Evidence map›Paper›PMID 37228797›Full record

ArticleUCL open. Environment2021

Key opportunities and challenges for the use of big data in migration research and policy.

Lydia H V Franklinos, Rebecca Parrish, Rachel Burns, Andrea Caflisch, Bishawjit Mallick, Taifur Rahman, Vasileios Routsis, Ana Sebastián López, Andrew J Tatem, Robert Trigwell

Abstract read
In one paragraph

Article in UCL open. Environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Lydia H V FranklinosInstitute for Global Health, University College London, London, UK.ORCID https://orcid.org/0000-0002-5766-3986
Rebecca ParrishInstitute for Global Health, University College London, London, UK.ORCID https://orcid.org/0000-0003-3105-7969
Rachel BurnsCentre of Public Health Data Science, Institute of Health Informatics, University College London, London, UK.ORCID https://orcid.org/0000-0002-9559-2867
Andrea CaflischUnited Nations' Displacement Tracking Matrix, International Organization for Migration, International Organization for Migration, Juba, South Sudan.
Bishawjit MallickCU Population Center, Institute of Behavioral Science, University of Colorado Boulder Campus, Boulder, CO, USA.ORCID https://orcid.org/0000-0002-9492-1059
Taifur RahmanHealth Management BD Foundation, Sector 6, Uttara, Dhaka, Bangladesh.
Vasileios RoutsisDepartment of Information Studies, University College London, London, UK.ORCID https://orcid.org/0000-0001-8275-4284
Ana Sebastián LópezGMV Innovating Solutions Ltd, HQ Building, Thomson Avenue, Harwell Campus, Didcot, UK.
Andrew J TatemWorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.ORCID https://orcid.org/0000-0002-7270-941X
Robert TrigwellUnited Nations' Displacement Tracking Matrix, International Organization for Migration, United Nations, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.

Indexed as

big dataclimate changecross-disciplinary researchdata securitydisplacementenvironmenthealthhumanitarianmigrationpolicy

Identifiers

PMID37228797
PMCPMC10171412

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.