ArticleZooKeys2013
A specialist's audit of aggregated occurrence records: An 'aggregator's' perspective.
Article in ZooKeys, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed.
- From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences.PLoS biology · 2021Article
- The Atlas of Living Australia: History, current state and future directions.Biodiversity data journal · 2021Article
- ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna.Scientific data · 2019Article
- Article
- Contrasting spatial, temporal and environmental patterns in observation and specimen based species occurrence data.PloS one · 2018Article
- To increase trust, change the social design behind aggregated biodiversity data.Database : the journal of biological databases and curation · 2018Article
- Article
- Biodiversity analysis in the digital era.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016Review
- The geospatial data quality REST API for primary biodiversity data.Bioinformatics (Oxford, England) · 2016Article
- Corrected data re-harvested: curating literature in the era of networked biodiversity informatics.Biodiversity data journal · 2015Article
- The GBIF integrated publishing toolkit: facilitating the efficient publishing of biodiversity data on the internet.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A recent ZooKeys' paper (Mesibov, 2013: http://www.pensoft.net/journal_home_page.php?journal_id=1&page=article&SESID=df7bcb35b02603283dcb83ee0e0af0c9&type=show&article_id=5111) has highlighted data quality issues in aggregated data sets, but did not provide a realistic way to address these issues. This paper provides an aggregator's perspective including ways that the whole community can help to address data quality issues. The establishment of GBIF and national nodes (national aggregators) such as the Atlas of Living Australia (ALA) have integrated and exposed a huge diversity of biological observations along with many associated issues. Much of the admirable work by Mesibov (2013) was enabled by having the data exposed. Data quality, one of the highest priorities for GBIF, the national nodes and other aggregators, depends on both automatic methods and community experts to detect and correct data issues. Not all issues can however be automatically detected or corrected, so community assistance is needed to help improve the quality of exposed biological data. We do need to improve the infrastructure and associated processes to more easily identify data issues and document all changes to ensure a full record is permanently and publicly available.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.