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29,145 results for “Association”
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Opioid medication use and blood DNA methylation: epigenome-wide association meta-analysis
<p>We conducted the first large-scale epigenome-wide meta-analysis of blood DNA methylation and recent use of opioid medications. There were five participating studies (10,842 individuals; 9,886 European ancestry and 956 African ancestry participants) including four that used the newer Illumina EPIC/850K array and one that used the older Illumina 450K array. We identified novel loci differentially methylated in relation to opioid medication use.</p>
Spatiotemporal dynamics in freshwater amphipod assemblages are associated with surrounding terrestrial land use type - Dataset
<p>Biological assemblages are the result of dynamic processes that have explicit temporal and spatial dimensions. While biodiversity patterns can be directly inferred from the structure of these assemblages, an assessment of changes through time and space is needed to understand how organisms initially assembled and how they are responding to local environmental and biotic factors. Small freshwater streams are particularly affected by contemporary anthropogenic activities and biological invasions, yet are commonly less studied, as studies often focus on lakes and large streams. Here, we conducted a spatially explicit analysis of keystone shredder assemblages across eight years in twelve replicated small tributary streams. In each stream, we monitored multiple sites per km stream length. By assessing temporal beta diversity dynamics, defined by the gain or loss of species or abundance-per-species at individual sites, we show that changes in amphipod assemblages occur within the context of the surrounding terrestrial matrix and reflect recent amphipod colonization history. While amphipod composition was mostly constant in streams located in forested catchments, streams embedded in catchments with more extensive agricultural land use displayed more pronounced temporal changes, either driven by colonization of unoccupied upstream locations, or by more pronounced but undirected fluctuations in gains and losses of species or abundance-per-species. Our study thus suggests that agricultural landscapes might destabilize aquatic amphipod assemblages, causing higher temporal changes in community structures, and highlighting the vulnerability of aquatic ecosystems to terrestrial land use drivers.</p>
Raw data files associated with the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees"
<p>These are the raw data CSV files associated with the results described in the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees".</p> <p>By Sara Hellström, Verena Strobl, Lars Straub, Wilhelm H. A. Osterman, Robert J. Paxton, Julia Osterman</p>
Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma
<p>The processed multiplexed IF data and sample ID corresponding to the raw data in record (https://zenodo.org/record/4300912#.Y-WGBuzMKY-). All code used to produce the results of this study are available at <a href="https://github.com/cBio-MSKCC/Halo_Melanoma_IL2">https://github.com/cBio-MSKCC/Halo_Melanoma_IL2</a>.</p>
X-ray tomographic datasets associated with the article "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography" (DOI: 10.1016/j.conbuildmat.2024.139091)
<p>This Zenodo repository provides two sets of 3D images, which constitute part of the dataset base for the article titled "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography", written by the same authors cited here, together with other co-authors. The article is published in the journal "Construction and Building Materials". It can be reached <em>via</em> the following URL: <a href="https://doi.org/10.1016/j.conbuildmat.2024.139091" target="_blank" rel="noopener">https://doi.org/10.1016/j.conbuildmat.2024.139091</a>.</p> <p>The core specimens were obtained in 2019 from semi-dense asphalt (SDA) pavement sections located in the Swiss Canton of Zürich. For each of three pavement sections, labelled in the following as SDA4-1yr, SD4-5yr and SDA8, 100 mm diameter cores were extracted, both inside (I) and outside (O) of the wheel path, in order to see the effect of the traffic load on the pore space characteristics. Out of the original cores for the SDA4 pavements, 5 30 mm diameter sub-cores were drilled out of their centers, both in- and out-of the wheel path, and investigated with X-ray tomography. Only 1 30 mm core was analyzed for SDA8, both in- and out- of the wheel path. The asphalt in that pavement type has lower porosity, making it less interesting from the sound absorption viewpoint.</p> <p>The whole dataset consists of .7z archive files. Such files have the following designations: SDA_J_K_L_Tomogram.7z or SDA_J_K_L_PoreSpaceBinTomogram.7z, where J = 1,2, K = I,O and L = 1,2,3,4,5. When referring to the specimen naming within the corresponding article, the first index, J, refers to the specimen "age": J = 1 indicates the 1-year old specimens (called SDA4-1yr within the article); J = 2 refers to the 5-year old ones (SDA4-5yr). The second index, K, refers to the location of the specimen within the pavement section course ("I" for in-wheel path and "O" for out-of-wheel path). The final index L just enumerates the distinct specimens of the same group.</p> <p>There are two additional groups of archive files: LNA_I_Tomogram.7z/LNA_I_PoreSpaceBinTomogram.7z refers to the single in-wheel-path, 7-year old specimen (called SDA8 within the article); LNA_O_Tomogram.7z/LNA_O_PoreSpaceBinTomogram.7z refers to the single out-of-wheel path, 7-year old specimen.</p> <p>The two sets/types of 3D images can be recognized by the different file naming.</p> <p>The first set includes the raw X-ray tomograms of the 22 specimens analyzed. Each tomogram is stored in the form of a "stack" (or series) of 16-bit unsigned integer 2D TIFF image file, being one 2D cross-section (also called "slice", in tomographic jargon) from the "tomographed" volume. Such slices are contained in a folder. The folder was then archived in a .7z archive file.</p> <p>The second set of 3D images is characterized by the filename pattern SDA_J_K_L_PoreSpaceBinTomogram.7z. Each zipped folder contains the slices of the binary tomogram of the whole pore space of the respective specimen, segmented according with the 3d image analysis workflow described within the article. Each slice of such tomogram was stored as a 8-bit unsigned integer 2D TIFF image file, whose pixels can have only two possible values: 255, if the pixel is inside the segmented pore space; 0 if the pixel is outside it.</p> <p>Almost all of the acquired tomograms have an isotropic voxel size of 0.0214 mm, meaning that each slice is separated in space from the next one by such distance. The samples SDA_2_O_1 and SDA_2_I_1 have a voxel size of 0.0220 mm, while the sample LNA_I has a voxel size of 0.0223 mm.</p>
Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130
<p>The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products.</p> <p>This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from:</p> <p>GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq</p> <p>The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using</p> <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> <p>with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 .</p> <p>the first 10 lines of gbifid.tsv.gz as extracted via</p> <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> <p>are:</p> <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> <p>Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via:</p> <p>https://gbif.org/occurrence/2997162320</p> <p>and</p> <p>https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771</p> <p>respectively.</p> <p>This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them. </p> <p>In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it. </p> <p>Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .</p>
A catalog of associated, machine-learning-derived phase arrival times for ten days of seismic data in the Yellowstone region
<p>This dataset contains the associated phase picks and event information from applying a deep learning phase picker to continuous data recorded over March 25 – April 3, 2014, on 20 three-component stations and 14 vertical-component stations in the Yellowstone region. This 10-day period contains an M<sub>w</sub> 4.8 event, the largest earthquake in the Yellowstone region since 1980. The catalog and deep learning phase picker are described in Armstrong et al. (submitted).</p> <p>The arrivals were associated using the method described by Baker et al. (2021) and located using HypoInverse2000 (Klein, 2002). There are 1,053 events in this catalog, including 855 that were previously unidentified. Events that also appear in the University of Utah Seismograph Stations catalog have an event identifier (evid) beginning with “6”, while new events begin with “9”. </p> <p>Columns include:</p> <ul> <li>A simple event number</li> <li>the network, station, channel, and location code for the arrival time</li> <li>the arrival time in UTC (arrival_time) and Unix (arrival_time_epoch) format</li> <li>any static correction applied to the arrival time</li> <li>the P-pick first motion polarity as determined by a machine learning model - up (1), down (-1), or unknown (0)</li> <li>the arrival time residual </li> <li>the take off angle in degrees </li> <li>the event latitude and longitude in degrees</li> <li>the event depth in km</li> <li>the event origin time in UTC (origin_time) and Unix (origin_time_epoch) format</li> <li>the azimuthal gap of the event in degrees</li> <li>the root mean square error (RMS) of the event location</li> <li>the event identifier (evid) - begins with a “6” for events in the UUSS catalog and a “9” for new events</li> </ul> <p> </p>
Occasional songs from the Royal Shooting Association in Copenhagen, Denmark (1782–1869)
<p>The data set is centered around a catalogue (enumerative bibliography) of occasional songs addressed to an illustrious recipient at parties organized by the Royal Copenhagen Shooting Association (Det Kongelige Kjøbenhavnske Skydeselskab og Danske Broderskab).</p> <p>A more detailed description will be found in the research article with the title "Increasing Access to Ephemeral Prints. How to Construct and Analyze a Dataset From the Golden Age of Literature in Nineteenth-Century Denmark" (submitted to the journal <em>Orbis Litterarum</em>, in August 2022).</p> <p>The dataset was also published as a printed Danish bibliography entitled</p> <p>Holger Berg: Kongesange og skydeviser fra Det Kongelige Kjøbenhavnske Skydeselskab og Danske Broderskab 1784-1869. Odense: Kle-Art, 2022. ISBN 978-87-92750-35-8</p>
Data for: Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease.
<p>Summary of Bivariate GWAS scan results reported in:<br> <a href="https://pubmed.ncbi.nlm.nih.gov/30525989/">Bivariate Genome-Wide Association Scan Identifies 6 Novel Loci Associated With Lipid Levels and Coronary Artery Disease. </a>Siewert KM, Voight BF. Circ Genom Precis Med. 2018 Dec;11(12):e002239. doi: 10.1161/CIRCGEN.118.002239.</p> <p>PMID: 30525989 </p>
Genome-wide association statistics of Hearing Problems
<p>Genome-wide Association Statistics of Hearing Problems</p> <p>Citation: De Angelis F, Zeleznik OA, Wendt FR, Pathak GA, Tylee DS, De Lillo A, Koller D, Cabrera-Mendoza B, Clifford RE, Maihofer AX, Nievergelt CM, Curhan GC, Curhan SG, Polimanti R. Sex differences in the polygenic architecture of hearing problems in adults. Genome Med. https://doi.org/10.1186/s13073-023-01186-3</p> <p>COLUMN HEADERS<br> chromosome: chromosome<br> base_pair_location: position<br> effect_allele: effect allele (corresponds to the effect size’s sign; may not be the alternate allele)<br> other_allele: non-effect allele<br> beta: effect measured as beta, sign corresponds to the effect of the effect allele<br> standard_error: standard error of the effect<br> effect_allele_frequency: effect allele frequency in UK Biobank participants of European descent<br> p_value: p value of the association statistic<br> variant_id: variant identifier<br> rs_id: rsID of the variant<br> n: sample size per variant</p> <p> </p>
Data tables associated with manuscript 'Grant et al., Regional amplified warming in the Southwest Pacific during the mid-Pliocene (3.3-3.0 Ma)'
<p>This repository holds the data files associated with manuscript Grant et al., Regional amplified warming in the Southwest Pacific during the mid-Pliocene (3.3-3.0 Ma), submitted to Climate of the Past. https://doi.org/10.5194/egusphere-2023-108 </p> <p> The R Script and R Data used to analyse the data and produce the figures can be found in GitHub repository https://github.com/GRG-GNS/Pliocene-SST-Southwest-Pacific.git</p> <p>###########Terms and units</p> <p>Sea Surface Temperatures (SSTs) are in degrees Celsius. Latitude are in degrees north. Longitude in degrees east.</p> <p> </p> <p>NZESM (New Zealand Earth system model; Williams et al., 2016), UKESM (United Kingdom Earth System model; Sellar et al., 2019), HadISST 1870-1879 AD (HadleyCentre Sea Ice and Sea Surface Temperature; NCAR, 2022), mPWP (mid-Pliocene Warm Period 3.3 - 3.0 Ma), MIS5e (Marine Isotope Stage 5e; 125 ka). SSP1 /2/ 3 (Socio-economic Pathways; IPCC). UK'37 - alkenone biomarker SST proxy TEX - TEX86 biomarker SST proxy</p> <p>Two periods were extracted from NZESM and UKESM i) 2036-2040 AD for SSP 2, and ii) 2090-2099 AD for SSP1, SSP 2, SSP 3. These are provided as stand alone values and in reference to HadiIST (e.g. NZESM - HadISST or NZESM.HadISST) </p> <p><strong>Table 2.</strong> <strong>Statistical distribution of mid-Pliocene Warm Period (3.3-3.0 Ma) Sea Surface Temperatures anomalies (SST; °C) relative to HadiSST (1870-1879 AD) using </strong> <strong>BAYSPLINE calibration (Tierney and Tingley, 2018). The total range is calculated as the difference between maximum and minimum temperature and represents glacial to interglacial extremes. </strong></p> <p><strong>Table 3. Site annual mean Sea Surface Temperature anomalies (SST; °C) for UKESM and NZESM with respect to HadISST (1870-1879 AD) for SSP2-4.5 2040 AD (2036–2045 AD). </strong></p> <p><strong>Table 4. Site annual mean Sea Surface Temperature anomalies (SST; °C)for UKESM and NZESM with respect to HadISST (1870-1879 AD) for SSP1-2.6, SSP2-4.5, SSP3-7.0 at 2095 AD (2090–2099 AD). </strong></p> <p><strong>Table A1. Comparison between </strong> <strong> derived SST using BAYSPLINE with TEX<sub>86</sub> -index SST calibrations of Schouten <em>et al</em>. (2002), Kim <em>et al</em>, (2010), OPTIMAL (Dunkley Jones <em>et al</em>., 2020) and BAYSPAR (Tierney and Tingley, 2015). </strong></p> <p><strong>Table S1. All site sea surface temperature (SST; °C) data used in results with index and calibrations of Müller98 (Müller <em>et al</em>., 1998) and BAYSPLINE (Tierney and Tingley, 2018). The proxy type and references are also provided.</strong></p> <p><strong>Table S2. Site sample data for analyses undertaken this study, including all and TEX<sub>86 </sub>index calculations and calibrations. References for calibrations are contained within column headers.</strong></p> <p><strong>Table S3. Seasonal and annual mean sea surface temperature (SST; °C) model outputs of HadISST (NCAR, 2022), UKESM (Sellar <em>et al</em>., 2019), NZESM (Williams <em>et al</em>., 2016) at the seven Southwest Pacific sites (DSDP 594, ODP 1172, ODP 1168, ODP 1125, ODP 1123, DSDP 593, DSDP 590) for SSP2 2040 AD (2036-2045 AD), and SSP1, 2, and 3 2095 AD (2090-2099 AD). Including UKESM and NZESM with respect to HadISST.</strong></p> <p><strong>Table S4. Site sea surface temperature (SST; °C) annual means and seasonal range for UKESM and NZESM SSP2-4.5 2036-2045 AD, with MPWP interglacial modal means and total glacial range (maximum to minimum SST). </strong></p> <p><strong>Table S5. Compiled sea surface temperature (SST; °C) interglacial means for MIS 5e (125kyr; Cortese et al., 2013) and mPWP (3.3-3.0 Ma) and model annual means for HadISST (1870-1879 AD), and SSP2-4.5 2090-2099 AD for UKESM, NZESM.</strong></p> <p> </p>
Data associated with the article "Evolution and phylogenetic distribution of endo-α-mannosidase"
<p>Data associated with the article "Evolution and phylogenetic distribution of endo-α-mannosidase"</p> <p>Changelog:</p> <p>version 1.1</p> <ul> <li>added <em>Tunicaraptor</em> motif analysis alignment</li> </ul> <p>version 1.0</p> <ul> <li>Initial release</li> </ul> <p> </p> <p>Funding statement: National Science Centre of Poland is acknowledged for funding of the project 2020/36/C/NZ8/00081, "The role of glycosylation in the emergence of animal multicellularity", which enabled the creation of this research output.</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Digital Assets for "Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey"
<p>These are morphological catalogs and trained <a href="https://github.com/aritraghsh09/GaMPEN">GaMPEN</a> models for Hyper Suprime-Cam galaxies. Please refer to <a href="https://gampen.readthedocs.io/en/latest/Public_data.html">https://gampen.readthedocs.io/en/latest/Public_data.html</a> and <a href="https://arxiv.org/abs/2212.00051">https://arxiv.org/abs/2212.00051</a> for more details about this data release. </p> <p> </p> <p><strong>Catalog Files</strong></p> <ol> <li>g_0_025_preds_summary.csv --> Structural parameter catalog for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_preds_summary.csv --> Structural parameter catalog for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_preds_summary.csv --> Structural parameter catalog for 0.50 < z < 0.75 HSC i-band galaxies </li> </ol> <p> </p> <p><strong>Trained PyTorch Model Files</strong></p> <ol> <li>g_0_025_real_data.pt --> Trained Model for z < 0.25 HSC g-band galaxies </li> <li>r_025_050_real_data.pt --> Trained Model for 0.25 < z < 0.50 HSC r-band galaxies </li> <li>i_050_075_real_data.pt --> Trained Model for 0.50 < z < 0.75 HSC i-band galaxies </li> <li>sim_g_0_025.pt --> Trained Model for Simulated z < 0.25 HSC g-band galaxies </li> <li>sim_r_025_050.pt --> Trained Model for Simulated 0.25 < z < 0.50 HSC r-band galaxies </li> <li>sim_i_050_075.pt --> Trained Model for Simulated 0.50 < z < 0.75 HSC i-band galaxies </li> </ol>
Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States
<p><strong>Description</strong></p> <p>This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request. </p> <p> </p> <p><strong>Abstract</strong></p> <p>Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.</p> <p> </p>
Simulated tracks and associated melting of 6912 small to giant Antarctic icebergs, September 1997 to December 2008
<p>We present a dataset of Antarctic iceberg drift tracks and melting that includes small, medium-sized, and giant tabular icebergs with a realistic size distribution. An iceberg model is initialized with 6912 observed iceberg positions and sizes around Antarctica. The dataset is the result of a 2017 study "A simulation of small to giant Antarctic iceberg evolution: Differential impact on climatology estimates" published in JGR:Oceans (<a href="https://doi.org/10.1002/2016JC012513">https://doi.org/10.1002/2016JC012513</a>).</p> <p>We simulate drift and lateral melt using iceberg-draft averaged ocean currents, temperature, and salinity. A new basal melting scheme, originally applied in ice shelf melting studies, uses in situ temperature, salinity, and relative velocities at an iceberg's bottom. Climatology estimates of Antarctic iceberg melting based on simulations of small (≤2.2 km), “small-to-medium-sized" (≤10 km), and small-to-giant icebergs (including icebergs >10 km) exhibit differential characteristics: successive inclusion of larger icebergs leads to a reduced seasonality of the iceberg meltwater flux and a shift of the mass input to the area north of 58°S, while less meltwater is released into the coastal areas. This suggests that estimates of meltwater input solely based on the simulation of small icebergs introduce a systematic meridional bias; they underestimate the northward mass transport and are, thus, closer to the rather crude treatment of iceberg melting as coastal runoff in models without an interactive iceberg model. Future ocean simulations will benefit from the improved meridional distribution of iceberg melt, especially in climate change scenarios where the impact of iceberg melt is likely to increase due to increased calving from the Antarctic ice sheet.</p> <p> </p>
Variant calls for 'Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration'
<p>A VCF of SNP calls used for input to GWAS in https://elifesciences.org/articles/26255</p>
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits
<p>Genetic association studies have identified hundreds of independent genetic signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a relevant T2D tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million variants. We anticipate that deep learning methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.</p>
Data associated with the Turbet et al. 2023 manuscript (GCM simulations of TRAPPIST-1b, c and d)
<p>GCM simulations of TRAPPIST-1b, c and d, assuming H2O- and CO2-dominated atmospheres (in netCDF format). The GCM simulations were performed with the Generic PCM, historically known as the LMD Generic GCM. The simulations were used in the Turbet et al. 2023 manuscript.</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.