#  1\. Introduction

This document is the initial version of the Data Management Plan (DMP). The
tool DMPOnline  1  has been used for writing this document. It has been
written with the specific template created for H2020 projects. In this final
version, four plans for different datasets have been written. These can be
found in the Annex of this document following the template _Final Review DMP_
.

In STARS4ALL, the following datasets have been identified and described:

  * **Photometer** . These correspond to the data generated by photometers (sensors). In STARS4ALL, a network of photometers will be deployed during the project. Citizens can expand this network in the future buying new photometers from our marketplace. These photometers will send data periodically to our system where they are stored. 

  

  * **NightKnights game** : In STARS4ALL, games with a purpose will be developed to acquire and process data from LPIs. These data will generate two datasets: i) one with the results of the tasks execution, and ii) another with the gamification process results like points, badges, etc. … The plan for the first dataset is equivalent to the plan generated for the toolbox (annex). The plan for the second dataset will be ready in the next version. 

  

  * **Cities At Night** : Cities at Night is a citizen science project that aims to create a map, similar to Google maps, of the Earth at night using night time colour photographs taken by astronauts onboard the ISS. Cities At Night is one of our initiatives and we manage the data management of the project. Two datasets are generated: i) tasks, with the definition of the task created, and ii) taskrun, with the results of the executions of these tasks 

  

  * **Community Health dataset** : STARS4ALL is a project oriented to create awareness among citizens, so for us, it is very important to measure the impact of CAs on the community. It goes without saying that this data must be open and shared with the community in order to engage newcomers and increase their participation. 


##  STARS4ALL - Photometers - Final review DMP

###  1\. Data summary

State the purpose of the data collection/generation

The purpose of the data collected is to monitor light pollution with the
measurements taken by our photometers (sky brightness sensors). You can find
more information here:  http://tess.stars4all.eu/.

These data will be used by scientists for research purposes. It can be used to
generate prediction models on sky brightness, to measure the impact of
diferent lighting policies, etc.

Explain the relation to the objectives of the project

One of the objectives in the project is to deploy a network of photometers to
monitor light pollution.

Specify the types and formats of data generated/collected

Each photometer generates a JSON object that is sent to our system and indexed
in our database. Periodic datasets in CSV are generated and published in our
data portal and Zenodo.

The data collected is the following:  
  
<table>  
<tr>  
<th>

Field ield Namea e Typeype Unitsnits

</th>  
<th>

Optionalptional Descriptionescription

</th> </tr>  
<tr>  
<td>

seq

</td>  
<td>

integer -

</td>  
<td>

Sequence number. If possible use 32 bits. The sequence number will start in 1

mandatory at each device reboot.

</td> </tr>  
<tr>  
<td>

name

</td>  
<td>

string

</td>  
<td>

\-

</td>  
<td>

mandatory Instrument friendly name. Should be unique as it identifies the
device

</td> </tr>  
<tr>  
<td>

freq

</td>  
<td>

float

</td>  
<td>

Hz

</td>  
<td>

Raw reading as a frequency with 3 decimal digits precission (milihertz)

mandatory

NNNNN.NNN

</td> </tr>  
<tr>  
<td>

mag

</td>  
<td>

float

</td>  
<td>

</td>  
<td>

Visual magnitude (formulae?) corresponding to the raw reading). Transmitted

</td> </tr>  
<tr>  
<td>

mag/arcsec^2 mandatory up to two decimal places NN.NN

</td> </tr>  
<tr>  
<td>

tamb

</td>  
<td>

float

</td>  
<td>

ºC madatory Ambient Temperature. Transmitted up to one decimal place

</td> </tr>  
<tr>  
<td>

tsky

</td>  
<td>

float

</td>  
<td>

Sky Temperature. Transmitted up to one decimal place az integer degrees

optional photometer optical axis Azimuth sent only on instruments with

ºC mandatory

accelerometer alt integer degrees optional photometer optical axis Altitude

(angle): sent only on instruments with accelerometer

</td> </tr>  
<tr>  
<td>

rev

</td>  
<td>

integer - mandatory Payload data format revision number. Current

</td> </tr> </table>

Specify if existing data is being re-used (if any)

We are not aware of any reuse of this data for other purposes, for the moment.

Specify the origin of the data

Data generated in our photometer network are being collected from our network
of sensors located around the world. The upto-date map is available here:
http://dashboards.stars4all.eu/tess-chart/tess-locations.html

State the expected size of the data (if known)

A photometer network with 45 devices generates 134MB of data each month. In
the short-term, we expected to duplicate this number, reaching 200 devices in
the middle-term.

Outline the data utility: to whom will it be useful

The data generated by our photometers have a clear application in the light
pollution research field. We have data collected since 2017 in an operation
model of 24/7/365. This will provide a invaluable source of information to
understand the behaviour of the light pollution in a specific area. The
effects of light pollution in biodiversity, human health and energy wasted are
well documented in scientific literature.

####  2.1 Making data findable, including provisions for metadata [FAIR data]

Outline the discoverability of data (metadata provision)

All data have been uploaded and indexed in our data portal
(http://data.stars4all.eu) adding the metadata defined by CKAN.

This includes:

  1. Title 

  

  2. Description 

  

  3. Tags 

  

  4. License 

  

  5. Visibility 

  

  6. Source 

  

  7. Version 

  

  8. Author / Author email 

  

  9. Maintainer /email 

  

  

The datasets can be found using any of the metadata defined previously from a
search field. They are also accesible in our data portal/zenodo, and re-usable
since they are published with the CC-BY 4.0 license.

The fields defined inside the datasets are described in the description field
of the dataset.

Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?

Photometer datasets are uploaded to our portal and to Zenodo (in our community
https://zenodo.org/communities/stars4all  ). Zenodo is used by our project to
generate DOIs and to archive (make persistent) our datasets. This also allows
indexing our data in OpenAire.

Outline naming conventions used

For naming, the following convention has been used:

Month-Year

Outline the approach towards search keyword

Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.

Outline the approach for clear versioning

Both Zenodo and our data portal have a versioning system included in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.

Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how

There is no entry in the Metadata Standards Directory for the datasets
generated in our project.

Nevertheless, night sky brightness data is archived using the "NSBM Community
Standards for Reporting Skyglow

Observations", which was officially adopted at the 12th European Symposium for
the Protection of the Night Sky and endorsed by the International Dark Sky
Association (IDA) and by the International Astronomical Union (IAU) in Beijing
2012.

####  2.2 Making data openly accessible [FAIR data]

Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so

All data generated is made openly available.

Specify how the data will be made available

Data is available from our data portal (
http://ckan.stars4all.eu/dataset/tess-monthly-measurements  ) and from our
Zenodo

community (

http://www.zenodo.org/communities/stars4al

l

)

Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?

You only need a browser to access to all data. For more documentation, access
the STARS4ALL website (http://www.stars4all.eu), Data portal section.

You can find more information here:

Data portal deliverable:
https://figshare.com/articles/D5_4_STARS4ALL_data_portal/4212555

Sensor API:
https://figshare.com/articles/D4_9_Sensor_Management_API_final_/4508690

Specify where the data and associated metadata, documentation and code are
deposited

The data are deposited in:

Data portal (including metadata) -> _http://ckan.stars4all.eu/dataset/tess-
monthly-measurements_

Zenodo Community -> _http://www.zenodo.org/communities/stars4all_

The interpretation of each measure, as well as the technical components of the
device, are open and available to general public in the following link:
_https://drive.google.com/file/d/0Bw_qv1ze9sY2Xy1BYk1Fcmx1X1k/view?usp=sharing_

Software is available on Github (  _https://github.com/STARS4ALL/photometer-
api_ ) and the API properly documented in Apiary:
_http://docs.photometer.apiary.io_

Specify how access will be provided in case there are any restrictions

You can use the contact email for data issues of our project:
data@stars4all.eu

####  2.3 Making data interoperable [FAIR data]

Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.

To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.

Data use the standard "NSBM Community Standards for Reporting Skyglow
Observations".

Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?

Data uses the "NSBM Community Standards for Reporting Skyglow Observations"
standard.

####  2.4 Increase data re-use (through clarifying licenses) [FAIR data]

Specify how the data will be licenced to permit the widest reuse possible

Data is licensed with CC-BY 4.0 that allows to:

Sharehare — copy and redistribute the material in any medium or format

Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.

Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed

Data is available in real time through the APi and dashboards, and monthly, in
a dataset format through Zenodo and our data portal.

Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why

Data will be used by light pollution researchers to study the impact of the
light pollution in the environment and to be used in scientific publications.
Plus, it could be used for environmental studies or commercial activities sush
as measure the quality of the sky in rural hotels environments.

There is not restriction in the use of the data. We only request to cite us
using our DOI.

Describe data quality assurance processes

The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.

Specify the length of time for which the data will remain re-usable

Data will be continuously archived while the Zenodo platform remains open.

###  3\. Allocation of resources

Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs

The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.

Clearly identify responsibilities for data management in your project

The data management process is managed by UPM (Universidad Politécnica de
Madrid).

Describe costs and potential value of long term preservation

We use Zenodo for long-term preservation. With these datasets, scientists will
be able to measure the evolution of the sky quality throughout the years and
to study the impact different outdoor lighting policies.

###  4\. Data security

Address data recovery as well as secure storage and transfer of sensitive data

These kind of data do not have security restrictions because they do not
provide sensitive values. Also, these data do not contain personal information
so there is not need to apply protection measurements.

Data, with the exception of datasets in Zenodo, are hosted in UPM's servers.
Only authorised personnel can access the location where these servers are
hosted and periodical backups are being done.

###  5\. Ethical aspects

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

These datasets do not contain personal information so there is not need to
apply data protection measurements and to request permission to photometers'
owners to publish these data.

###  6\. Other

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.

##  STARS4ALL - NightKnights - Final review DMP

###  1\. Data summary

State the purpose of the data collection/generation

Night Knights (  https://www.nightknights.eu/  ) is a Game With A Purpose
(GWAP) aimed at Data Linking; more specifically, by playing the game, players
help in classifying images taken from the International Space Station (ISS)
with respect to a given taxonomy (hence, links are created between images and
categories). In short, all actions by all players are weighted and aggregated
by the game, producing a “crowdsourced” classification of ISS pictures that
can be used for subsequent analysis of the light pollution phenomenon. Details
on the Night Knights application and its internal functioning are given in
deliverables D4.4, D4.6 and D4.10.

The data set consists of the output data of the Night Knights game. Data are
stored in a MySQL database securely accessible by means of a dedicated API, as
described in the following.

Explain the relation to the objectives of the project

One of the objectives of STARS4ALL is to implement gamification mechanisms to
increase citizen participation (O4.1 -> WP4). Another STARS4ALL objective is
to give support to LPIs (Light Pollution Initiatives) in data management (O4.4
-> WP4). For that reason, STARS4ALL has deployed a data portal for backing
them.

Specify the types and formats of data generated/collected

The format of the dataset was defined in a way to facilitate its subsequent
reuse, i.e. the game output contains all information needed for further
processing and evaluation of the data.

Data collected is divided in two categories: donetasks and results.

In the first case, the dataset contains the individual tasks executed by
users.  
  
<table>  
<tr>  
<th>

Field Nameield a e

</th>  
<th>

Typeype

</th>  
<th>

Unitsnits Optionalptional Descriptionescription

</th> </tr>  
<tr>  
<td>

idUser

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the identifier of the user

</td> </tr>  
<tr>  
<td>

taskId

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the internal id of the task

</td> </tr>  
<tr>  
<td>

smallPhotoUrl

</td>  
<td>

url

</td>  
<td>

\-

</td>  
<td>

mandatory It is the url of the photo used to be classified

</td> </tr>  
<tr>  
<td>

timestamp

</td>  
<td>

datetime

</td>  
<td>

\-

</td>  
<td>

mandatory It represents the execution time

</td> </tr>  
<tr>  
<td>

choosenCategory enumerate -

</td>  
<td>

It is the category choosen by the user. It can be: black, city, stars, aurora,

mandatory astronaut or none

</td> </tr>  
<tr>  
<td>

groundTruth boolean -

</td>  
<td>

mandatory It represents if the task is a golden task

It represents if the user has been logged in the system or is an anonymous

</td> </tr>  
<tr>  
<td>

guest boolean -

</td>  
<td>

mandatory

</td> </tr> </table>

user.

In the second case, the dataset contains the results obtained in NightKnights.

Field Nameield a e Typeype Unitsnits Optionalptional Descriptionescription  
  
<table>  
<tr>  
<th>

resultId integer

</th>  
<th>

\-

</th>  
<th>

mandatory It is the identifier of the result

</th> </tr>  
<tr>  
<td>

taskId integer

</td>  
<td>

\-

</td>  
<td>

It is the same as the submitted task (previous dataset), so to allow for

mandatory identification.

</td> </tr>  
<tr>  
<td>

smallPhotoUrl url

</td>  
<td>

\-

</td>  
<td>

mandatory It is the url of the photo used to be classified

</td> </tr>  
<tr>  
<td>

class enumerate -

</td>  
<td>

It is the category choosen by the user. It can be: black, city, stars, aurora,

mandatory astronaut or none

</td> </tr>  
<tr>  
<td>

numPlayers integer -

</td>  
<td>

mandatory Number of players that were actually needed to classify the photo

The timestamp of the result (moment in which the classification was cross-

</td> </tr>  
<tr>  
<td>

solutionDate datetime -

</td>  
<td>

mandatory

</td> </tr> </table>

validated and the photo removed from the game)

Specify if existing data is being re-used (if any)

Data generated is used in the project Cities At Night, one of the STARS4ALL
light pollution initiatives. Some examples can be found at
http://citiesatnight.org/index.php/related-projects/  .

Data is being used as a training dataset in machine learning algorithms, as
can be consulted in the following paper:

Gloria Re Calegari, Gioele Nasi and Irene Celino: "Human Computation vs.
Machine Learning: an Experimental Comparisonu an o putation vs. achine
earning: an xperi ental o parison  for Image Classificatiofor I  age
lassificationn", Human Computation Journal, vol. 5, issue 1, pp. 13-30, DOI:
10.15346/hc.v5i1.2, 2018. [gold open access]

Specify the origin of the data

The starting point is the NASA database, with almost half a million pictures
taken by the astronauts on the International Space Station.

These images are classified by users using the application NightKnights (
http://www.nightknights.eu  ).

The dataset is generated using the NightKnights API, which is described here:
https://crowdtaskmanagement.docs.apiary.io/  Methods used:

Night Knights Game Results Collection

Night Knights Game Done Tasks Collection

State the expected size of the data (if known)

The size of the data generated depends of the number of executions and the
users who play with the game. The average per year is about 10MB, so a big
volume of data is not expected in the future.

Outline the data utility: to whom will it be useful

Data is used for:

Calculating community health indicators of the project Cities At Night. See
http://dashboards.stars4all.eu/social/

Research in the Human Computation field. Some examples are

Gloria Re Calegari, Gioele Nasi and Irene Celino: "Human Computation vs.
Machine Learning: anu an o putation vs. achine earning: an

Experimental Comparison for Image Classificationxperi ental o parison for I
age lassification", Human Computation Journal, vol. 5, issue 1, pp. 13-30,

DOI: 10.15346/hc.v5i1.2, 2018.  [gold open access  ]

Gloria Re Calegari and Irene Celino: "Interplay of Game Incentives, Player
Profiles and Task Difficulty inInterplay of a e Incentives, layer rofiles and
ask ifficulty in

Games with a Purposea es ith a urpose", in proceedings of the 21th
International Conference on Knowledge Engineering and

Knowledge Management - EKAW 2018, LNAI Volume 11313, pp. 306-321, DOI:
10.1007/978-3-030-03667-

6_20, 2018.  [green open access (eprint) at arxiv  ]

Gloria Re Calegari, Andrea Fiano and Irene Celino: "A Framework to build Games
with a Purpose for Linked ra e ork to build a es ith a urpose for inked

Data Refinementata efine ent", in proceedings of the International Semantic
Web Conference 2018, Best Paper Award  Resources Track, LNCS Volume 11137, pp.
154-169, DOI: 10.1007/978-3-030-00668-6_10, 2018. [green open access (eprint)
at arxiv]

####  2.1 Making data findable, including provisions for metadata [FAIR data]

Outline the discoverability of data (metadata provision)

As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:

  1. Title 

  

  2. Description 

  

  3. Tags 

  

  4. License 

  

  5. Visibility 

  

  6. Source 

  

  7. Version 

  

  8. Author / Author email 

  

  9. Maintainer /email 

  

  

The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.

The fields defined inside of the datasets are described in the description
field of the dataset.

Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?

NightKnights results are uploaded to our portal and Zenodo (
https://zenodo.org/communities/stars4all  ). Zenodo is used to generate DOIs
and to archive (make persistent) our datasets. Also, this allows indexing our
data in OpenAire.

Outline naming conventions used

For naming donetasks datasets, the following convention has been used:
results-nightknights-year

For naming results datasets, the following convention has been used:
donetasks-nightknights-year

Outline the approach towards search keyword

Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.

Outline the approach for clear versioning

Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.

Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how

The format of the dataset was defined in a way to facilitate its subsequent
reuse, i.e. the game output contains all information needed for further
processing and evaluation of the data.

Night Knights data gives information about three main themes:

Classification results (classified photo, assigned classification, number of
players needed to come to a classification agreement)

Players’ actions (“log” of all classification actions done by anonymized game
users)

Game evaluation data (including KPIs like number of tasks
available/started/completed, number of players, total played time, throughput,
average life play, etc.)

####  2.2 Making data openly accessible [FAIR data]

Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so

All data generated have been made openly available except some personal data
such as the identifier of the user in Social Networks. That is because the
user can play in the game logging in with its twitter, facebook or google+
accounts. This personal data is closed to respect the personal data law.

Specify how the data will be made available

Data is available from our data portal. There are two different datasets: i)
one for the results , ii) one for the individual tasks. In both cases, there
are different resources (files) per year.

Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?

Only a browser is needed to access to the data. Plus, the API of the data
portal can be used. For more information, check:
https://docs.ckan.org/en/2.7/api/index.html

Specify where the data and associated metadata, documentation and code are
deposited

The data are deposited in our:

Data portal, inside of each project. (  http://data.stars4all.eu  )

Zenodo Community -> http://www.zenodo.org/communities/stars4all

The source code can be found here:

https://github.com/STARS4ALL/gwap-enabler

You can find tutorials here:

https://github.com/STARS4ALL/gwap-enabler-tutorial

_https://crowdtaskmanagement.docs.apiary.io_

Specify how access will be provided in case there are any restrictions

You can use the contact email for data issues of our project:
data@stars4all.eu

####  2.3 Making data interoperable [FAIR data]

Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.

To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.

Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?

Vocabulary is described in the deliverables:

D4.10 Games Release (final version)

D5.7 : STARS4ALL tools for crowdsourcing activities (initial release)

D5.8 : STARS4ALL tools for crowdsourcing activities (final release)

####  2.4 Increase data re-use (through clarifying licenses) [FAIR data]

Specify how the data will be licenced to permit the widest reuse possible

Data is licensed with CC-BY 4.0 that allows to:

Sharehare — copy and redistribute the material in any medium or format

Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.

Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed

There is no embargo period. Dataset are published periodically.

Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why

These data can be used as a complement of the work done in the Cities At Night
project and the application Dark Skies ISS

( _https://crowdcrafting.org/project/darkskies/_ )

Describe data quality assurance processes

The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.

Specify the length of time for which the data will remain re-usable

Data will be archived while the Zenodo platform remains open.

###  3\. Allocation of resources

Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs

The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.

Clearly identify responsibilities for data management in your project

The data management process is managed by UPM (Universidad Politécnica de
Madrid).

Describe costs and potential value of long term preservation

We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the light pollution using images taken by
astronauts of the ISS (International Space Station)

###  4\. Data security

Address data recovery as well as secure storage and transfer of sensitive data

There is only one field in the dataset that may be considered as personal
data. This field, idUser, contains the id of the user who executes the action.
Nevertheless, this field is an internal identificator (it does not contain the
user name) and the full name or another personal information cannot be traced
from this id.

Data of the data portal are hosted in UPM's servers. Only authorised personnel
can access the location where these servers are hosted and periodical backups
are being done.

###  5\. Ethical aspects

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

These datasets do not contain personal information so there is not need to
apply data protection measurements.

The only personal information stored in our servers in the social identifier
(facebook, twitter or google+) of the users. This information is not available
in the datasets (but a user id is present in the dataset after executing an
anonymising process).

According with the Data Protection Spanish National Law, a consent is
requested to the user to store this information.

See  _https://www.nightknights.eu/#/privacy_

###  6\. Other

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any)

No other issues reported.

##  STARS4ALL - Cities At Night - Final review DMP

###  1\. Data summary

State the purpose of the data collection/generation

Cities at Night is a citizen science project that aims to create a map,
similar to Google maps, of the Earth at night using night time colour
photographs taken by astronauts onboard the ISS.

NASA has a database with almost half a million pictures taken by the
astronauts on the International Space Station.To organise all this data with
the use of computers would be infeasible since it would take extremely complex
algorithms to interpret the photographs. The human eye, however, knows
immediately if the camera was pointing at a city or simply at the stars.

For this reason we created Cities at Night, a platform with 3 apps with which
anyone can help while enjoying beautiful pictures taken from space.

Explain the relation to the objectives of the project

One of the objectives of STARS4ALL is to give support to LPIs (Light Pollution
Initiatives) in data management (O4.4). For that reason, STARS4ALL has
deployed a data portal for backing them.

Specify the types and formats of data generated/collected

The data collected is divided in two categories: task and taskrun. tasktask
defines the tasks created to be executed by users in the Dark Skies
application.  
  
<table>  
<tr>  
<th>

Field Nameield a e

</th>  
<th>

Typeype

</th>  
<th>

Unitsnits Optionalptional Descriptionescription

</th> </tr>  
<tr>  
<td>

task_calibration

</td>  
<td>

boolean

</td>  
<td>

\-

</td>  
<td>

mandatory Define if is a golden task

</td> </tr>  
<tr>  
<td>

task_created

</td>  
<td>

datetime

</td>  
<td>

\-

</td>  
<td>

mandatory Define when the task was created

</td> </tr>  
<tr>  
<td>

task_id

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the identifier of the task

</td> </tr>  
<tr>  
<td>

task_info

</td>  
<td>

json object

</td>  
<td>

\-

</td>  
<td>

It is a json object with the input source (video, image, sound, etc ...) of
the

task

mandatory

</td> </tr>  
<tr>  
<td>

task_n_answer

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the number of answers necessary to complete the task

</td> </tr>  
<tr>  
<td>

task__priority_0

</td>  
<td>

float

</td>  
<td>

\-

</td>  
<td>

mandatory Priority of the task

</td> </tr>  
<tr>  
<td>

task_project_id

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the identifier of the project associated to the task

</td> </tr>  
<tr>  
<td>

task_quorum

</td>  
<td>

integer

</td>  
<td>

\-

</td>  
<td>

mandatory It is the number of different users necessary to accept the task

</td> </tr>  
<tr>  
<td>

task_state

</td>  
<td>

enumerate -

</td>  
<td>

mandatory Define the state of the task (COMPLETED)

</td> </tr>  
<tr>  
<td>

task_info_idiss

</td>  
<td>

string -

</td>  
<td>

Define the id of the task in string format. In our case, the name of the

mandatory image that we have to classify

</td> </tr>  
<tr>  
<td>

task_info_link

</td>  
<td>

url -

</td>  
<td>

mandatory Define the place where user can find the image.

</td> </tr>  
<tr>  
<td>

task_info_link_big

</td>  
<td>

url -

</td>  
<td>

mandatory Define the url of the image in high resolution

</td> </tr>  
<tr>  
<td>

task_info_link_small url -

</td>  
<td>

mandatory Define the url of the image to be shown to the user (small
resolution)

</td> </tr> </table>

taskruntaskrun defines the executions of an specific task.  
  
<table>  
<tr>  
<th>

Field Nameield a e

</th>  
<th>

Typeype Unitsnits Optionalptional Descriptionescription

</th> </tr>  
<tr>  
<td>

task_run__calibration

</td>  
<td>

boolean -

</td>  
<td>

mandatory Define if the origin is a golden task

</td> </tr>  
<tr>  
<td>

task_run__created

</td>  
<td>

datetime -

</td>  
<td>

mandatory Define when the task was shown to the user to be executed.

</td> </tr>  
<tr>  
<td>

task_run__external_uid

</td>  
<td>

string -

</td>  
<td>

Define if the task was executed with an external application (using

mandatory the API)

</td> </tr>  
<tr>  
<td>

task_run__finish_time

</td>  
<td>

datetime -

</td>  
<td>

mandatory Define when the task was executed.

</td> </tr>  
<tr>  
<td>

task_run__id

</td>  
<td>

integer -

</td>  
<td>

mandatory It is the identifier of the object

</td> </tr>  
<tr>  
<td>

task_run__info

</td>  
<td>

json object -

</td>  
<td>

mandatory It is a json object with the response (result) of the user.

</td> </tr>  
<tr>  
<td>

task_run__project_id

</td>  
<td>

integer -

</td>  
<td>

mandatory It is the identifier of the project associated to the task

</td> </tr>  
<tr>  
<td>

task_run__task_id

</td>  
<td>

integer -

</td>  
<td>

mandatory It is the identifier of the task (see previous table)

</td> </tr>  
<tr>  
<td>

task_run__timeout

</td>  
<td>

boolean -

</td>  
<td>

mandatory Define if the user solved the task in the time assigned to it.

</td> </tr>  
<tr>  
<td>

task_run__user_id

</td>  
<td>

integer -

</td>  
<td>

mandatory It is the id of the user

</td> </tr>  
<tr>  
<td>

task_runinfo__classification enumerate -

</td>  
<td>

It is the result of the classification (AURORA, CITY, BLACK,

mandatory

ISS)

</td> </tr>  
<tr>  
<td>

task_runinfo__cloudy enumerate -

</td>  
<td>

It provides more information about the quality of ther image

mandatory shown. In our case if the level of clouds in the image

</td> </tr>  
<tr>  
<td>

task_runinfo__img_big url -

</td>  
<td>

mandatory Image in high resolution classified

</td> </tr>  
<tr>  
<td>

task_runinfo__img_smal url -

</td>  
<td>

mandatory Image in small resolution classified

</td> </tr> </table>

Specify if existing data is being re-used (if any)

Data generated in these crowdsourcing activities and present in this dataset
is being used in many research papers and projects such as:

EVALUATING THE ASSOCIATION BETWEEN ARTIFICIAL LIGHT-AT-NIGHT EXPOSURE AND
BREAST

AND PROSTATE CANCER RISK IN SPAIN. (
http://citiesatnight.org/index.php/portfolio-view/6799-2/  )

ARTIFICIALLY LIT SURFACE OF EARTH AT NIGHT INCREASING IN RADIANCE AND

EXTENT.  (http://citiesatnight.org/index.php/portfolio-view/alan-increasing/
)

Artificial light at night and cognitive development and performance in
children, Juana Maria Delgado-Saborit,

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e

s

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n

s

e

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n

i

n

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i

d

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,

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a

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i

e

r

r

e

,

I

D

A

,

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l

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r

i

d

a

,

U

S

A

ISGlobal (Spain) and King’s College London (UK)

EMISSI@N, Kevin G. Gaston, Environmental and Sustainability Institute of
University of Exeter, Cornwall, United

Kingdom

More information can be found in:

http://citiesatnight.org/index.php/related-projects/

http://citiesatnight.org/index.php/science-research

/

Specify the origin of the data

The starting point is the NASA database with almost half a million pictures
taken by the astronauts on the International Space Station.

These images are classified by users with the project Dark Skies (
https://crowdcrafting.org/project/darkskies/  ) that it is running in the
platform Crowdcrafting.

State the expected size of the data (if known)

According to the estimation done based on previous datasets, the size of
storing the execution of 193090 tasks takes up 625MB. These tasks comprise a
period of one and a half year.

Outline the data utility: to whom will it be useful

As it has been described in section "data re-used section", these data is
relevant for light pollution researchers because they can be used to estimate
the light pollution in cities as well as the colour of them (ambar, blue,
white, etc.).

####  2.1 Making data findable, including provisions for metadata [FAIR data]

Outline the discoverability of data (metadata provision)

As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:

  1. Title 

  

  2. Description 

  

  3. Tags 

  

  4. License 

  

  5. Visibility 

  

  6. Source 

  

  7. Version 

  

  8. Author / Author email 

  

  9. Maintainer /email 

  

  

The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.

The fields defined inside of the datasets are described in the description
field of the dataset.

Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?

Dark Skies ISS tasks and taks runs are uploaded to our portal and Zenodo(
https://zenodo.org/communities/stars4all  ). That is because, Zenodo is used
by our project to generate DOIs and to archive (make persistent) our datasets.
Also, this allows indexing our data in OpenAire.

Outline naming conventions used

For naming task datasets, the following convention has been used:
CitiesAtNight-Tasks

For naming task_run datasets, the following convention has been used:
CitiesAtNight-Tasks_run_(Year)

Outline the approach towards search keyword

Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.

Outline the approach for clear versioning

Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.

Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how

There is no entry in the Metadata Standards Directory for the datasets
generated in our project.

There is no standard used for crowdsourcing activities. Nevertheless, we use
the data model of PyBossa

( _http://docs.pybossa.com/en/latest/model.html_ ). Following the PyBossa
model, data will be stored in a database system (Postgres) as JSON objects.

Depending on their nature, projects in PyBossa belong to two different
categories: thinking or sensing. The thinking category is used in projects
where users solve problems, such as tagging images. The sensing category is
used for projects where users gather data. The generated metadata are stored
as a result of a task (taskrun). Once a quorom is reached, the value is
incorporated to the resource.

####  2.2 Making data openly accessible [FAIR data]

Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so

All data generated have been made openly available.

Specify how the data will be made available

Data is available from our data portal. There are two different datasets: i)
one for tasks , ii) one for the results. In the case of the results, there are
different resources (files) per year.

Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?

Only a browser is needed to access to the data. Plus, the API of the data
portal can be used. For more information, check:
_https://docs.ckan.org/en/2.7/api/index.html_

Specify where the data and associated metadata, documentation and code are
deposited

The data are deposited in our:

Data portal, inside of each project. (  _http://data.stars4all.eu_ )

Zenodo Community -> _http://www.zenodo.org/communities/stars4all_

Specify how access will be provided in case there are any restrictions

You can use the contact email for data issues of our project:
data@stars4all.eu

####  2.3 Making data interoperable [FAIR data]

Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.

To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.

Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?

There is no standard used for crowdsourcing activities. Nevertheless, we use
the data model of PyBossa  ( _http://docs.pybossa.com/en/latest/model.html_ ).

####  2.4 Increase data re-use (through clarifying licenses) [FAIR data]

Specify how the data will be licenced to permit the widest reuse possible

Data is licensed with Creative Commons License. Cities at Night by Alejandro
Sánchez de Miguel et al. Atlas of astronaut photos of Earth at night A&G
(2014) is distributed under Creative Commons Non commercial attribution –
Share 4.0

International licence.  _https://doi.org/10.1093/astrogeo/atu165_ However, if
the Cities at Night data are a principal component of a science paper then co-
authorship to PI's should be offered.

Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed

There is no embargo period. Dataset is published periodically.

Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why

As it has been mentioned in previous sections, these data is very important
for light pollution research. Some examples can be found in:
_http://citiesatnight.org/index.php/related-projects/_

Describe data quality assurance processes

The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.

Specify the length of time for which the data will remain re-usable

Data will be archived while Zenodo platform remains open.

###  3\. Allocation of resources

Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs

The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.

Clearly identify responsibilities for data management in your project

The data management process is managed by UPM (Universidad Politécnica de
Madrid).

Describe costs and potential value of long term preservation

We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the light pollution using images taken by
astronauts of the ISS (International Space Station)

###  4\. Data security

Address data recovery as well as secure storage and transfer of sensitive data

There is only one field in the dataset that may be due to security
restrictions because it may be considered as personal data. This field,
task_run_user_id, contains the id of the user who executes the action.
Nevertheless, this field is an internal identificator (not contains the name
of the user) and the full name or another personal information can not be
traced from this id.

Data of the data portal are hosted in UPM's servers. Only authorised personnel
can access the location where these servers are hosted, and periodical backups
are being done.

All the generated data in the platform Crowdcrafting by the volunteers are,
unless otherwise stated, under the _Open Data_

_Commons License_ . Check the description the project's page for further
details about which license is going to be applied  .

###  5\. Ethical aspects

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

These datasets do not contain personal information so there is not need to
apply data protection measurements.

###  6\. Other

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.

##  STARS4ALL - Community Health - Final review DMP

###  1\. Data summary

State the purpose of the data collection/generation

In order to carry out the community health analysis in WP3, we use data from
the various LPIs to calculate a set of metrics as set out in D3.4. We are
primarily concerned with analysing the number of classifications or tasks
carried out, how these are distributed among the community, and how many
people in total are participating. Since we cover a range of project types in
the LPIs, we for now focus on a set of data that is particular to the
classification-based projects, however much of this can be easily adapted to
other types of project, too.

Explain the relation to the objectives of the project

One of the objectives on WP3 is to analyse effectivity of community awareness
activities, in our case, LPIs. Community engagement needs to be continuously
scrutinized and monitored and relevant activities and strategies need to be
adjusted accordingly.

The aim here is to set up the analysis tools that will allow us to take
informed and well-balanced decisions regarding our activities.

Specify the types and formats of data generated/collected

Contribution data are exported from individual projects, such as Dark Skies,
Globe at Night etc, and include the number of contributes over time, the
number of users who are contributing, and task metrics.  
  
<table>  
<tr>  
<th>

Field ield Namea e Typeype Unitsnits Optionalptional Descriptionescription

</th> </tr>  
<tr>  
<td>

day datetime -

</td>  
<td>

mandatory Analyzed day

</td> </tr>  
<tr>  
<td>

rows integer -

</td>  
<td>

mandatory Number of entries processed

</td> </tr>  
<tr>  
<td>

contributions integer -

</td>  
<td>

mandatory Number of contributions done

</td> </tr>  
<tr>  
<td>

users integer -

</td>  
<td>

mandatory Number of users have made a contribution

</td> </tr>  
<tr>  
<td>

starters integer -

</td>  
<td>

mandatory Number of starters

</td> </tr> </table>

Specify if existing data is being re-used (if any)

Not for the moment.

Specify the origin of the data

These data are generated by several scripts that periodically run in our
servers. The source code is here:  https://github.com/STARS4ALL/STARS4ALL-
social-dashboard

State the expected size of the data (if known)

The total amount of data collected in these datasets is 8.7MB (with data
collected from two years ago). These data will depend of the amount of
contributions but we do not expect more than 10MB of data per year.

Outline the data utility: to whom will it be useful

The target of this data is to the initiative/project coordinator. With this
dataset, the coordinator will be able to measure the health of the community
behind the project.

Also, these datasets can be valuable for researchers who working with online
communities.

####  2.1 Making data findable, including provisions for metadata [FAIR data]

Outline the discoverability of data (metadata provision)

As it happens with the rest of the data, all data generated in this project
have been uploaded and indexed in our data portal (http://data.stars4all.eu)
adding the metadata defined by CKAN. This includes:

  1. Title 

  

  2. Description 

  

  3. Tags 

  

  4. License 

  

  5. Visibility 

  

  6. Source 

  

  7. Version 

  

  8. Author / Author email 

  

  9. Maintainer /email 

  

  

The datasets of our project can be found using any of the metadata defined
previously from a search field. Also, they are accesible in our data
portal/zenodo, and re-usable since they are published with the CC-BY 4.0
license.

The fields defined inside of the datasets are described in the description
field of the dataset.

Outline the identifiability of data and refer to standard identification
mechanism. Do you make use of persistent and unique identifiers such as
Digital Object Identifiers?

Community health indicators are uploaded to our portal and Zenodo (
https://zenodo.org/communities/stars4all  ). That is because, Zenodo is used
by our project to generate DOIs and to archive (make persistent) our datasets.
Also, this allows to index our data in OpenAire.

Outline naming conventions used

For naming, the following convention has been used: social-name_of_the_project

Outline the approach towards search keyword

Our datasets are published both in our data portal and in Zenodo. A set of
tags have been defined to tag each dataset, facilitating their search. Plus,
in our data portal, based on CKAN, users can make searches based on the common
metadata defined for all datasets that has been previously described in this
section.

Outline the approach for clear versioning

Both Zenodo and our data portal have a versioning system including in the
metadata of the datasets. The version changes when a modification has been
made on a dataset. The change of the version does not imply a request of a new
DOI.

Specify standards for metadata creation (if any). If there are no standards in
your discipline describe what metadata will be created and how

There is no entry in the Metadata Standards Directory for the datasets
generated in our project. The convention follow to describe the fields of the
dataset is decribed in D3.6 Models and methods for community health monitoring
(final release)

####  2.2 Making data openly accessible [FAIR data]

Specify which data will be made openly available? If some data is kept closed
provide rationale for doing so

All data generated have been made openly available.

Specify how the data will be made available

Data is available from our data portal. There is one social dataset per each
project and from our Zenodo community  (
_http://www.zenodo.org/communities/stars4all_ )

Specify what methods or software tools are needed to access the data? Is
documentation about the software needed to access the data included? Is it
possible to include the relevant software (e.g. in open source code)?

You only need a browser to access to the data. Plus, you can use the API of
the data portal. You can find more information here:
_https://docs.ckan.org/en/2.7/api/index.html_

Specify where the data and associated metadata, documentation and code are
deposited

The data are deposited in our:

Data portal, inside of each project. (  _http://data.stars4all.eu_ )

Zenodo Community -> _http://www.zenodo.org/communities/stars4all_

You can access to visual components here:

_http://dashboards.stars4all.eu/social/_

The source code is here:  _https://github.com/STARS4ALL/STARS4ALL-social-
dashboard_

Specify how access will be provided in case there are any restrictions

You can use the contact email for data issues of our project:
data@stars4all.eu

####  2.3 Making data interoperable [FAIR data]

Assess the interoperability of your data. Specify what data and metadata
vocabularies, standards or methodologies you will follow to facilitate
interoperability.

To facilitate interoperability, we have used the same metadata for all
datasets generated in the project. It is based in the metadata vocabulary
given by CKAN.

Standards and methodologies are described in D3.6 Models and methods for
community health monitoring.

Specify whether you will be using standard vocabulary for all data types
present in your data set, to allow inter-disciplinary interoperability? If
not, will you provide mapping to more commonly used ontologies?

Vocabulary is described in deliverable D3.6 Models and methods for community
health monitoring.

####  2.4 Increase data re-use (through clarifying licenses) [FAIR data]

Specify how the data will be licenced to permit the widest reuse possible

Data is licensed with CC-BY 4.0 that allows to:

Sharehare — copy and redistribute the material in any medium or format

Adaptdapt — remix, transform, and build upon the material for any purpose,
even commercially.

Specify when the data will be made available for re-use. If applicable,
specify why and for what period a data embargo is needed

There is no embargo period. Dataset is published periodically.

Specify whether the data produced and/or used in the project is useable by
third parties, in particular after the end of the project? If the re-use of
some data is restricted, explain why

These data is very important for the coordinators of the LPIs (Light Pollution
Initiatives) because they can see easily the impact of their policies and the
health of their community. Also, it can be used by social researchers to
measure diferent social communities.

Describe data quality assurance processes

The process to publish a dataset is supervised. A curator checks whether the
dataset is generated properly, before being published.

Specify the length of time for which the data will remain re-usable

Data will be archived while Zenodo platform remains open.

###  3\. Allocation of resources

Estimate the costs for making your data FAIR. Describe how you intend to cover
these costs

The cost of making our data FAIR in Zenodo is free. The cost of the data
infrastructure will be covered by the STARS4ALL foundation.

Clearly identify responsibilities for data management in your project

The data management process is managed by UPM (Universidad Politécnica de
Madrid).

Describe costs and potential value of long term preservation

We use Zenodo for long term preservation. With these datasets, scientists will
be able to measure the evolution of the health on social communities.

###  4\. Data security

Address data recovery as well as secure storage and transfer of sensitive data

These kind of data do not have security restrictions because they are not
sensitive values. Also, these data do not contain personal information so
there is not need to apply protection measurements.

Data, with the exception of datasets in Zenodo, are hosted in UPM's servers.
Only authorised personnel can access the location where these servers are
hosted, and periodical backups are being done.

###  5\. Ethical aspects

To be covered in the context of the ethics review, ethics section of DoA and
ethics deliverables. Include references and related technical aspects if not
covered by the former

These datasets do not contain personal information so there is not need to
apply data protection measurements.

###  6\. Other

Refer to other national/funder/sectorial/departmental procedures for data
management that you are using (if any) No other issues reported.