metadata | Eviota

Metadata

Wed, 07 Jul 2021 00:00:00 +0000

Adding metadata exponentially increases the value of data. Did your region add a new town to its boundaries? How do you adjust old data to conform to constantly changing geographic boundaries? What are some practical ways of combining satellite sensory data with my organization’s records? And do I have the right to do so? Metadata logs the history of data, providing instructions on how to reuse it, also setting the terms of use. We automate this labor-intensive process applying the FAIR data concept.

In our observatory we apply the concept of FAIR (findable, accessibe, interoperable, and reusable digital assets) in our APIs and in our open-source statistical software packages.

The hidden cost item

Metadata gets less attention than data, because it is never acquired separately, it is not on the invoice, and therefore it remains an a hidden cost, and it is more important from a budgeting and a usability point of view than the data itself. Metadata is responsible for industry non-billable hours or uncredited working hours in academia. Poor data documentation, lack of reproducible processing and testing logs, inconsistent use of currencies, keywords, and storing messy data make reusability and interoperability, integration with other information impossible.

FAIR Data and the Added Value of Rich Metadata we introduce how we apply the concept of FAIR (findable, accessibe, interoperable, and reusable digital assets) in our APIs.

Organizations pay many times for the same, repeated work, because these boring tasks, which often comprise of tens of thousands of microtasks, are neglected. Our solution creates automatic documentation and metadata for your own historical internal data or for acquisitions from data vendors. We apply the more general Dublin Core and the more specific, mandatory and recommended values of DataCite for datasets – these are new requirements in EU-funded research from 2021. But they are just the minimal steps, and there is a lot more to do to create a diamond ring from an uncut gem.

Map your data: bibliographis, catalogues, codebooks, versioning

Updating descriptive metadata, such as bibliographic citation files, descriptions and sources to data files downloaded from the internet, versioning spreadsheet documents and presentations is usually a hated and often neglected task withing organization, and rightly so: these boring and error-prone tasks are best left to computers.

Already adjusted spreadsheets are re-adjusted and re-checked. Hours are spent on looking for the right document with the rigth version. Duplicates multiply. Already downloaded data is downloaded again, and miscategorized, again. Finding the data without map is a treasure hunt. Photo: © N.

The lack of time and resources spend on documentation over time reduces reusability and significantly increases data processing and supervision or auditing costs.

Our observatory metadata is compliant with the Dublin Core Cross-Domain Attribute Set metadata standard, but we use different formatting. We offer simple re-formatting from the richer DataCite to Dublin Core for interoperability with a wider set of data sources.
We use all mandatory DataCite metadata fields, all the the recommended and optional ones.
It complies with the tidy data principles.

In other words: very easy to import into your databases, or join with other databases, and the information is easy to find. Corrections, updates can automatically managed.

What happened with the data before?

We are creating Codebooks that are following the SDMX statistical metadata codelists, and resemble the SMDX concepts used by international statistical agencies. (See more technical information here.)

Small organizations often cannot afford to have data engineers and data scientists on staff, and they employ analysts who work with Excel, OpenOffice, PowerBI, SPSS or Stata. The problem with these applications is that they often require the user to manually adjust the data, with keyboard entries or mouse clicks. Furthermore, they do not provide a precise logging of the data processing, manipulation history. The manual data processing and manipulation is very error prone and makes the use of complex and high value resources, such as harmonized surveys or symmetric input-output tables, to name two important source we deal with, impossible to use. The use of these high-value data sources often requires tens of thousands of data processing steps: no human can do it faultlessly.

What is even more problematic that simple applications for analysis do not provide a log of these manipulations’ steps: pulling over a column with the mouse, renaming a row, adding a zero to an empty cell. This makes senior supervisory oversight and external audit very costly.

Our data comes with full history: all changes are visible, and we even open the code or algorithm that processed the raw data. Your analysts can still use their favourite spreadsheet or statistical software application, but they can start from a clean, tidy dataset, with all data wrangling, currency and unit conversion, imputation and other low-priority but important tasks done and logged.

Metadata

Tue, 01 Jun 2021 11:00:00 +0000

Our observatory has a new data API which allows access to our daily refreshing open data. You can access the API via api.economy.dataobservatory.eu (apologies for the ugly, temporary subdomain masking!).

All the data and the metadata are available as open data, without database use restrictions, under the ODbL license. However, the metadata contents are not finalized yet. We are currently working on a solution that applies the FAIR Guiding Principles for scientific data management and stewardship, and fulfills the mandatory requirements of the Dublic Core metadata standards and at the same time the mandatory requirements, and most of the recommended requirements of DataCite. These changes will be effective before 1 July 2021.

The Competition Data Observatory temporarily shares an API with the Economy Data Observatory, which serves as an incubator for similar economy-oriented reproducible research resources.

api.economy.dataobservatory.eu: processing metadata

Descriptive Metadata


Identifier	An unambiguous reference to the resource within a given context. (Dublin Core item), but several identifiders allowed, and we will use several of them.
Creator	The main researchers involved in producing the data, or the authors of the publication, in priority order. To supply multiple creators, repeat this property. (Extends the Dublin Core with multiple authors, and legal persons, and adds affiliation data.)
Title	A name given to the resource. Extends Dublin Core with alternative title, subtitle, translated Title, and other title(s).
Publisher	The name of the entity that holds, archives, publishes prints, distributes, releases, issues, or produces the resource. This property will be used to formulate the citation, so consider the prominence of the role. For software, use Publisher for the code repository. (Dublin Core item.)
Publication Year	The year when the data was or will be made publicly available.
Resource Type	We publish Datasets, Images, Report, and Data Papers. (Dublin Core item with controlled vocabulary.)

Recommended for discovery

The Recommended (R) properties are optional, but strongly recommended for interoperability.


Subject	The topic of the resource. (Dublin Core item.)
Contributor	The institution or person responsible for collecting, managing, distributing, or otherwise contributing to the development of the resource. (Extends the Dublin Core with multiple authors, and legal persons, and adds affiliation data.) When applicable, we add Distributor (of the datasets and images), Contact Person, Data Collector, Data Curator, Data Manager, Hosting Institution, Producer (for images), Project Manager, Researcher, Research Group, Rightsholder, Sponsor, Supervisor
Date	A point or period of time associated with an event in the lifecycle of the resource, besides the Dublin Core minimum we add Collected, Created, Issued, Updated, and if necessary, Withdrawn dates to our datasets.
Related Identifier	An identifier or identifiers other than the primary Identifier applied to the resource being registered.
Rights	We give SPDX License List standards rights description with URLs to the actual license. (Dublin Core item: Rights Management)
Description	Recommended for discovery.(Dublin Core item.)
GeoLocation	Similar to Dublin Core item Coverage

The Subject property: we need to set standard coding schemas for each observatory.
Contributor property:
- DataCurator the curator of the dataset, who sets the mandatory properties.
- DataManager the person who keeps the dataset up-to-date.
- ContactPerson the person who can be contacted for reuse requests or bug reports.
The Date property contains the following dates, which are set automatically by the dataobservatory R package:
- Updated when the dataset was updated;
- EarliestObservation, which the earliest, not backcasted, estimated or imputed observation.
- LatestObservation, which the earliest, not backcasted, estimated or imputed observation.
- UpdatedatSource, when the raw data source was last updated.
The GeoLocation is automatically created by the dataobservatory R package.
The Description property optional elements, and we adopted them as follows for the observatories:
- The Abstract is a short, textual description; we try to automate its creation as much as a possible, but some curatorial input is necessary.
- In the TechnicalInfo sub-field, we record automatically the utils::sessionInfo() for computational reproducability. This is automatically created by the dataobservatory R package.
- In the Other sub-field, we record the keywords for structuring the observatory.

Optional

The Optional (O) properties are optional and provide richer description. For findability they are not so important, but to create a web service, they are essential. In the mandatory and recommended fields, we are following other metadata standards and codelists, but in the optional fields we have to build up our own system for the observatories.


Language	A language of the resource. (Dublin Core item.)
Alternative Identifier	An identifier or identifiers other than the primary Identifier applied to the resource being registered.
Size	We give the CSV, downloadable dataset size in bytes.
Format	We give file format information. We mainly use CSV and JSON, and occasionally rds and SPSS types. (Dublin Core item.)
Version	The version number of the resource.
Rights	We give SPDX License List standards rights description with URLs to the actual license. (Dublin Core item: Rights Management)
Funding Reference	We provide the funding reference information when applicable. This is usually mandatory with public funds.
Related Item	We give information about our observatory partners’ related research products, awards, grants (also Dublin Core item as Relation.) We particularly include source information when the dataset is derived from another resource (which is a Dublin Core item.)

In the Language we only use English (eng) at the moment.
By default We do not use the Alternative Identifier property. We will do this when the same dataset will be used in several observatories.
The Size property is measured in bytes for the CSV representation of the dataset. During creations, the software creates a temporary CSV file to check if the dataset has no writing problems, and measures the dataset size.
The Version property needs further work. For a daily re-freshing API we need to find an applicable versioning system.
The Funding reference will contain information for donors, sponsors, and co-financing partners.
Our default setting for Rights is the CC-BY-NC-SA-4.0 license and we provide an URI for the license document.
In the RelatedItem we give information about:
- The original (raw) data source.
- Methodological bibilography reference, when needed.
- The open-source statistical software code that processed the data.

Administrative (Processing) Metadata

Administrative Metadata

Like with diamonds, it is better to know the history of a dataset, too. Our administrative metadata contains codelists that follow the SXDX statistical metadata standards, and similarly strucutred information about the processing history of the dataset.

See for further reference The codebook Class.


Observation Status	SDMX Code list for Observation Status 2.2 (CL_OBS_STATUS), such as actual, missing, imputed, etc. values.
Method	If the value is estimated, we provide modelling information.
Unit	We provide the measurement unit of the data (when applicable.)
Frequency	SDMX Code list for Frequency 2.1 (CL_FREQ) frequency values
Codelist	Euros-SDMX Codelist entries for the observational units, such as sex, etc.
Imputation	SDMX Code list for Frequency 2.1 (CL_IMPUT_METH) imputation values
Estimation	The estimation methodology of data that we calculated, together with citation information and URI to the actual processing code
Related Item	We give information about the software code that processed the data (both Dublin Core and DataCite compliant.)

See an example in the The codebook Class article of the dataobservatory R package.