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9,277 results for “ETS”
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
CLDF dataset reflecting Zariquiey, Blum et al.'s "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin", work in progress.
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zariquiey, Roberto and Blum, Frederic and Valenzuela, Pilar and Koile, Ezequiel and Blasi, Damian and Gray, Russell and List, Johann-Mattis. "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin" (work in progress).</p> </blockquote>
Menegoni et al. (2025) - Figs. C1, C2 and C3
<p>Supplementary figures representing the results of the Maximum Likelihood Estimator (MLE) based analysis of the discontinuity size and aperture of the different discontinuity sets performed using the FracPaQ functions (Healy et al., 2016; Rizzo et al., 2017). Figs. C1 and C2 refer to the discontinuity size of the discontinuities detected on the DOMs. Fig. C3 refers to the discontinuity aperture measured during fieldwork.</p> <p>Please, to more information refer to the following paper:</p> <div>Menegoni, N., Panara, Y., Greenwood, A., Mariani, D., Zanetti, A., & Hetényi, G. (in print). Fracture network characterisation of the Balmuccia peridotite using drone-based photogrammetry, implications for active-seismic site survey for scientific drilling. <em>Journal of Rock Mechanics and Geotechnical Engineering</em>. https://doi.org/10.1016/j.jrmge.2024.03.012</div>
CLDF dataset derived Zalizniak et al.'s "Database of Semantic Shifts" from 2024
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zalizniak A. et al (2024). Database of Semantic Shifts. Moscow: Institute of Linguistics, Russian Academy of Sciences. [Accessed on 05.02.2024]</p> </blockquote>
Wikipedia: wikipedia-et (Estonian)
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p><p></p>https://et.wikipedia.org/
Supporting data for "Global Model of Atmospheric Chlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, observation tables, and Python scripts for reproducing the analysis results/ figures presented in "Global Model of Atmospheric Chlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.</p>
Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts".
<p>Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts". The data set consists of five folders: “Codon_usage_amoebae_and_viruses”, "Genome_annotations_amoebae", "Phylogenetic_trees_18S_amoebae", “Phylogenomic_trees_amoebae”, and "Viral_integration_detection_amoebae". The “Codon_usage_amoebae_and_viruses” folder contains for each amoeba host the calculated codon usage tables in the subfolder "codon_usage_table_host", the calculated codon usage preferences using different scores in the subfolder "codon_usage_scores_host", and the calculated codon usage preferences of giant viruses versus each host in the subfolder "codon_usage_scores_viruses_vs_host". The giant viruses in the subfolder "codon_usage_scores_viruses_vs_host" are organised by viral family and genus in separate sub-subfolders. The "Genome_annotations_amoebae" folder contains the generated genome annotations in different formats and the manually curated mitochondrial genome annotations for each amoeba host. The "Phylogenetic_trees_18S_amoebae" contains for the eukaryotic phyla <em>Discosea</em>, <em>Heterolobosea</em>, and <em>Tubulinea, </em>the 18S rRNA nucleotide alignments, distance matrices, and computed phylogenetic trees. The folder "Phylogenomic_trees_amoebae" contains for the eukaryotic clades <em>Amoebozoa</em> and <em>Discoba, </em>the protein alignment matrices and computed phylogenomic trees. The folder "Viral_integration_detection_amoebae" contains the MCP databases used (fasta file, alignment file, HMM profile and DIAMOND BLASTX database) and the MCP sequences detected in this study and the blast results of these. </p>
Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)
<p><strong>Supportive data for the eLife version of record.</strong></p> <p><strong>(1) Data used for the Michaelis Menten Kinetics.</strong></p> <p><strong>HPLC-based assay.</strong> Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH<sub>2</sub>PO<sub>4</sub>/K<sub>2</sub>HPO<sub>4</sub> pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine K<sub>M</sub> (±s.e.) and k<sub>cat</sub> (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline).</p> <p><strong>Charcoal GTP hydrolysis assay. </strong>The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-<sup>32</sup>P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl<sub>2</sub>, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above.</p> <p><strong>(2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2.</strong></p> <p>MP was performed as described in (Guaitoli et al., 2023).<strong> </strong>Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates.</p> <p><strong>(3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg.</strong></p> <p>Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023).</p> <p><strong>(4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024)</strong></p> <p>Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly,<strong> </strong>HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification.</p> <p><strong>Figure 6 Source Data 1:</strong> <span>Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to </span>LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were <span>used for the quantification shown in Figure 6.</span></p>
CLDF dataset derived from Liu et al.'s "Comparative Wordlist of Newari Varieties" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Liu, Zhenyang; Jacques, Guillaume; List, Johann-Mattis (2023): Creating a Standardized Comparative Wordlist of Newari Varieties. (26/07/2023), URL: https://calc.hypotheses.org/6269.</p> </blockquote>
CLDF dataset derived from Ugarte et al.'s "NorthPeruLex - A Lexical Dataset of Small Language Families and Isolates from Northern Peru (forthcoming).
<p>Cite the source of the dataset as:</p> <blockquote> <p>Ugarte, Carlos and Blum, Frederic and Ingunza, Adriano and Gonzales, Rosa and Peña, Jaime. Forthcoming. NorthPeruLex - A Lexical Dataset of Small Language Families and Isolates from Northern Peru.</p> </blockquote>
CLDF Dataset derived from Bender et al. 2003 "Proto-Micronesian Reconstructions"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—1. Oceanic Linguistics Vol. 42(1), 1-110, DOI: 10.2307/3623449</p> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—2. Oceanic Linguistics Vol. 42(2), 271-358, DOI: 10.2307/3623243</p> </blockquote>
Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.
<p>This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.</p> <p> </p> <p>===============================================================================<br>===============================================================================<br>Overview<br>===============================================================================<br>===============================================================================</p> <p>Data and model products associated with the manuscript "Mammal niches are not <br>conserved over continental scales" by Goldstein et al. </p> <p>Files are organized into two subdirectories. The first, "model_inputs/", <br>contains 8 data files intended to be used as part of the reproducible code <br>repository at https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible. <br>The second subdirectory, "model_outputs/", contains modeled products giving<br>estimated spatially varying niche relationships and predictions of relative<br>abundance.</p> <p>Below, we describe the contents of each file type. See the main manuscript <br>for full methodology, data sources, and discussions of spatial scales.</p> <p>NOTE: Version 1 of this dataset contained some errors that have been corrected<br>in Version 2. Version 2 was used as the input dataset for the analyses in the<br>associated manuscript. Version 1 should not be used.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 1: "model_inputs/"<br>===============================================================================<br>===============================================================================</p> <p>Two versions of each of four files are provided, corresponding to analyses <br>that do or do not consider ancient genetic lineages as potential sources of <br>spatial nonstationarity in mammal niches. Each file type is formatted the same,<br>and the versions are differentiated by either the suffix "nolineage" or <br>"lineage" in the filename. </p> <p>===============================================================================<br>File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv<br>===============================================================================<br>These files are .csvs giving spatial covariate data for each scale 2 cell<br>in North America, summarized to 5000 m. All percentage values are given in <br>10ths of a percent (scale of 0-1000). The following columns are provided:</p> <p>- grid_cell: Scale 2 cell ID<br>- Arable: pct arable land (Jung et al. 2020)<br>- EVI_mean: mean enhanced vegetation index (Didan 2021)<br>- EVI_Q95: 95th quantile of EVI (Didan 2021)<br>- Forest: Pct forest cover (Jung et al. 2020)<br>- Grassland: pct grassland (Jung et al. 2020)<br>- Pastureland: pct pastureland (Jung et al. 2020)<br>- Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018)<br>- Precipitation: avg annual precip. (Vega et al. 2017)<br>- Shrubland: pct shrubland (Jung et al. 2020)<br>- Temp_max: Average maximum daily temperature (Vega et al. 2017)<br>- Terrain_roughness (Amatulli et al. 2018)<br>- Wetlands: pct wetlands (Jung et al. 2020)<br>- is_land: Whether or not the cell is on land vs. ocean, used for filtering<br>- Agriculture: Pct. agricultural land (Jung et al. 2020)<br>- Pop_den_sqrt: Square root of human population density (CIESIN 2018)<br>- EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)</p> <p> </p> <p>===============================================================================<br>File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv<br>===============================================================================</p> <p>These files give summaries of iNaturalist sampling effort and detections<br>for target species. The following columns are provided:</p> <p>- grid_cell: Scale 3 cell ID<br>- n: Total iNaturalist effort in the cell (number of obs. of all mammals)<br>- The remaining columns are named for species. Each column gives the count<br> of observations of the species in the cell.</p> <p>===============================================================================<br>File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS<br>===============================================================================</p> <p>The ct_datlist files contain R objects that are lists of lists. These objects ultimately<br>contain all of the camera detection histories and camera-level covariate data used<br>in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package<br>to organize these detection data.</p> <p>Each outer list is of length equal to the number of species. The ith element of each<br>list contains the following named slots:</p> <p>- species: a string giving the name of the ith species<br>- umf: an unmarkedFrameOccu object. This object has three important slots:<br> - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating<br> whether the target species was observed in that 10-day window;<br> - siteCovs: a (# deployments) x 2 data frame with the following columns:<br> - site_ID: A unique ID of the exact location, shared by deployments with the same<br> coordinates<br> - subproject_ID: A unique ID indicating which camera array is associated <br> with this deployment<br> - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:<br> - year: the year of survey, relative to 2020 (zero-year is 2020)<br> - yday_scaled: the (scaled) Julian date of the beginning of the window<br> - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect<br> - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)<br> - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)<br> - obs_len_scaled: Scaled duration of window, to account for some windows <br> being cut off at < 10 days<br>- coords: a data frame. Originally, this file gave the exact position for each camera,<br> but these exact locations have been scrubbed for privacy. See the original sources<br> cited in the manuscript for full details. This data frame contains the following column:<br> - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera</p> <p>===============================================================================<br>File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv<br>===============================================================================</p> <p>These files are used for bookkeeping to track the relationships between the <br>three spatial scales in this study. Each row corresponds to a single "scale 2"<br>cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each<br>species indicating whether and where it is in the species' range. </p> <p>Note that the scale names in the code don't match the manuscript. In the code,<br>"scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity<br>grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km<br>SVC grid.</p> <p>The following columns are provided:<br>- scale2_grid_ID: unique ID for each cell in the 5 km intensity grid<br>- scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation<br>- scale4_grid_ID: unique ID for each cell in the 100 km SVC grid<br>- GRID_ID_[species]: for each species, a column is provided on the S4 scale<br> counting each cell in the species' modeled range. NAs<br> indicate that the S2 cell defined in the row is not<br> included in the species' modeled range.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 2: "model_outputs/"<br>===============================================================================<br>===============================================================================</p> <p>===============================================================================<br>File 1: svc_estimates.csv<br>===============================================================================</p> <p>This file gives an estimate of the effect of each covariate on each species'<br>intensity, and the uncertainty in that estimate, for each species/covariate<br>pair. Results correspond to lineage models for species with phylogeographies<br>and non-lineage species otherwise. Each row represents the effect of one <br>covariate on one species' relative intensity process within one 100 km cell g. <br>Note that many estimates of beta_g are uncertain even for strong spatial <br>effects---the model is often confident that a spatial process is supported <br>while estimates of the realized process are uncertain.</p> <p>The following columns are provided:<br>- x: the x-coordinate of the 100 km cell<br>- y: the y-coordinate of the 100 km cell<br>- species<br>- parname: the name of the covariate<br>- mean: the mean of the posterior samples of beta_g<br>- 2.5%: the 2.5th quantile of the posterior samples of beta_g<br>- 50%: the 50th quantile of the posterior samples of beta_g<br>- 97.5%: the 97.5th quantile of the posterior samples of beta_g</p> <p>The following spatial projection is used to define X/Y coordinates:<br>"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83"</p> <p>===============================================================================<br>File 2: predicted_intensity.tif<br>===============================================================================</p> <p>This file contains a raster "brick" giving the predicted intensity surface <br>and uncertainty in this surface for each species. All predictions are generated<br>using models that do *not* account for lineage information---this means that <br>predictions for species with lineages are not from the models reported in the<br>main manuscript. The reason for this is that we found that lineages were <br>overall unsupported, so better predictions can be arrived at by excluding this<br>source of uncertainty in the underlying intensity process.</p> <p>The raster brick has 66 layers. Each layer provides either the mean predicted<br>log intensity in each grid cell across the species range or else provides<br>the standard error of that predicted log intensity. Layer names indicate output<br>type and species associated with each layer.</p> <p><br>===============================================================================<br>===============================================================================<br>References<br>===============================================================================<br>===============================================================================</p> <p>Camera data are obtained from the following sources, which can be consulted to<br>obtain the original raw camera data</p> <p>- Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353.<br>- Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775.<br>- Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318.<br>- Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024).<br>- Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252.<br>- Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023).<br>- Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372.<br>- Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29.<br>- McMurry, S. et al. In review (2024).<br>- Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287. <br>- Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295.<br>- McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29.<br>- McMurry, S., Parsons, A., Lasky, M., Luongo, K., Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K., Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29.</p> <p>Covariate data are obtained from the following sources:<br>- Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).<br>- Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).<br>- Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).<br>- Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F.<br>- Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021).<br>- Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).<br>- Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).<br>- Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024).</p> <p>iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).</p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
'Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise' by Chen Zhao et al.
<p>Code and data used to produce figures in "Subglacial Water Amplifies Antarctic Contributions to Sea-Level Rise" by Chen Zhao et al.</p> <p>We are happy to help with anybody with any problems with this code, please get in touch (chen.zhao@utas.edu.au) or raise an issue.</p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
CLDF dataset derived from Deepadung et al.'s "Lexical Comparison of Palaung Dialects" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Deepadung, Sujaritlak; Buakaw, Supakit; and Rattanapitak, Ampica (2015): A lexical comparison of the Palaung dialects spoken in China, Myanmar, and Thailand. Mon-Khmer Studies 44. 19-38.</p> </blockquote>
Data for Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. DOI:10.1093/jmammal/gyab153
<p>Data sets used in Mellado et al. The impacts of marking on bats: mark-recapture models for assessing injury rates and tag loss. Journal of Mammalogy. 103:100-110. (https://doi.org/10.1093/jmammal/gyab153)</p> <p>File Descriptions:</p> <p>CapHistTagLoss.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with both tags (B), arm bands (A), collar (C), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> CaptHistTagInj.txt - Capture histories for <em>Carollia perspicillata</em> identifying if individual was captured with no lesions from arm band (A), minor injury (I), major injury (M), not captured (0) or not monitored (dot). Covariates included are Sex, Forearm Length and Scaled Mass Index.<br> LesionOccurrence.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of lesions of any type due to arm bands.<br> RingCondition.txt - Censored time-to-event data for survival analysis. Recorded events were the occurrence of damage to arm bands.<br> SMI.txt - Longitudinal data for individual <em>Carollia perspicillata</em> Scaled Mass Index, identifying individual records, the occurrence of lesions, sex, month, year</p> <p> </p>
Dataset for journal article: Nava et al. (2021) "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient"
<p>Dataset and R script for the article "Microalgae colonization of different microplastic polymers in experimental mesocosms across an environmental gradient" by Nava V., Matias M., Castillo-Escrivà A., Messyasz B. and Leoni B. accepted by Global Change Biology.</p>
CLDF dataset derived from Hartmann et al.'s "Valency Patterns Leipzig" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hartmann, Iren & Haspelmath, Martin & Taylor, Bradley (eds.) 2013. Valency Patterns Leipzig. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://valpal.info)</p> </blockquote>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </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.