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15,702 results for “history”
Kinetochore life histories reveal an Aurora B dependent error correction mechanism in anaphase
<p>Dataset of kinetochore tracks in human RPE1 cells showing chromosome dynamics and segregation from prometaphase through to anaphase as described in detail in Sen, Harrison, Burroughs and McAinsh, 2021, https://doi.org/10.1101/2021.03.30.436326 Tracks correspond to 3D time-lapse movies of Ndc80-eGFP and were acquired in the 488nm channel using 1\% laser power, 50 ms exposure time/z-plane, 93 z-planes, 307 nm z-step, which results in 4.7 s/z-stack time frame. Cells are subject to nocodazole arrest-and-release or equivalent treatment with DMSO as indicated in the folder names, and some cells are subject to additional treatment with ZM to inhibit Aurora B (also indicated in folder names). Tracks were produced using kinetochore tracking software, KiT v2.3 (see Armond et al., 2016, Bioinformatics), available from https://github.com/cmcb-warwick/KiT/ </p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes
<p>Tree sequences inferred for the 1000 Genomes phase 3 autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("1kg_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("1kg_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Oral history transcripts from the H.J. Andrews Experimental Forest Program, 1996 to 2018
Oral history interviews have been conducted over the last two decades with members of the Andrews Forest community who provided valuable historical information about the program and related issues. On the occasion of the 50th anniversary of the experimental forest (1998), history professor Max Geier (Western Oregon University) conducted 33 interviews with individuals (or pairs of people) and five research groups from 1996-1998. About 20 years later (2013-2018) historian Sam Schmieding (Oregon State University) conducted an additional 10 oral histories, including some with people who had been interviewed by Geier 20 years earlier. Several additional relevant oral histories with people who have been important in the history of the Andrews Forest have been conducted and are also included in this collection. This data package includes an inventory and transcripts of these oral history interviews including brief biosketches of interviewees.
Transportation network system including trails, road construction history, and gates for the Andrews Experimental Forest, 1952-2011
Transportation network locations within the Andrews Experimental Forest. Includes locations of all the roads, trails, and gates within and around the forest. Original road layer was drawn on maps in 1992 and field validated. The road construction history (1952-1990) has been captured as an attribute. Roads were updated in 2004 to include roads that have been abandoned. Gates were field checked in 2004, as well as trail locations. The three data sets were updated after the 2008 LiDAR data was delivered. Roads were digitized on-screen from the bare-earth DEM, and gates were moved to match the new road network. Trails were updated for the 2011 Andrews map update. Many were located through GPS, and new trails were added. The original data is represented, as well as the updated datasets. The road network dataset is in an esri file geodatabase format, and the other datasets are in esri shapefile format, and all are in a zipped file format.
Black-throated Blue Warbler capture histories, Hubbard Brook Experimental Forest
This dataset provides body measurements and encounter histories for black-throated blue warblers. Birds were captured in mist nets, given unique combinations of colored leg bands and a numbered, aluminium USGS leg band, and aged as either yearlings or older breeders based on plumage characters. Standard body measurements were taken, following Pyle 1997 (Pyle, P. 1997. Identification guide to North American birds. Slate Creek Press, Bolinas, CA). All birds were released unharmed after banding and measurements were completed. Capture histories were generated from resightings of banded individuals on three gridded study plots at the HBEF: low elevation (250-350 m; 85 ha), middle elevation (450-600 m; 65 ha), and high elevation (750-850 m; 35 ha). See Rodenhouse et al. 2003 for plot details. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Data have been described and published in: Rodenhouse, N. L., Sillett, T. S., Doran, P. J., & Holmes, R. T. (2003). Multiple density-dependence mechanisms regulate a migratory bird population during the breeding season. Proceedings. Biological sciences, 270(1529), 2105–2110. https://doi.org/10.1098/rspb.2003.2438 Sillett, T. S., & Holmes, R. T. (2002). Variation in Survivorship of a Migratory Songbird throughout Its Annual Cycle. Journal of Animal Ecology, 71(2), 296–308. http://www.jstor.org/stable/2693447
SMB01 Variation in soil respiration and bacterial community due to species-specific plant-soil history at konza prairie
We conducted a “home vs. away” plant-soil feedback greenhouse experiment using two C3 grass species (Bromus inermis and Pascopyrum smithii) grown in soil collected from Konza Prairie. We used a closed-circuit CO2 trapping method and isotopic analysis to differentiate between root-derived and SOM-derived CO2 production. We investigated how soil chemistry and soil bacterial communities differed in soils with a history of B. inermis vs soils with a history of P. smithii.
GIS05 GIS Coverages Defining Konza Prairie Burn History (1977-present)
This dataset contains a comprehensive record of burn histories for the Konza Prairie Biological Station (KPBS) dating from 1972. Burn history data contains date burned, area burned and type of treatment (prescribed burns, complete and partial burns, and wildfires). These data are available as zipped (.zip) shapefiles (.shp).
GIS13 GIS Coverages Defining Konza Wildfire and Supplementary Burn History (1977-present)
This dataset contains a comprehensive record of supplemental burns, wildfires, wildfire cleanup burns for the Konza Prairie Biological Station (KPBS) dating from 1972. Burn history data contains date burned, area burned and type of treatment (wildfires, wildfire cleanup, and supplemental burns). Burn histories for planned, prescribed burns are available in dataset GIS05. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology
<p>What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators.</p> <p>Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories.</p> <p>Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography.</p> <p><strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong></p>
Potential effects of invasive plants on mosquito life-history traits.
<p>Invasive plants offer suitable oviposition sites for some vector species (a); invasive plant litter increases proliferation of immature vectors (b); dense canopy cover or thickets of invasive plants provide suitable micro-habitats for adult mosquitoes (c); nectariferous flowers (d) and extra-floral glands (e) of invasive plants are important sugar sources for adult vectors; invasive plants can influence the pathogen transmission ability of the vector (f).</p> <p>A grey-scaled version was published as Figure 1 in <a href="https://doi.org/10.3390/v13010032">Agha et al. (2020)</a>.</p> <p>Required software: <a href="https://krita.org/">Krita</a> and <a href="https://www.gimp.org/">Gimp</a>.</p>
Historical uncertainty in Gregory of Tours's History of the Franks (book 7)
<p>Our goal was to create a research dataset based on geographical and chronological uncertainties in the work of Gregory of Tours's *History of the Franks* (book 7). We used and modified a topology of geographical and chronological uncertainty based on a rudimentary schema that would be universal when analysing an historical source :</p> <p>Chronological :<br> * uncertain dating<br> * uncertain method of dating<br> * lack of dating<br> * precise dating</p> <p>Geographical: <br> * uncertain location<br> * general location (region, country)<br> * lack of location <br> * precise location</p> <p>After working on book 7 for a while, that schema was reworked as those 9 types of uncertainty : </p> <p>Chronological :<br> * uncertain_dating<br> * uncertain_method_dating<br> * event_dating_null<br> * precise_dating</p> <p>Geographical: <br> * uncertain_location<br> * general_location <br> * event_location_null<br> * uncertain_method_location<br> * precise_location</p> <p><br> The geographical and chronological focus makes it possible to identify where and when, in a source, the historical uncertainty is higher. </p> <p>Using python, that dataset was then automatically cleaned and enhanced with bounding box based on geo-mapping information for the entries of geographical uncertainty. Those were classified as either precise_location or general_location. </p> <p>For example, anything relating to a city general area (like "in the Rouen area") creates a general_location bounding box encompassing the *current* geographical space occupied by the municipality of Rouen (in the format 'LongMin', 'LongMax', 'LatMin', 'LatMax' in a single column "bbox"). Anything described as a unique point in space (like "in Paris") creates a precise_location and its corresponding lat/long system of coordinates. </p> <p>This is an arbitrary way to translate slightly undefined geographical concepts of uncertainty into formal data, but at least it can be fully explained explicitly.<br> </p> <p>Translation used: Tours G. <em>et alii</em>, <em>The history of the franks</em>, Penguin Books Limited, 1974, <a href="https://books.google.ch/books?id=4Lx-M2RHGgoC">https://books.google.ch/books?id=4Lx-M2RHGgoC</a>.</p>
Supplement for "Using Phylogenetic Networks to Model Chinese Dialect History"
<p>This is the supplementary material accompanying the paper "Using Phylogenetic Networks to Model Chinese Dialect History", which appeared in 2014 in "Language Dynamics and Change" (volume 4, issue 2).</p>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Unique demographic history and population substructure among the Coorgs of Southern India
<p>Quality filtered GSA data of the individuals analysed in Mukhopadhyay et al., 2024 from Coorg, Karnataka, India.</p> <p> </p>
Phlorest phylogeny derived from Lee 2015 'A Sketch of Language History in the Korean Peninsula'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee S (2015) A Sketch of Language History in the Korean Peninsula. PLoS ONE 10(5): e0128448. doi:10.1371/journal.pone.0128448</p> </blockquote>
Phlorest phylogeny derived from Chacon & List 2015 'Improved computational models of sound change shed light on the history of the Tukanoan languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chacon TC, List J-M (2015) Improved computational models of sound change shed light on the history of the Tukanoan languages. Journal of Language Relationship, 3:177–203.</p> </blockquote>
Phlorest phylogeny derived from Honkola et al. 2013 'Cultural and climatic changes shape the evolutionary history of the Uralic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Honkola T, Vesakoski O, Korhonen K, Lehtinen J, Syrjänen K & Wahlberg N. 2013. Cultural and climatic changes shape the evolutionary history of the Uralic languages. Journal of Evolutionary Biology, 26(6):1244–1253.</p> </blockquote>
Supplementary files for: A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle
<p>Supplementary materials for the Earth-science Reviews paper 'A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle'. </p> <p>Supplementary Material 1 – Palaeomagnetic pole list for the ca. 2000–540 Ma interval.<br> Supplementary Material 2 – IGCP 440 pre-700 Ma geotectonic database (with minor corrections made) in shapefiles format<br> Supplementary Material 3 – Neoproterozoic sedimentary facies point data of Li et al. (2013) in shapefile format<br> Supplementary Material 4 – Generalised global large igneous province (LIP) database for 2010–0 Ma (after Ernst et al., 2021) in both shapefile and Excel formats<br> Supplementary Material 5 – Global passive margin database of (Bradley, 2008) in shapefile format<br> Supplementary Material 6 – Global orogen database of Condie et al. (2021) with minor modifications and in shapefile format<br> Supplementary Material 7 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ia (0-90W-0)<br> Supplementary Material 8 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ib (0-90E-0)<br> Supplementary Material 9 – 2000–540 Ma global animation highlighting the occurrence of LIP events in time and space, including possible plume centres.<br> Supplementary Material 10 – GPlates project files for the two alternative global 2000–540 Ma full-plate animations with associated geotectonic databases</p>
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.