Some very simple practices while releasing datasets as open data to help reusers making the most out of the data.

1. Euroalert.net:
Some very simple
practices to help with the
reuse of open datasets
Open Data Standards Day 2016
IODC16 Pre-event
Madrid, October 5th 2016

2. “Data is the new oil”

3. … but they forgot about ...
“data is not in the
surface, data is
dirty and very
difficult to extract”

4. 1. We make explorations
Search for data
deposits

5. 2. We extract data from documents
Documents are
deposits of raw
material.

6. 3. We create value at refinery
From raw data we
refine products
and services

7. How open data standards can help?
1. Mainly in step 2: Data extraction
2. But also in step 1: data discovery

8. Simple rules, frequently forgotten (I)
1. Records do not include a field with a unique identifier, which makes it very
difficult to monitor changes when the dataset is updated.
2. Records do not contain a field with the date when it was last updated,
which also complicates monitoring which records have changed from one
publication version to the next one.
3. Records do not contain a field with the date of creation, which makes it
difficult to know the date each one were incorporated to the dataset.
4. Fields do not use commonly agreed standards for the type of data they
contain. This often occurs in fields with dates and times, or economic values,
etc…but is also common in other fields.
5. The record is published on the dataset much later than on the website. This
can make a dataset useless for reuse if the service requires immediacy.

9. Simple rules, frequently forgotten (II)
5. Inconsistencies between the content of the dataset and its equivalent
published on HTML web pages. Inconsistencies can be of many types, from
records published on the website and not exported to the dataset to
differences in fields that are published in one format or the other.
6. Service Level Agreements on the publication of datasets are not
specified overtly. This is not to judge those agreements as good or bad;
what is really important is that they are known, as it is very hard to plan data
reuse ahead when you do not know what to expect.
7. These elements are not provided: a simple description about the content of
the fields and structure of the dataset, as well as the relevant criteria used to
analyze that content (lists of elements for factor variables, update criteria,
meaning of different states, etc.).

10. Some food for the discussion (I)
1. Do standardization organizations are
realistic when setting up the goals of new
open data standards?
2. Is the traditional approach for standards
valid for open data domain?

11. Some food for the discussion (II)
1. Do you know how many open data datasets
are published with no unique identifier or last
update date for each row?
2. If this is the baseline .... Is it realistic to think
about linked open data?
3. Why new open data standards do not have a
dissemination plan to ensure uptake?

12. An off-topic proposal to discuss
How far should a public administration go with regard to the
provision of value—added services based on open data?
“open government datasets should be released
in such condition that a reuser can build the
same services that are provided for free by the
data holder”
http://opendatacon.org/boundaries-public-administrations-establish-public-service-delivery-related-open-data/

13. Licence for the presentation
October 2016