Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,663
datasets available to search
ShareScore release 0.9.0
Dataset results
1,663 results for “BIAS”
Annual egg productivity predicts female-biased mortality in avian species
<p>Among avian species, the differential cost entailed by either sex in competition for mates have been regarded as the main evolutionary influence on sex differences in mortality rates. However, empirical evidence suggests that sex-biased adult mortality is mainly related to differential energy investment in gamete production, a greater annual mass devoted to egg production leading to higher female mortality. We explicitly tested the generality of this pattern in a comparative framework. Annual egg production can be relatively large in some species (up to 200% of female body mass) and annual mortality is generally biased towards females. We showed that greater annual egg productivity resulted in higher mortality rates of females relative to males. Mating system was secondarily important, species where males were more involved in mating competition having more equal mortality rates between the sexes. However, both traits explained only a limited fraction of the interspecific variation in female-biased mortality. Other traits, such as sexual size dimorphism and parental care, had much weaker influences on female-biased mortality. Our results suggest that both annual mass devoted to gamete production by females and mating system contribute to the evolution of the fundamental life-history trade-off between reproduction and survival in avian taxa.</p>
Supplementary File_Nirmatrelvir_Risk of Bias Excel Tool (Version 1)
<p>Supplementary material (Risk of Bias Excel Tool (Version 1)) for the Cochrane Review "Nirmatrelvir combined with ritonavir for preventing and treating COVID-19".</p>
Large zero bias peaks and dips in a four-terminal thin InAs-Al nanowire device
<p>This file contains the raw data and processing codes of the paper 'Large zero bias peaks and dips in a four-terminal thin InAs-Al nanowire device'.</p>
Supplementary material 7 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S7. Taxonomic classification of the rDNA of fungal.: Explanation note: Taxonomic classification of the rDNA of fungal shotgun metagenome.
Supplementary material 3 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S3. Data set of the SSU V4 and V5 barcodes.: Explanation note: Data set of the SSU V4 and V5 barcodes.
Supplementary material 1 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S1. Characteristics of soil samples.: Explanation note: Characteristics of soil samples used in this study.
Supplementary material 6 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S6. Data set of the LSU D1, D2, and D3 barcodes.: Explanation note: Data set of the LSU D1, D2, and D3 barcodes.
Supplementary material 2 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S2. Taxonomic composition and clustering of the mock community sample.: Explanation note: Taxonomic composition and clustering of the mock community sample.
Supplementary material 1 from: Torralba-Burrial A, Merino-Sáinz I, Anadón A (2014) The relevance, biases, and importance of digitising opportunistic non-standardised collections: A case study in Iberian harvestmen fauna with BOS Arthropod Collection datasets (Arachnida, Opiliones). ZooKeys 404: 71-89. https://doi.org/10.3897/zookeys.404.6520
Harvestmen specimens included in this unplanned collection events subset.: Explanation note: Alternative link for download: http://hdl.handle.net/10651/24734
Supplementary material 1 from: Thomsen M, Wernberg T, Olden J, Byers J, Bruno J, Silliman B, Schiel D (2014) Forty years of experiments on aquatic invasive species: are study biases limiting our understanding of impacts? NeoBiota 22: 1-22. https://doi.org/10.3897/neobiota.22.6224
List of reviewed references.: Explanation note: References are divided into general (G), freshwater (F), aquatic (FM) or marine (M) journals. References with an asterisk (*) did not replicate or pseudo-replicated either treatment and/or control plots.
Dataset from: "The necessity to choose causes reward-related anticipatory biasing: Parieto-occipital alpha-band oscillations reveal suppression of low-value targets"
<p>Dataset from the following publication:</p> <p>Heuer, A., Wolf, C., Schütz, A. C., & Schubö, A. (2017). The necessity to choose causes reward-related anticipatory biasing: Parieto-occipital alpha-band oscillations reveal suppression of low-value targets. Scientific Reports, 7:14318. doi:10.1038/s41598-017-14742-w</p>
Figure 5 in Taxonomic bias in biodiversity data and societal preferences
Figure 5. Relation between age, origin and quality of the occurrence data for 24 taxonomic classes. Graph showing the first two axes of a Multiple Correspondence Analysis (MCA) performed on 5 million random occurrences. Labels in black represent the categories considered for all occurrences. Classes' names (in green) are placed at the average position of the class occurrences. Occurrence age contains eight time intervals and an Unknown Year category; data origin contains three categories: Specimen for specimen-based occurrences, Observation for observation-based occurrences, and Unknown for unknown origins; data quality contains four categories: Temporal issue for the lack of year or month, Spatial issues for the lack of coordinates, Both issues and No issue.
Figure 4 in Taxonomic bias in biodiversity data and societal preferences
Figure 4. Taxonomic heterogeneity in sampling, occurrence data origin and quality for 24 taxonomic classes. Top: Proportion of species per class recorded in GBIF with at least one occurrence (light green: p>1), with more than 20 occurrences (green: p>20), and with more than 20 spatially distinct occurrences (i.e. "decently" sampled – dark green: p>20d). For all classes, except Aves, less than 1/3 of all species are "decently" sampled. Classes are ranked according to their proportion of "decently" sampled species. Middle: Occurrence origin (basisOfRecord) for each class. Some classes like Amphibia have a high proportion of occurrences based on specimens (blue: living or preserved specimen, material samples or fossils), whereas others like Aves have a majority of occurrences based on observation (orange: machine or human observation, literature). Grey bars show occurrences where the record basis is unknown. Classes are ranked according to their proportion of specimenbased occurrences. Bottom: Data incompleteness. Proportion of occurrences with spatial (purple) or temporal (yellow) inaccuracies for each class. Spatial inaccuracy corresponds to an occurrence lacking coordinates or tagged has having geospatial issues by GBIF. Temporal inaccuracy corresponds to a sampling event with no specified month or year. Classes are ranked according to their proportion of occurrences with spatial issues.
Figure 3 in Taxonomic bias in biodiversity data and societal preferences
Figure 3. Biodiversity occurrences recorded in GBIF between 1900 and 2006. For each curve, the number of occurrences was plotted yearly. Top: black = all 24 classes considered together, yellow = Aves; Middle: yellow = Magnoliopsida, blue = Insecta, green = Liliopsida; Bottom: green = Actinopterygii, yellow = Mammalia, light blue = Reptilia, dark blue = Amphibia, orange = Florideophyceae, purple = Globothalamea.
Figure 2 in Taxonomic bias in biodiversity data and societal preferences
Figure 2. Evolution over time of the taxonomic bias for each class. The larger the circle, the higher the deviation from I, the 'ideal' number of occurrences per class if no taxonomic bias is observed. Red dots indicate negative deviations (i.e. shortfall in occurrences = under-represented classes); green dots indicate positive deviations (i.e. excess of occurrences = over-represented classes).
Figure 1 in Taxonomic bias in biodiversity data and societal preferences
Figure 1. Taxonomic bias in biodiversity occurrence data. The vertical line at x = 0 depicts the 'ideal' number of occurrences per class, where each class is sampled proportionally to its number of known species. Green and red bars show the classes that are over- and under-represented in the GBIF mediated database compared to this 'ideal' sampling, respectively. Insects lack>200 millions occurrences and birds have an excess of>200 millions occurrences compared to an unbiased taxonomic sampling. Because birds and insects are greatly over- and
The Impact of Biases on Health Disinformation Research
Open the record for dataset details and reuse information.
Risk-of-bias v.2 assessment with large language models
<p>See https://bitbucket.org/aimedtech/fewshot_rob for more information.</p>
Brick by Brick Bias: Arab Muslim Experience of Intersectionality in Housing
<p>Experimental data on intersectional discrimination against Arab Muslims in the Swedish rental housing market. Definitions for variables are in the Excel data file. The Stata do-file contains the complete analysis in accordance with the paper. The published paper can be accessed here: <a href="https://doi.org/10.1080/1369183X.2024.2366319">https://doi.org/10.1080/1369183X.2024.2366319</a>. </p>
Addressing bias in digital cultural heritage collections metadata: the example of the DE-BIAS project
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.