# Workshop: How to set up and configure a Dataverse repository that suits your needs

Alina Danciu, Geneviève Michaud, Baptiste Rouxel, Tom Villette

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## To cite this version:

Alina Danciu, Geneviève Michaud, Baptiste Rouxel, Tom Villette. Workshop: How to set up and configure a Dataverse repository that suits your needs. École thématique. France. 2022. hal-03906342

HAL Id: hal-03906342

https://sciencespo.hal.science/hal-03906342

Submitted on 19 Dec 2022

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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# Workshop: How to set up and configure a Dataverse repository that suits your needs

IASSIST 2022

07-06-2022

DOI:10.5281/zenodo.6625972

Alina Danciu, Geneviève Michaud, Baptiste Rouxel
CDSP, Sciences Po & CNRS
# How to set up and configure a Dataverse repository that suits your needs

1. Data Documentation Initiative (DDI) and FAIR

2. The Dataverse project in brief

3. What's in a data repository?

4. Assess your needs

5. Main Dataverse features

6. A Dataverse bingo

7. Desirable improvements

8. Extending functionalities

9. Stay tuned / contribute to the Dataverse project
# Data Documentation Initiative (DDI) and FAIR

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## Quiz : What is FAIR ?

* An elaborate contemporary folk dance involving the energetic flapping of the jaws and waving of hands, followed by a prolonged period of inactivity

* A specific set of universally agreed practices for sharing research data, implemented by adhering to well-defined specifications applying equally across all domains

* A compelling article published in *Nature* in 2016, describing the basic principles which should be followed for sharing research data in sciences of all kinds
<figure><img src="image_5.png" /></figure>

## Quiz : What is FAIR ?

* An elaborate contemporary folk dance involving the energetic flapping of the jaws and waving of hands, followed by a prolonged period of inactivity

* A specific set of universally agreed practices for sharing research data, implemented by adhering to well-defined specifications applying equally across all domains

* A compelling article published in *Nature* in 2016, describing the basic principles which should be followed for sharing research data in sciences of all kinds
<figure><img src="image_6.png" /></figure>

## Quiz : What is FAIR ?

* An elaborate contemporary folk dance involving the energetic flapping of the jaws and waving of hands, followed by a prolonged period of inactivity

* A specific set of universally agreed practices for sharing research data, implemented by adhering to well-defined specifications applying equally across all domains

* A compelling article published in *Nature* in 2016, describing the basic principles which should be followed for sharing research data in sciences of all kinds
<figure><img src="image_7.png" /></figure>

## Quiz : What is FAIR ?

* An elaborate contemporary folk dance involving the energetic flapping of the jaws and waving of hands, followed by a prolonged period of inactivity

* A specific set of universally agreed practices for sharing research data, implemented by adhering to well-defined specifications applying equally across all domains

> A compelling article published in *Nature* in 2016, describing the basic principles which should be followed for sharing research data in sciences of all kinds
# FAIR is a (simple) idea

* Findable, Accessible, Interoperable, Re-usable (“The FAIR Guiding Principles”)*

* Promote data-sharing and reuse within and between domains

* DDI has been focused on data sharing and reuse for decades

* Important ideas whose time has come

* Demand for more data (large projects, new technologies)

* Broader acceptance of data-sharing as *important*

**The key to FAIR data is *metadata***

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DATA DOCUMENTATION INITIATIVE

<footnote>* Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. 2016;3. 160018. https://doi.org/10.1038/sdata.2016.18</footnote>
# How does DDI help about FAIR

* Many people talk about FAIR but focus only on Findability and Accessibility

* This isn't “FAIR”, it's “FA”

* FAIR include the Interoperability and Reusability parts as well!

* This has been the primary focus of DDI for a long time

* The hard, expensive part...

* DDI is for people who want to be serious about FAIR!

* DDI provides the rich metadata which is required

* Reminder: DDI is written in XML
# What is DDI?

* DDI = Data Documentation Initiative

* An international metadata standard

* Used primarily in the social and behavioural sciences, economics, health

* An open standard designed for data sharing and reuse

* A structure for describing data and its related information

* Describes data from surveys and other observation-based data collection methods

* Currently moving towards covering new data types and data from new domains.
# DDI Specifications

* DDI Codebook (aka DDI-C)

* DDI Lifecycle (aka DDI-L)

* DDI Cross Domain Integration (aka “DDI-CDI”) - to be released

* Each one suits different needs and types of data
# DDI Codebook

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* An XML description of a “codebook” (a data dictionary)

* Rectangular files

* No concept of metadata reuse

* Based on models in existing analysis tools

* (Stata, SPSS, SAS, etc.)

* Included Dublin Core and descriptive “study-level” metadata

* Machine-readable

* Described data for a single study (one point in time)

* After-the-fact description to support archiving and reuse
# DDI Codebook

Metadata not
structured

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Lost Metadata Manager

DDI Codebook
structures

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Metadata structured
by DDI Codebook
standard

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Do you speak xml ? No? Then you need a tool.

<titl>Canadian Community Health Survey, 2012: Annual
Component </titl>

<labl>Questionnaire (.pdf)</labl>

<dataDscr><notes>The variables in this study are
identical to earlier waves. </notes></dataDscr>

<titl>Canadian Gallup Poll, May 2000</titl>

<dataChck>Quality checks were performed by Carleton
University Data Centre. </dataChck>
Want to know more ?

* Request Training from the DDI Alliance Training Group

* DDI Alliance, responsible for the standard, is a rich source of information (check the website!)

* DDI Training Group Zenodo Collection

* Attend CODATA / DDI Alliance webinars
# The Dataverse project in brief
## Dataverse in brief

Dataverse is an open source research data repository software, that is being developed at Harvard IQSS & supported by a consortium (GDCC). It has been initially dedicated to the SSH but is being widely used worldwide.

To date, 78 instances are using Dataverse, among which:

- [Scholars Portal](Borealis)

- [DataverseNO](#)

- [SODHA](#)

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Source: [Dataverse Metrics](#)
# Dataverse at the CDSP

Our first need was a repository to host our data and our DDI metadata. Dataverse uses DDI-Codebook (DDI-C) / DDI 2.5

The CDSP has set up an institutional SSH research data repository: data.sciencespo, including two collections:

- CDSP databank: curated up to the level of the variable, pseudonymized,

- national scope

- Sciences Po Self-deposit collection

- institutional scope
# What's in a data repository?
# Dataverse and FAIR

Dataverse provides a framework to achieve data FAIRness (Findable, Accessible, Interoperable and Reusable). Customizing a Dataverse instance and curating a dataset improves data final reusability.

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Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool (v1.0.0). Zenodo.

https://doi.org/10.5281/zenodo.4063720. Available at: https://www.f-ujl.net, the test results shown here are based on preliminary data and code which still is under development.

Results shown are available here (resource evaluated 23-05-2022)
# FAIR data

doi

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a persistent identifier

That takes you to
<a landing page>

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Where you find
<quality metadata>

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Including <a license>

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And a one step <access to
curated data>
# What's in a data repository landing page?

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Figure from: Helena Cousijn, Amye Kenall, Emma Ganley, Melissa Harrison, David Kernohan, Thomas Lemberger, Fiona Murphy, Patrick Polischuk, Simone Taylor, Maryann Martone & Tim Clark, 2018, "A data citation roadmap for scientific publishers", www.nature.com, https://doi.org/10.1038/sdata.2018.259
# Collections, Datasets, files

Information is arranged in Collections, Datasets and Files:

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## Collections

including as many (sub-)collections as needed

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## Datasets

are included in a collection and include

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## Files

données qualitatives (Sciences Po, Centre de données socio-politiques (CDSP), CNRS)

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Mar 29, 2021

Base de données pédagogique : Styles de vie et Environnement (2017)

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May 10, 2022

Petev, Ivaylo, 2022, "Base de données pédagogique : Styles de vie et Environnement (2017)", https://doi.org/10.21410/7E4/OFVDJL, data.sciencespo, V1, UNF:6:7NGeEEHrqVHU/x/Old2VGA== [fileUNF]

Les bases de données pédagogiques ont été créées par le Centre de Données Socio-Politiques à partir d'enquêtes préexistantes. Leur but est de mettre à disposition un matériel facile à prendre en main, dans le cadre d'enseignements et cours. Elles ont pour seule fonction la format...

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cdsp_sven2017_dataset.csv.zip
ZIP Archive - 72.9 KB
Published May 10, 2022
0 Downloads
MD5: ee3_4d6

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# Anatomy of a dataset 1/3

Dataverse offers a comprehensive **landing page** for each dataset, a strong basis for FAIR data.

This is a public web page showing metadata.

This page is meant for **humans** to read it.

But it serves also metadata for **machines**.

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# Anatomy of a dataset 2/3

Since version 4.8, Dataverse supports **Schema.org** embedded metadata in every dataset landing page.

Schema.org metadata are meant for **machines** to support **discoverability** of datasets. Search engines rely on such metadata.

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Anatomy of a dataset 3/3

When a dataset is created, Dataverse sends to the **PID provider** (**Datacite** for DOIs), a set of metadata and gets back a newly minted PID.

The PID provider secures the **link** between the PID and the dataset URL on the repository. It is the responsibility of the PID maintainer to **maintain** this link up to date.

DataCite Search

Works

People

Repositories

Members

Support

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Sign in

Pratiques numériques - vague 1 (2013)

Dataset published 2020 via data.sciencespo

L'enquête "Pratiques numériques" (PN) mesure l'évolution des comportements liés aux technologies de l'information et de la communication (TIC) dans un panel où les répondants ont été équipés d'une tablette tactile connectée. L'enquête est répétée annuellement. S'inscrivant dans la tradition des enquêtes TIC", cette étude a pour objectif de décrire l'équipement des ménages (ordinateur fixe, ordinateur portable, accès à Internet y compris par téléphone mobile, téléviseur, console de jeux, etc.), ainsi que les usages numériques des..

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No citations were reported. No usage information was reported.

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https://doi.org/10.21410/7e4/dwhy6t

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Cite

This data repository is not currently reporting usage information. For information on how your repository
can submit usage information, please see our documentation.

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Repository

Centre de données socio-politiques

Download

DataCite XML

DataCite JSON
Assess your needs

<table><tr><td>Who?</td><td>Resources and skills</td></tr><tr><td>For whom?</td><td>Data depositors, data curators, re-users</td></tr><tr><td>How?</td><td>Curation processes</td></tr><tr><td>What?</td><td>Data types, scientific field(s)</td></tr></table>
## Who?

What are your available **resources** and **skills**? This should be your first question. In other terms: what are and will be your available IT **resources** (system admin, devOps) to **setup** and **maintain** a Dataverse instance over time.

You will first bear **installation** costs. **Maintenance** comes with a cost (upgrades and backups):

**A repository is a commitment to sustainability**
# For whom?

What is the underlying **organization**?

<table><tr><td>Is <strong>internationalization</strong> needed?</td><td></td><td>See <u>UI customization</u>, <u>limitations</u>, metadata language</td></tr><tr><td>Do you need to <strong>signal</strong> metadata on other portal?</td><td>Will the repository be harvested?<br>Will the repository harvest others?</td><td>See <u>OAI-PMH</u></td></tr><tr><td><strong>Who</strong> are the <strong>depositors</strong>, the <strong>curators</strong>?</td><td>Are they included in a directory<br>(University Identity Provider)?<br>A federated directory (like <u>eduGAIN</u>)?</td><td>See <u>authentication</u></td></tr></table>
# How? Research data management

How is **Research Data Management** organized?

<table><tr><td>Automation</td><td>Will you rely only on the <span style="color:red;">web interface</span>?<br>Will you <span style="color:red;">automate</span> some tasks?</td><td>See <a href="#">Dataverse API</a></td></tr><tr><td>Curation</td><td>Are there dedicated team members for <span style="color:red;">data and metadata curation</span>?<br>Are data providers self-depositing?</td><td>How should you structure the collections tree to facilitate RDM? See <a href="#">permissions</a></td></tr><tr><td>Workflows</td><td>Is there collaboration taking place before data publication?</td><td>Review, private URL Embargo, <a href="#">Workflows</a></td></tr></table>
## Which data?

<table><tr><td>Do you manage <strong>qualitative</strong> data?</td><td>See high volume and storage</td></tr><tr><td>Do you manage <strong>quantitative</strong> data?</td><td>See file ingest, integrations, previewers</td></tr><tr><td>Do you manage <strong>active</strong> data?</td><td>See collaboration features, <a href="#">dataset versioning</a></td></tr><tr><td>Is it a <strong>general purpose</strong> or a <strong>specialized repository</strong>?</td><td>See metadata blocks, metadata customization, templates</td></tr></table>
# Main Dataverse features

Crowdsourced list of Dataverse features
https://dataverse.org/software-features
# Dataset metadata

* **Persistent Identifiers (PID)**

* DOI or Handle are supported by Dataverse

* Mandatory at dataset level, optional at file level

* **Metadata**

* Fields customization per collection (enable/disable blocks of metadata, select fields & configure as required/optional)

* Controlled vocabularies

* Templates

Improves metadata quality

* **Licenses**

* From version 5.10 licenses are available as controlled vocabularies

Improves interoperability

<figure><img src="image_39.png" /></figure>
# Dataset files

* File **ingestion**:

* Tabular data (SPSS, Stata, R, Excel, C/TSV)

* ZIP file ingestion & tree view

* Other formats uploaded as is

* Access restrictions at file-level

* Metadata: Limited file level metadata (tags)

<figure><img src="image_40.png" /></figure>
# Metadata export formats

* DDI

* DDI HTML Codebook

* OpenAire / DataCite

* Dublin core

* OAI-ORE

* Schema.org JSON-LD

* JSON

<figure><img src="image_41.png" /></figure>

Export Metadata

<figure><img src="image_42.png" /></figure>

Dublin Core

DDI

DataCite

DDI HTML Codebook

JSON

OAI_ORE

OpenAIRE

Schema.org JSON-LD
# Publication workflow

* Versioning

* Embargo (file level)

* Pre-reserved PID

* PID available for a pre-print

* Private URL for review

* Advanced workflow customization, triggering actions

* actions on pre/post publication

* long term preservation
# Harvesting metadata (OAI-PMH)

* **Server:** Enable harvesting for your Dataverse by creating sets (defined by a query)

* **Client:** Harvest other Dataverse instances

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>
# Authentication

You can configure a single source of authentication or a **combination** of several.

* Local account

* Username/Password

* Institutional accounts (Shibboleth)

* Home institution's Identity Provider (University SSO)

* Federation of Identity Providers (EduGain)

* External accounts ( OAuth2)

* Google

* ORCID

* GitHub

* Microsoft

<figure><img src="image_46.png" /></figure>

## Log In

Log in or sign up with your institutional account — more information about account creation. Leaving your institution? Please contact Harvard Dataverse Support for assistance.

Your Institution

Please select...

<figure><img src="image_47.png" /></figure>

Continue

Allow me to type the name of my institution

Other options

Username/Email

GitHub

Google

ORCID

Sign up for a Dataverse account.
# Permissions

* Customization of permissions at a [collection](https://docs.openstreetmap.org/collections/), [dataset](https://docs.openstreetmap.org/datasets/) or [file level](https://docs.openstreetmap.org/files/)

* Manage permissions by

* User

* Group

* Role (= list of predefined permissions: can publish, can edit, can download, ...)

* If a file is [restricted](https://docs.openstreetmap.org/files.#), users can send an access request
# UI customization

* Homepage

* Stylesheet (CSS)

* Footer

* Carousel

* Search facets

<figure><img src="image_48.png" /></figure>
Internationalization

* UI language switcher

* Does not translate the metadata

<figure><img src="image_49.png" /></figure>
<table><thead><tr><th></th><th>Dataverse instance</th><th>Collection</th><th>Dataset</th><th>File</th></tr></thead><tbody><tr><td>Branding</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Permissions</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>PID (DOI or Handle)<br>PID granularity is set at the instance level</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>License</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Search facets<br>(metadata filters)</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Metadata blocks choice</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Metadata profiles<br>(mandatory/optional metadata)</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Metadata language</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Metadata templates</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Harvesting entrypoint<br>(OAI-PMH)</td><td>✓</td><td>✓</td><td>✓</td><td></td></tr></tbody></table>

Main settings and relevant scope for Dataverse
3 use cases & a Dataverse bingo
## Use case #1

You are asked to set up a repository to host data produced within one astronomical research center. Researchers will (self) deposit data with minimum support. No curation team is involved. Data are meant to be public, except some unpublished data that will support pre-prints.

What choices would you suggest?
## Use case #2

You are missioned to setup a generic purposes repository for a federation of universities. Data project are often created by research groups that span over more than one institution. Dedicated curation team are available in each partner institution. The repository metadata should be showcased on a national Open Science Portal.

Did we tell you this use case takes place in Belgium?

What choices would you suggest? How collection could be organized?
## Use case #3

You are responsible for the national Open Science Portal.
# Desirable improvements

---
# Desirable improvements

* Current limitations

* PID sources (one single shoulder / PID source)

* Exporting metadata only possible after publication

* Possible improvements

* OAI-PMH (lacks license, object type dataset)

* DDI support (export / harvesting)

* Internationalization (only UI and CVs, no multiple languages for other metadata)
# Extending functionalities

* Out of the box

* Advanced metadata customization

* Long term preservation (BagIt)

* External tools & community tools

* pyDataverse

* Data previewers

* Data explorer

* Data curation tool

* Dataverse Feed

* Other tools

* DP Creator

* Whole Tale
# Advanced metadata customization

Customization can be done, but increases maintenance cost and can create errors.

<figure><img src="image_50.png" /></figure>

* Adding new fields

* It's fine

* Adding controlled vocabularies

* It's fine but updates are tricky

* Editing existing fields

* Could break functionalities in the current / upcoming version

Adding translations is needed if supporting multiple languages.

New fields are not supported in the DDI export (but controlled vocabularies are supported for original fields).
<figure><img src="image_51.png" /></figure>

Custom metadata blocks at DaRUS (University of Stuttgart) Dataverse instance https://doi.org/10.18419/darus-2463
Long term preservation (BagIt)

* Dataverse supports [BagIt](#)

* Research Data Alliance (RDA) conformant

* Workflow can be configured to automatically archive data

* An infrastructure is needed to store the data
# A RDM "swiss knife": pyDataverse

* Developed by Stefan Kasberger at AUSSDA

* (Austrian Social Science Data Archive)

* Provides a python interface to make operations on Dataverse

* (example: using the API, migrate data, ...)

* Can also be used to explore data

* (example: download data -> run any script)
# Data previewers

* Developed by the Dataverse community

* Currently available:

* PDF, text

* Spreadsheet (example shown https://doi.org/10.15454/HOBUZH/RC K1NH)

* Image, video, audio

* HTML

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>
# Data explorer

* Developed by Scholars Portal

* Provides a web application to explore data from (ingested) tabular files

<figure><img src="image_54.png" /></figure>
Data curation tool

* Developed by Scholars Portal

* Provides a web application to help editing variable-level metadata for tabular files

https://github.com/scholarsportal/Dataverse-Data-Curation-Tool
# Dataverse Feed

* Prototype

* Easily embed your datasets into an existing webpage

* Customizable (features, appearance)

## Dataverse Feed

This is an example page using CDSP-SCPO/dataverse-feed with the data.sciencespo Dataverse instance. Here are the datasets from the CDSP collection:

<figure><img src="image_55.png" /></figure>

15 items

### Dynamiques de mobilisation - vague 4 (2014)

Tiberj, Vincent; Gougou, Florent, 2020, "Dynamiques de mobilisation - vague 4 (2014)", https://doi.org/10.21410/7E4/BWYIVL, data.sciencespo, V7, UNF:6: +kc5MZJ42xVcRwZiplspSg== [fileUNF]

La majeure partie des connaissances produites en sociologie politique quantitative provient des enquêtes conduites au moment des élections. L'étude longitudinale DYNAMOB est proposée par Florent Gougou, Vincent Tiberj et vingt-cinq politistes français. Elle se distingue des enquêtes classiques en couvrant aussi bien les périodes électorales que les périodes ordinaires. Ce dispositif vise à mesurer...

View

doi:10.21410/7E4/BWYIVL
## Other tools

* Reproducible research tool: [Whole Tale](https://www.researchgate.net/whole-tale)

* NSF-funded Data Infrastructure Building Block (DIBBS)

* Sensitive data analysis: [DP Creator](https://dp-creator.org) (in development)

* Leverages the open source tools from the [OpenDP](https://openDP.org) library (Harvard / Microsoft collaboration)

* Produce differentially private statistics on Dataverse

* And more!
How to stay tuned /
How to contribute to the
Dataverse Project

<figure><img src="image_56.png" /></figure>
## Ways to contribute

* Bug reports / Feature ideas / Feedback

* Pull requests: source code changes proposals

* Sponsoring issues (GDCC) / Financial support

## Dataverse Community

* **Google group:** official google group for Dataverse users interested in building a community around Dataverse Network software and expanding its functionality within and beyond social sciences.

* **GitHub**

* **Slack / Matrix**

* **Events**

* **Dataverse community calls** (~2 per month)

* **Dataverse community meetings** (1 per year)
Thank you

Alina.Danciu@sciencespo.fr Genevieve.Michaud@sciencespo.fr Baptiste.Rouxel@sciencespo.fr
# Accreditation

<figure><img src="image_57.png" /></figure>

The DDI and FAIR section (slides 3-15) is adapted from slides authored by the DDI Alliance Training Working Group

Based on content developed at the DDI Train-the-Trainers Dagstuhl workshop 2018

Picture credits - Codebook slide (13)

* Picture 1 (left) credit: woodleywonderworks, https://www.flickr.com/photos/wwworks/2472232245 (CC-BY) woodleywonderw

* Picture 2 (right) credit: Stephen Edmonds: https://www.flickr.com/photos/popcorncx/3516880947/ (CC-BY-SA)