple "source" field; description becomes more generic to cover all. Would it be better to have 3 separate pages per each resource, with some form of inter-linkage?
Borrowing Taku's table:
RDL v0.1 | RDL v0.1.1 | Comments/Notes |
---|---|---|
component | category (or theme?) | We didn't use category to not mess with the main default category variable used in Jkan; but I guess we can do it. But then we need to rename the E.category attribute, shown below |
contribution | dataset | ok |
resource | distribution | to me sounds less clear/appropriate than resource, but ok (may prefer to print resource in the webpage) |
abstract | description | ok |
organization | publisher | ok |
geo_coverage | spatial | this is meant for specific ISO3 code, while geo_area on Event Set is meant for a location name as sub-domain, eg. geo_coverage : AFG; geo_area : Kabul |
name | title | ok |
format | mediatype | ok |
publish | / | ok (but then have some other way to easily hide datasets from cat in case of need) |
/ | issued | ok |
/ | modified | ok (only show if different from issued) - but we also have version attribute for this |
Attribute | Description | Type |
---|---|---|
Category | Type of asset |
|
@matamadio for the South Sudan example I think those should actually be three separate datasets each with a single distribution. My reasoning here is that they all come from separate original datasets managed by different organisations which means they have different metadata and probably licensing. (I'm not sure that the CC-BY licence on the OSM extracts is legitimate as OSM uses ODbL and requires use of the same licence for any non-trivial extracts?).
It looks like the common elements here are country of coverage and that they might have been produced by the same project/team? A country based grouping of datasets (e.g. as a custom category page built using the geo coverage) would give one easy way to represent these within the current data model and catalogue. Being able to describe the "Project" that produces some datasets might also be helpful, but that seems like an extension.
According to new plan to have data and metadata hosted by the DDH2 platform, we need to map our schema to the one in use there, which is a modified version on ISO-19115.
There is a already a good alignment for general dataset attributes. I will look into mapping-matching of current RDL schema version.
The Global Landslide hazard map is a gridded dataset of landslide hazard produced at the global scale. Landslides happen around the world and have devastating impacts on people and the built environment. To better understand the spatial and temporal distribution of landslide hazard worldwide, the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR) commissioned Arup to undertake a landslide hazard assessment at a global scale. Using a global landslide inventory, landslide susceptibility information provided by NASA, and an innovative machine learning model, our geohazard and risk management experts produced a state-of-the-art quantitative landslide hazard map for the whole world.
\nThe dataset comprises gridded maps of estimated annual frequency of significant landslides per square kilometre. Significant landslides are those which are likely to have been reported had they occurred in a populated place; limited information on reported landslide size makes it difficult to tie frequencies to size ranges but broadly speaking would be at least greater than 100 m2. The data provides frequency estimates for each grid cell on land between 60°S and 72°N for landslides triggered by seismicity and rainfall. Applications of this dataset include improved hazard screening based on frequency and severity, consistent national, regional and global scale exposure assessment, estimates of annual expected impact on population and the built environment.
\n", "acronym": null, "topics": [ { "id": "610dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "Environment and Natural Resources" } ], "practice": { "code": "ENV", "name": "Environment, Natural Resources & the Blue Economy" }, "work_unit": null, "working_unit_vpu": null, "wb_project_reference": null, "grant_number": null, "parent_dataset": null, "collection_id": [ "World Bank Energy Datasets" ], "translated_title": null, "point_of_contact": [ { "id": "620dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "sfraser@worldbank.org", "role": { "code": "POINT_OF_CONTACT", "name": "Point of Contact" }, "email": "sfraser@worldbank.org", "upi": "000474062", "externally_visible_email": false }, { "id": "630dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "Emma Katrine Alexandra Phillips", "role": { "code": "TEAMLEAD", "name": "Team Lead" }, "email": "ephillips@worldbank.org", "upi": "000339312", "externally_visible_email": false }, { "id": "640dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "Mattia Amadio", "role": { "code": "COLLABORATOR", "name": "Collaborator" }, "email": "mamadio@worldbank.org", "upi": "000542499", "externally_visible_email": false }, { "id": "650dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "Stuart Alexander Fraser", "role": { "code": "COLLABORATOR", "name": "Collaborator" }, "email": "sfraser@worldbank.org", "upi": "000474062", "externally_visible_email": false }, { "id": "660dc536-b5c7-eb11-bacc-000d3a5a1c19", "name": "Mattia Amadio", "role": { "code": "COLLABORATOR", "name": "Collaborator" }, "email": "mamadio@worldbank.org", "upi": "000542499", "externally_visible_email": false } ], "dates": [ { "id": "670dc536-b5c7-eb11-bacc-000d3a5a1c19", "date": "4/29/2021 9:52:45 PM", "type": "LAST_UPDATED_DATE" }, { "id": "680dc536-b5c7-eb11-bacc-000d3a5a1c19", "date": "7/1/2020 3:43:13 PM", "type": "CREATED_DATE" } ], "language_supported": [ { "id": "690dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "EN", "name": "English", "language_id": "fb3550b0-f4af-ea11-a812-000d3a32896d" } ], "citation": "Global Landslide Hazard Map, The World Bank" }, "keywords": [ { "id": "379a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "energydata.info" }, { "id": "309a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "global" }, { "id": "2e9a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "Hazards" }, { "id": "2c9a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "landslide" }, { "id": "329a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "mapping" }, { "id": "359a8689-b5c7-eb11-bacc-000d3a5a1a58", "name": "risk assessment" } ], "constraints": { "id": "6a0dc536-b5c7-eb11-bacc-000d3a5a1c19", "legal": { "access_constraints": null, "use_constraints": null, "other_constraints": null, "disclaimer": "The global hazard levels do not replace the need for detailed natural hazard risk analysis and/or expert advice at local scale. While the hazard level is scientifically determinef, there are still uncertainties in the data and analysis. Data users should access more information by contacting relevant national authorities, reviewing the recommended resources.\r\n\r\nThe information contained in this dataset is provided for informational purposes only and does not constitute legal or scientific advice or service. The World Bank makes no warranties or representations, express or implied as to the accuracy or reliability of this tool or the data contained therein. Any use thereof or reliance thereon is at the sole and independent discretion and responsibility of the user. No conclusions or inferences drawn from this data should be attributed to the World Bank group.", "copyright": null }, "security": { "classification": "Public", "exception": null, "user_note": null }, "license": { "license_id": "Creative Commons Attribution-Non Commercial 4.0", "custom_license_information": null, "license_reference": null } }, "data_quality": { "id": "6b0dc536-b5c7-eb11-bacc-000d3a5a1c19", "data_notes": "The dataset is publicly available for download and use and it consists of 4 global map layers:\r\n\r\n- Mean annual rainfall-triggered landslide hazard (1980 – 2018): raster values represent the modelled average annual frequency of significant rainfall-triggered landslides per sq. km.\r\n- Median annual rainfall-triggered landslide hazard (1980 – 2018): raster values represent the modelled median annual frequency of significant rainfall-triggered landslides per sq. km.\r\n- Mean annual earthquake-triggered landslide hazard: raster values represent the modelled average annual frequency of significant earthquake-triggered landslides per sq. km.\r\n- Aggregate hazard index ranging from 1 (low) to 4 (very high) implemented in Thinkhazard! website (www.thinkhazard.org)" }, "geographical_extent": { "id": "6c0dc536-b5c7-eb11-bacc-000d3a5a1c19", "description": null, "coverage": [ { "id": "6d0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "1W", "name": "World" }, { "id": "6e0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "4E", "name": "East Asia and Pacific" }, { "id": "6f0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AS", "name": "American Samoa" }, { "id": "700dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AU", "name": "Australia" }, { "id": "710dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BN", "name": "Brunei Darussalam" }, { "id": "720dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KH", "name": "Cambodia" }, { "id": "730dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CN", "name": "China" }, { "id": "740dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CK", "name": "Cook Islands" }, { "id": "750dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "FJ", "name": "Fiji" }, { "id": "760dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PF", "name": "French Polynesia" }, { "id": "770dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GU", "name": "Guam" }, { "id": "780dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "HK", "name": "Hong Kong SAR, China" }, { "id": "790dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "ID", "name": "Indonesia" }, { "id": "7a0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "JP", "name": "Japan" }, { "id": "7b0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KI", "name": "Kiribati" }, { "id": "7c0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KP", "name": "Korea, Democratic People's Republic of" }, { "id": "7d0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KR", "name": "Korea, Republic of" }, { "id": "7e0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LA", "name": "Lao People's Democratic Republic" }, { "id": "7f0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MO", "name": "Macao SAR, China" }, { "id": "800dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MY", "name": "Malaysia" }, { "id": "810dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MH", "name": "Marshall Islands" }, { "id": "820dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "FM", "name": "Micronesia, Federated States of" }, { "id": "830dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MN", "name": "Mongolia" }, { "id": "840dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MM", "name": "Myanmar" }, { "id": "850dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NR", "name": "Nauru" }, { "id": "860dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NC", "name": "New Caledonia" }, { "id": "870dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NZ", "name": "New Zealand" }, { "id": "880dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NU", "name": "Niue" }, { "id": "890dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MP", "name": "Northern Mariana Islands" }, { "id": "8a0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PW", "name": "Palau" }, { "id": "8b0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PG", "name": "Papua New Guinea" }, { "id": "8c0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PH", "name": "Philippines" }, { "id": "8d0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "WS", "name": "Samoa" }, { "id": "8e0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "SG", "name": "Singapore" }, { "id": "8f0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "SB", "name": "Solomon Islands" }, { "id": "900dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TW", "name": "Taiwan, China" }, { "id": "910dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TH", "name": "Thailand" }, { "id": "920dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TP", "name": "Timor-Leste" }, { "id": "930dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TO", "name": "Tonga" }, { "id": "940dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TV", "name": "Tuvalu" }, { "id": "950dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "VU", "name": "Vanuatu" }, { "id": "960dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "VN", "name": "Vietnam" }, { "id": "970dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "7E", "name": "Europe and Central Asia" }, { "id": "980dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AL", "name": "Albania" }, { "id": "990dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AD", "name": "Andorra" }, { "id": "9a0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AM", "name": "Armenia" }, { "id": "9b0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AT", "name": "Austria" }, { "id": "9c0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AZ", "name": "Azerbaijan" }, { "id": "9d0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BY", "name": "Belarus" }, { "id": "9e0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BE", "name": "Belgium" }, { "id": "9f0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BA", "name": "Bosnia and Herzegovina" }, { "id": "a00dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BG", "name": "Bulgaria" }, { "id": "a10dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "HR", "name": "Croatia" }, { "id": "a20dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CY", "name": "Cyprus" }, { "id": "a30dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CZ", "name": "Czech Republic" }, { "id": "a40dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "DK", "name": "Denmark" }, { "id": "a50dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "EN", "name": "Estonia" }, { "id": "a60dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "FO", "name": "Faroe Islands" }, { "id": "a70dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "FI", "name": "Finland" }, { "id": "a80dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "FR", "name": "France" }, { "id": "a90dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GE", "name": "Georgia" }, { "id": "aa0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "DE", "name": "Germany" }, { "id": "ab0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GI", "name": "Gibraltar" }, { "id": "ac0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GR", "name": "Greece" }, { "id": "ad0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GL", "name": "Greenland" }, { "id": "ae0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "HU", "name": "Hungary" }, { "id": "af0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "IS", "name": "Iceland" }, { "id": "b00dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "IE", "name": "Ireland" }, { "id": "b10dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "IM", "name": "Isle of Man" }, { "id": "b20dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "IT", "name": "Italy" }, { "id": "b30dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KZ", "name": "Kazakhstan" }, { "id": "b40dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "XK", "name": "Kosovo" }, { "id": "b50dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KG", "name": "Kyrgyz Republic" }, { "id": "b60dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LV", "name": "Latvia" }, { "id": "b70dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LI", "name": "Liechtenstein" }, { "id": "b80dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LT", "name": "Lithuania" }, { "id": "b90dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LU", "name": "Luxembourg" }, { "id": "ba0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MD", "name": "Moldova" }, { "id": "bb0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MC", "name": "Monaco" }, { "id": "bc0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "ME", "name": "Montenegro" }, { "id": "bd0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NL", "name": "Netherlands" }, { "id": "be0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MK", "name": "North Macedonia" }, { "id": "bf0dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NO", "name": "Norway" }, { "id": "c00dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PL", "name": "Poland" }, { "id": "c10dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PT", "name": "Portugal" }, { "id": "c20dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "RO", "name": "Romania" }, { "id": "c30dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "RU", "name": "Russian Federation" }, { "id": "c40dc536-b5c7-eb11-bacc-000d3a5a1c19", "code": "SM", "name": "San Marino" }, { "id": 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"140ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BM", "name": "Bermuda" }, { "id": "150ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CA", "name": "Canada" }, { "id": "160ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "US", "name": "United States" }, { "id": "170ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "8S", "name": "South Asia" }, { "id": "180ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AF", "name": "Afghanistan" }, { "id": "190ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BD", "name": "Bangladesh" }, { "id": "1a0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BT", "name": "Bhutan" }, { "id": "1b0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "IN", "name": "India" }, { "id": "1c0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "PK", "name": "Pakistan" }, { "id": "1d0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "NP", "name": "Nepal" }, { "id": "1e0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "MV", "name": "Maldives" }, { "id": "1f0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LK", "name": "Sri Lanka" }, { "id": "200ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "AO", "name": "Angola" }, { "id": "210ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BJ", "name": "Benin" }, { "id": "220ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BW", "name": "Botswana" }, { "id": "230ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BF", "name": "Burkina Faso" }, { "id": "240ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "BI", "name": "Burundi" }, { "id": "250ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CV", "name": "Cabo Verde" }, { "id": "260ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CM", "name": "Cameroon" }, { "id": "270ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CF", "name": "Central African Republic" }, { "id": "280ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "TD", "name": "Chad" }, { "id": "290ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KM", "name": "Comoros" }, { "id": "2a0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "ZR", "name": "Congo, Democratic Republic of" }, { "id": "2b0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "CG", "name": "Congo, Republic of" }, { "id": "2c0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "ET", "name": "Ethiopia" }, { "id": "2d0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "ER", "name": "Eritrea" }, { "id": "2e0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GQ", "name": "Equatorial Guinea" }, { "id": "2f0ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GA", "name": "Gabon" }, { "id": "300ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GM", "name": "Gambia, The" }, { "id": "310ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GH", "name": "Ghana" }, { "id": "320ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GN", "name": "Guinea" }, { "id": "330ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "GW", "name": "Guinea-Bissau" }, { "id": "340ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "KE", "name": "Kenya" }, { "id": "350ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LS", "name": "Lesotho" }, { "id": "360ec536-b5c7-eb11-bacc-000d3a5a1c19", "code": "LR", "name": 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"name": "Zimbabwe" } ], "geo_json": null, "bounding_box": { "west_longitude": null, "east_longitude": null, "north_latitude": null, "south_latitude": null }, "vertical_extent": { "min_value": null, "max_value": null, "vertical_CRS": null, "vertical_CRS_id": null } }, "temporal_extent": { "id": "4f0ec536-b5c7-eb11-bacc-000d3a5a1c19", "end_date": null, "start_date": null, "description": null, "duration": null, "coverage": null }, "temporal_resolution": { "periodicity": "Periodicity not specified" }, "spatial_resolution": { "id": "500ec536-b5c7-eb11-bacc-000d3a5a1c19", "granularity": null, "granularity_list": [ "Granularity not specified" ], "pixel_resolution": null }, "reference_system": { "id": "510ec536-b5c7-eb11-bacc-000d3a5a1c19", "reference_id": null, "version": "1", "map_projection": null }, "lineage": { "id": "520ec536-b5c7-eb11-bacc-000d3a5a1c19", "source_type": null, "source": null, "source_reference": null, "statistical_concept_and_methodology": "A statistical relationship was 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FYI @duncandewhurst @odscrachel - your current suggestions seem to align with this thread
As discussed in the last calls, we need to fix the schema to a "stable" starter version (called RDS 0.1), in order to add the showcase data homogeneously and to better understand gaps and issue of the schema to improve in the next versions.
The schema is simplified to a "core" implementation fitted to the data-out-DB approach (see GFDRR/rdl-data#36 ), and adapted in some parts to fit the JKAN structure in the schema file: https://github.com/GFDRR/rdl-jkan/blob/gh-pages/_data/schemas/rdl01.yml
This is the sheet I'm using to work on the schema. Changes to original schema (addition, rename, etc) in red: https://drive.google.com/file/d/1dYRv79i6tlabFFgPG_KncEekEoPUeDhv/view?usp=sharing (As a reference, here is the original version for SQL implementation from schema dev reports).
The major change compared to the initial schema is how the 4 components "speak" together; I've made an effort to match similar attributes and enums along schemas for better dataset linkage, clarity and homogeneity.
Before implementing it in the JKAN and related datasets metadata, it would be great to have a quick review together for green light.