Evidence map›Paper›PMID 34056584›Full record

ArticleFrontiers in big data2021

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Aakash Sharma, Thomas Bye Nilsen, Katja Pauline Czerwinska, Daria Onitiu, Lars Brenna, Dag Johansen, Håvard D Johansen

Abstract read
In one paragraph

Article in Frontiers in big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

7 authors.

Aakash SharmaDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
Thomas Bye NilsenDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
Katja Pauline CzerwinskaFaculty of Design, Computer Science, Media, RheinMain University of Applied Sciences, Wiesbaden, Germany.
Daria OnitiuNorthumbria Law School, Northumbria University, Newcastle upon Tyne, United Kingdom.
Lars BrennaDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
Dag JohansenDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
Håvard D JohansenDepartment of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using

Indexed as

big data processingcompliancedata sharinggossipopen dataprivacyprivacy policiesresearch data

Identifiers

PMID34056584
PMCPMC8155614

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.