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FIGURE 4 in Environmental predictors of the life history of the flag tetra Hyphessobrycon heterorhabdus (Characiformes: Characidae) in streams of the Eastern Amazon
FIGURE 4 |Variation of Condition Factor (K) for males (A) and females (B) ofHyphessobrycon heterorhabdus sampled between March 2019 and January 2020 in the Guamá River basin, Eastern Amazon, State of Pará, Brazil. The dashed line represents the accumulated monthly rainfall.
FIGURE 1 in Environmental predictors of the life history of the flag tetra Hyphessobrycon heterorhabdus (Characiformes: Characidae) in streams of the Eastern Amazon
FIGURE 1 | Location of the streams (black circles) in the Guamá River basin, Eastern Amazon, State of Pará, Brazil, where the specimens of Hyphessobrycon heterorhabdus were sampled between March 2019 and January 2020.
Indirect genetic effects are shaped by demographic history and ecology in Arabidopsis thaliana
<p><em>This folder contains data & code used for the study "Indirect genetic effects are shaped by demographic history and ecology in Arabidopsis thaliana"</em></p> <p>All data analyzed in the study are stored in the folder "data":</p> <ul> <li>"pheno_file.csv": the main phenotypic file corresponding to the experiment with paired plants used to estimate Indirect Genetic Effects.</li> <li>"pheno_file_single_plants.csv": phenotypic file with measurements of plant biomasses in the absence of competition (single plants)</li> <li>"call_method_75_TAIR9.csv": genomic data (SNPs) for each accession from the RegMap panel (ref [1])</li> <li>"Data_geo_RegMap_accessions.csv": geographic localization of each accession from the RegMap panel (ref [2])</li> <li>"igeGWAS_scores.csv": Genome-Wide Association Study (GWAS) results reporting for each SNP from the RegMap panel the p-value and estimated effect sizes of their direct and indirect genetic effects</li> <li>"1001_accessions_info.csv": geographic localization and admixture group for each accession from the 1001 genomes project (ref [3])</li> <li>"snp_data_all_samples.txt": allelic value of each accession from the 1001 genomes project at the eleven top SNPs associated with IGE</li> <li>"sample_names.txt": names of accessions listed in the file "snp_data_all_samples.txt"</li> <li>"climatic_data.csv": climatic data for each accessions from the 1001 genomes project (ref [4])</li> <li>"candidate_genes_all.csv": list of all genes (and associated GO terms) with a non-synonymous, nonsense, or frameshift mutation in close proximity (distance < half LD decay distance) and high linkage (r2>0.5) with a SNP significantly associated with IGE</li> <li>"genes.coord.bed": list of all genes in a +- 500 kb around top IGE SNPs and their coordinates</li> <li>"AllGenes_fst.GeneID.txt": pairwise Fst computed between each pair of admixture groups, for all genes annotated in the genome of A. thaliana</li> </ul> <p>"ABBA_BABA" subfolder contains ABBA_BABA statistics computed for each individual chromosome (Chr1-Chr5) using genomic windows of 20 kb with at least 250 SNPs per windows. ABBA-BABA statistics were computed using custom python scripts from https://github.com/simonhmartin/genomics_general</p> <p><br> "GEA" subfolder contains Genome-Environment Association results, with one file per chromosome x climatic variable. Climatique variable are indexed, following the order listed in the file "Climatic_variables.txt" within the subfolder "GEA". GEA analysis were run with the gemma program: https://github.com/genetics-statistics/GEMMA.</p> <p><br> "LD_IGE_SNPs" subfolders contains the list of SNPs located at +- 2Mb of a significant IGE SNP (one file per IGE SNP, named "SNPalias_LDSimplified.csv") and their linkage (r2) with the IGE SNP. It also contains the file "LD_windows_sizes.csv" with the half LD decay distances for all significant IGE SNP.</p> <p>All analysis performed to produce the tables and figures presented in the study (main manuscript & supplementary information) were done with the R script "Arabidopsis_IGE_analysis.R", which uses "manhattan_custom.R" as a source function to produce custom manhattan plots.</p> <p> </p> <p><strong>REFERENCES:</strong></p> <p>[1] Horton MW, Hancock AM, Huang YS, Toomajian C, Atwell S, Auton A, Muliyati NW, Platt A, Sperone FG, Vilhjálmsson BJ, et al. 2012. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nature Genetics 44: 212–216.</p> <p>[2] Anastasio AE, Platt A, Horton M, Grotewold E, Scholl R, Borevitz JO, Nordborg M, Bergelson J. 2011. Source verification of mis-identified Arabidopsis thaliana accessions. The Plant Journal 67: 554–566.</p> <p>[3] 1001 Genomes Consortium. 2016. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166: 481–491.</p> <p>[4] Ferrero-Serrano Á, Assmann SM. 2019. Phenotypic and genome-wide association with the local environment of Arabidopsis. Nature Ecology & Evolution 3: 274–285.</p>
Data supporting the manuscript entitled: 'Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.'
<p>Data living in this data repository supports the scientific article entitled: Intermittent soil water stress history favors microbial traits that better mitigate wheat biomass losses during subsequent water stress.</p> <p> </p> <p> </p> <p> </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Gustav Heinrich Otth, <a href="http://www.wikidata.org/entity/Q5889382">http://www.wikidata.org/entity/Q5889382</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Data from: Asynchronous life histories generate uneven arms races and impact the maintenance of mutualisms
<p>Mutualisms constitute a diverse class of ecologically important interactions, yet their ecological and evolutionary stability remain topics of debate in coevolutionary theory. Recent theoretical and empirical work has suggested that coevolutionary arms races may be involved in the maintenance of mutualistic interactions, sustaining mutually beneficial outcomes for interacting species while producing exaggerated traits. Here we present an individual-based model that evaluates how asynchronous life histories – i.e., partners with different average lifespans – change the dynamics of trait coevolution, the expected fitness outcomes for species involved, and the dynamics of selection differentials across time for each species. Results indicate that a longer-lived mutualist will consistently 'lose' an otherwise balanced coevolutionary arms race, being outpaced in both the mean trait value and fitness outcome compared to a shorter-lived partner. Furthermore, linear selection differentials on mutualistic traits become increasingly divergent as life histories become increasingly asynchronous, with the longer-lived species experiencing persistent directional selection and the shorter-lived species experiencing weaker, more inconsistent selection. These results suggest that asynchronous life histories can complicate the maintenance of mutualistic interactions via coevolutionary arms-races and that detecting coevolution via selection differentials may be difficult when life histories are sufficiently divergent.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Edward Whittall, <a href="http://www.wikidata.org/entity/Q5820029">http://www.wikidata.org/entity/Q5820029</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Edward Whittall, <a href="http://www.wikidata.org/entity/Q5820029">http://www.wikidata.org/entity/Q5820029</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Richard Kandt, <a href="http://www.wikidata.org/entity/Q76072">http://www.wikidata.org/entity/Q76072</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Carlo von Erlanger, <a href="http://www.wikidata.org/entity/Q69865">http://www.wikidata.org/entity/Q69865</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Theodor von Heuglin, <a href="http://www.wikidata.org/entity/Q61968">http://www.wikidata.org/entity/Q61968</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Georg Scheffler, <a href="http://www.wikidata.org/entity/Q21607877">http://www.wikidata.org/entity/Q21607877</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Native plant diversity creates microbial legacies that either promote or suppress non-natives, depending on drought history
<p>High-diverse native plant communities resist non-native plants more strongly than low-diverse communities, in part through resource competition. Yet, the role of soil biota is largely unknown, although non-native plants interact with soil biota. Here, we tested the responses of non-native plants to soil conditioned by different native plant diversities. We applied well-watered and dry treatments in the conditioning and response phases to explore the effects of historical and contemporary environmental stresses. Historical water conditions determined the effects of native diversity via soil biota on responding non-natives grown in well-watered environments. Non-native growth decreased with native species richness for well-watered soil inocula but increased for dry soil inocula. However, non-native growth in dry environments did not depend on conditioning native species richness of soil inocula. We provide a new understanding of mechanisms behind diversity-invasibility relationships and demonstrate that temporal variation in environmental stress shapes relationships among native plant diversity, soil biota, and non-native plants.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Raymond Specht, <a href="http://www.wikidata.org/entity/Q3277170">http://www.wikidata.org/entity/Q3277170</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Ernest Nelmes, <a href="http://www.wikidata.org/entity/Q18987018">http://www.wikidata.org/entity/Q18987018</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Leopold Heyrovský, <a href="http://www.wikidata.org/entity/Q12032978">http://www.wikidata.org/entity/Q12032978</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Michel Adanson, <a href="http://www.wikidata.org/entity/Q315861">http://www.wikidata.org/entity/Q315861</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Dietrich Braasch, <a href="http://www.wikidata.org/entity/Q21340628">http://www.wikidata.org/entity/Q21340628</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Walter Wittmer, <a href="http://www.wikidata.org/entity/Q18818801">http://www.wikidata.org/entity/Q18818801</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
AOL Dataset for Browsing History and Topics of Interest
<p><strong>AOL Dataset for Browsing History and Topics of Interest</strong></p> <p>This record provides the datasets of the paper <em>The Privacy-Utility Trade-off in the Topics API</em> (DOI: <a href="https://doi.org/10.1145/3658644.3670368" target="_blank" rel="noopener">10.1145/3658644.3670368</a>; arXiv: <a href="https://arxiv.org/abs/2406.15309" target="_blank" rel="noopener">2406.15309</a>).</p> <p>The datasets generating code and the experimental results can be found in <a href="https://doi.org/10.5281/zenodo.11229402" target="_blank" rel="noopener">10.5281/zenodo.11229402</a> (<a href="https://github.com/nunesgh/topics-api-analysis" target="_blank" rel="noopener">github.com/nunesgh/topics-api-analysis</a>).</p> <p><strong>Files</strong></p> <ol> <li><code>AOL-treated.csv</code>: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies. It contains singletons (individuals with only one domain in their browsing histories) and one outlier (one user with 150.802 domain visits in three months) that are dropped in some analyses.</li> <li><code>AOL-treated-unique-domains.csv</code>: Auxiliary dataset containing all the unique domains from <code>AOL-treated.csv</code>.</li> <li><code>Citizen-Lab-Classification.csv</code>: Auxiliary dataset containing the Citizen Lab Classification data, as of commit <a href="https://github.com/citizenlab/test-lists/tree/ebd0ee8d41977b381972b2f6c471af5437d8d015/lists" target="_blank" rel="noopener">ebd0ee8</a>, treated for inconsistencies and filtered according to Mozilla's Public Suffix List, as of commit <a href="https://github.com/publicsuffix/list/tree/5e6ac3a082505ac4cf08858bdb38382d9a912833" target="_blank" rel="noopener">5e6ac3a</a>, extended by the discontinued TLDs: .bg.ac.yu, .ac.yu, .cg.yu, .co.yu, .edu.yu, .gov.yu, .net.yu, .org.yu, .yu, .or.tp, .tp, and .an.</li> <li><code>AOL-treated-Citizen-Lab-Classification-domain-match.csv</code>: Auxiliary dataset containing domains matched from <code>AOL-treated-unique-domains.csv</code> with domains and respective topics from <code>Citizen-Lab-Classification.csv</code>.</li> <li><code>Google-Topics-Classification-v1.txt</code>: Auxiliary dataset containing the Google Topics API taxonomy v1 data as provided by Google with the Chrome browser.</li> <li><code>AOL-treated-Google-Topics-Classification-v1-domain-match.csv</code>: Auxiliary dataset containing domains matched from <code>AOL-treated-unique-domains.csv</code> with domains and respective topics from <code>Google-Topics-Classification-v1.txt</code>.</li> <li><code>AOL-reduced-Citizen-Lab-Classification.csv</code>: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.<br>This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset.</li> <li><code>AOL-reduced-Google-Topics-Classification-v1.csv</code>: This dataset can be used for analyses of browsing history vulnerability and utility, as enabled by third-party cookies, and for analyses of topics of interest vulnerability and utility, as enabled by the Topics API. It contains singletons and the outlier that are dropped in some analyses.<br>This dataset can be used for analyses including the (data-dependent) randomness of trimming-down or filling-up the top-s sets of topics for each individual so each set has s topics. Privacy results for Generalization and utility results for Generalization, Bounded Noise, and Differential Privacy are expected to slightly vary with each run of the analyses over this dataset.</li> <li><code>AOL-experimental.csv</code>: This dataset can be used to empirically verify code correctness for <a href="https://doi.org/10.5281/zenodo.11229402" target="_blank" rel="noopener">10.5281/zenodo.11229402</a>. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset.</li> <li><code>AOL-experimental-Citizen-Lab-Classification.csv</code>: This dataset can be used to empirically verify code correctness for <a href="https://doi.org/10.5281/zenodo.11229402" target="_blank" rel="noopener">10.5281/zenodo.11229402</a>. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset.</li> <li><code>AOL-experimental-Google-Topics-Classification-v1.csv</code>: This dataset can be used to empirically verify code correctness for <a href="https://doi.org/10.5281/zenodo.11229402" target="_blank" rel="noopener">10.5281/zenodo.11229402</a>. All privacy and utility results are expected to remain the same with each run of the analyses over this dataset.</li> </ol> <p><strong>License</strong></p> <p><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="noopener">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International</a>.</p>
ScienceDex guides
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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.