Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,393
datasets available to search
ShareScore release 0.7.1
Dataset results
5,393 results for “weight”
Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population
<h1>Manipulating a host-native microbial strain compensates for low microbial diversity by increasing weight gain in a wild bird population</h1> <h1> </h1> <p>These files contain data on bacteria present in the guts of wild great tit (Parus major) obtained from faecal samples and sequenced using Illumina MiSeq. These data resulted from an experiment which provided supplementary mealworms at the nest during the breeding season at number of woodland sites in Cork, Ireland. Approximately half of these nests were given mealworms covered in a freeze dried bacterial powder containing the bacteria Lactobacillus kimchicus, which had been isolated from great tit faeces from the previous season. This treatment aimed to disrupt the gut microbiota of the treatment birds in order to provide evidence for the gut microbiotas role in birds health and fitness. Included here are the 3 elements necessary to create a 'phyloseq object' containing the sample metadata, ASV (Amplicon Sequence Variant) count table and a taxonomy table. The metadata file includes the alpha diversity scores for each individual. The data include all negative control samples taken during sample collection and library preparation, which were removed before the main analyses. All analyses, except for the beta-diversity analyses, were conducted in R. All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p> <h2> </h2> <h2>## Description of the data and file structure </h2> <p>Taxonomy, ASV and metadata files required to create a phyloseq object in R. metadata.csv file contains data on individual birds (i.e. individual samples). The metadata includes descriptions of the bird itself and it's environment, namely:</p> <ul> <li>Rownames: unique sample ID for each sample, corresponds with asvTable.csv. </li> <li>Nest: unique identifier for the nest box associated with the bird being sampled. </li> <li>Sample.ID: unique identifier for the faecal sample or control sample.</li> <li>Bird.ID: Identity of the bird the sample came from, note some individuals sampled twice so some bird.ID's may reoccur in metadata with different Sample.ID.</li> <li>Date: Date the sample was taken dd/mm/yyyy.</li> <li>Day: Date the sample was taken, in days since 1st March.</li> <li>Ring.Mark: British Trust for Ornithology (BTO) metal ring ID where applicable. Birds only ringed at D15 so some young birds do not have IDRings.</li> <li>Site: ID of woodland site that bird was sampled at.</li> <li>Chick.LetterID: ID letter differentiates between different birds from the same nest. Either 'A'-'F' for nestlings, 'Fe' for females or 'M' for males.</li> <li>Age.code: BTO age code.</li> <li>Age.category: Age category that bird is in. D8 = 8 days post hatching, D15 = 15 days post hatching, adult = 1+ years post hatching.</li> <li>Sex: Bird's sex, only determined for adult birds. Fe = Female, M = Male.</li> <li>Wing_mm: Wing length in mm.</li> <li>Tarsus_mm: minimum tarsus length of bird in mm.</li> <li>Weight_g: bird's weight in grams.</li> <li>Faecal.Sample: bird's age at sampling.</li> <li>newRing: whether bird was fitted with a new BTO ring. Only relevant to adults.</li> <li>Treatment: the experimental treatment group that the bird was in. Either 'Treatment' when nest given L. kimchicus treated mealworms or 'Control' when nest given plain mealworms.</li> <li>Notes: field notes.</li> <li>Main.sample: indicates whether this sample was the main sample to be used for analysis, an alternative sample taken as a backup.</li> <li>Plate: the ID of the PCR plate which the sample was amplified on.</li> <li>Azenta_noPeriod: sample ID given to sequencing facility without special characters. Corresponds to fastq files and ASV table counts.</li> <li>Qubit_prePool: samples qubit score before pooling.</li> <li>Date_extracted: date the sample was extracted on dd/mm/yyyy.</li> <li>SampleType: whehther the sample was a 'main' sample intended for downstream analysis, a 'control' sample for detecting contamination during library preparation, a 'duplicate' for detecting PCR issues, a 'label_error' where sample was suspected of being mislabelled at some point, a 'repeat' sample intended to detect errors or issues, a 'contam' sample which was suspected of being contaminated, a 'common' sample used across different PCR plates to detect issues. Extraction_notes: notes regarding the DNA extraction of the sample. </li> <li>LibPrep_notes: notes regarding the library preparation of the sample.</li> <li>Ring.Mark.lab: the ring or sample ID written on the sample tube, recorded to help detect mislabelling.</li> <li>Post_lab_notes: notes regarding issues found post sequencing.</li> <li>NumberOfReads: number of sequence reads associated with the sample. </li> <li>DistanceToEdge: distance between nest and woodland edge in metres. </li> <li>BroodSize.D8: number of nestlings in the nest at day-8 post hatching. </li> <li>BroodSize.D15: number of nestlings in the nest at day-15 post hatching.</li> <li>firstEggLayDate: Date the first egg in the clutch was laid, in days since 1st March.</li> <li>lastEggLayDate: Date the last egg in the clutch was laid, in days since 1st March.</li> <li>Observed: number of unique ASV's (or taxa) detected in the sample.</li> <li>Chao1: Chao1 diversity of the sample.</li> <li>Shannon: Shannon diversity of the sample.</li> </ul> <p>The file 'taxonomy.csv' contains the taxonomic breakdown of each bacterial Amplicon Sequence Variant (ASV) found in the dataset from Phylum to Species. Obtained by using the Naive Bayes Classifier against the Silva (v138) taxonomic database.</p> <p>The file 'asvTable.csv' contains counts of each amplicon sequence variant's occurrence for each individual sample. Samples are rows and taxa are columns.</p> <p> </p> <h2>Sharing/Access information </h2> <p>All R code is available on GitHub (https://github.com/shan-e-s\). Raw Sequence data are available in the European Nucleotide Archive under access number PRJEB74941, and ERS18960426-ERS18960697.</p>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Data for predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors
<p>The data was used in the analysis presented in the manuscript: Predicting piglet survival until weaning using birth weight and within-litter birth weight variation as easily measured proxy predictors. The manuscript is published in <em>Animal</em> journal. The data is for piglet survival survival at different time-points from birth to weaning from two research farms.</p>
Whole-tree weight and mensurational data for 13 Quercus montana, 12 Quercus rubra, 12 Acer saccharum, and 21 Betula lenta trees harvested between 2000 and 2022 from Black Rock Forest, Cornwall, NY.
Fifty-eight trees ranging from 1.5 to 54.2 centimeters diameter at breast height from four dominant forest tree species in Black Rock Forest were felled, sectioned, and weighed immediately. Subsections were then dried to determine a dry-to-wet-weight ratio for each tree, which was used to determine total dried aboveground biomass for each tree. Stumps and leaves were included. These data enabled construction of species-specific formulae for each species to predict total tree aboveground dry biomass from dbh measurements of live trees for these four species from around the Black Rock Forest region.
Fall 2000 soil organic content survey -- ash-free dry weight analysis for soil samples from 10 GCE LTER sampling sites
Soil core samples were collected from the permanent plots at 10 GCE LTER sampling sites in October, 2000, to survey the fractional organic content in marsh sediments. Surveys will be conducted annually to assess changes in soil organic content in response to environmental factors documented by other GCE monitoring efforts.
Criollo and Crossbred Steer Comparison: Weight Gain, Grazing, Carcass Quality, 2015-2017
Rarámuri Criollo cows have behavioral traits that are desirable for rangelands in arid environments, but calves from this biotype are difficult to market through conventional methods. One strategy to improve marketability is to crossbreed these cows with traditional beef breed bulls. However, it is unclear whether crossbred calves will achieve marketable weights and carcass qualities on rangeland and whether they will retain the desirable grazing behaviors of their mothers. We evaluated these traits for two cohorts of Rarámuri Criollo (JRC), Mexican Criollo (MC), and Criollo × beef-breed crossbred (XC) steers. Final live and carcass weights of XC were greater than JRC and MC, but all three groups were market ready at 30-mo after finishing on grass. Carcass quality and average daily gain did not differ among biotypes. Both JRC and XC steers exhibited grazing patterns similar to those previously observed in JRC cows. These results suggest JRC, MC, and XC steers can achieve desirable slaughter weights in 30 months using a rangeland-based grass-fed protocol, and JRC and XC steers retain desirable grazing behaviors of JRC cows.
BSNE aeolian dust collector sample weights from the three ConMod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains measurements of dust collected by BSNE collectors for the Connectivity Modifier (ConMod) Pilot study plots from 2008-2016 on the Jornada Experimental Range. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 plots. The plots are 8 x 8 meters and have an 8 x 8 buffer zone on both sides of the plot (up and down). There are four BSNE (aeolian dust collector) stands for each plot, 2 in each of the 2 buffer zones (8 collectors per plot). Each stand contains 2 BSNE collectors at a 30cm height with the collection opening at 10cm x 2 cm wide x 5 cm height. These BSNE collectors are in a fixed position pointing into the direction of the prevailing wind, which corresponds to the plot alignment. The collectors in the upwind buffer are facing away from the plot and the collectors in the downwind buffer are facing into the plot. Upwind BSNEs collect the amount of dust entering the plot, and the downwind BSNEs collect the amount of dust moving off the plot. These collectors estimate the effectiveness of the plot surface in obstructing wind blown dust. This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
Invertebrate dry weight data in various locations, mostly within the LEF at El Verde
Contains information [identity, counts, size, fresh and dry weights] of various collections of invertebrates (and the occasional amphibian or reptile), which has allowed formulae describing the relationship between length and dry wt. of invertebrates to be obtained. These have been used in estimating biomass in studies of bromeliad, heliconia and litter inveretebrates in the LEF. They will have a wider application. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Canopy Trimming Experiment (CTE) litterbag invertebrate counts and weights data
Identification, number and dry weight of invertebrates recovered from each litterbag. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Lake Wingra: Fish Lengths and Weights 1995 - current
Data are collected annually to enable us to track the fish assemblages of Lake Wingra. Sampling is done at six littoral zone sites per lake with a beach seine, minnow or crayfish traps, and fyke nets, while a boat-mounted electrofishing system samples four littoral transects. Vertically hung gill nets are used to obtain two pelagic samples per lake from the deepest point. A trammel net samples across the thermocline at two nearshore sites per lake. Fish are identified to species. Lengths are measured for all fish caught, while weight and scale are collected from a subset. Derived data includes catch per unit effort and size distribution by species, lake, and year. Sampling Frequency: annually. Number of sites: 1. Note that 2020 data does not exist due to insufficient sampling.
North Temperate Lakes LTER: Fish Lengths and Weights 1981 - current
Data are collected annually to enable us to track the fish assemblages of eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish). Sampling on Lakes Monona, Wingra, and Fish started in 1995; sampling on other lakes started in 1981. Sampling is done at six littoral zone sites per lake with seine, minnow or crayfish traps, and fyke nets; a boat-mounted electrofishing system samples four littoral transects. Vertically hung gill nets are used to obtain two pelagic samples per lake from the deepest point. A trammel net samples across the thermocline at two sites per lake. In the bog lakes only fyke nets and minnow traps are deployed. Parameters measured include species-level identification and lengths for all fish caught, and weight and scale samples from a subset. Dominant species vary from lake to lake. Perch, rockbass, and bluegill are common, with walleye, large and smallmouth bass, northern pike and muskellunge as major piscivores. Cisco have been present in the pelagic waters of four lakes, and an exotic species, rainbow smelt, is present in two. The bog lakes contain mudminnows. Beach seining was discontinued after the 2019 season. The only sampling done in 2020 were a single gill-netting replicate in Sparkling, Crystal, and Trout lakes. Sampling in Fish Lake was missed in 2021 due to significant lake level changes. Data from the two bogs is missing in 2022. Sampling Frequency: annually Number of sites: 11.
Size-fractionated zooplankton dry weight collected using a 1-m diameter ring net with 200-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The density of zooplankton dry weight for five size fractions was determined at Palmer LTER Stations B and E. Samples were collected with a 1-m diameter, 200-μm mesh ring net towed obliquely from the surface to a target depth of 50 m and back. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. One-half of the catch was size-fractionated with nested sieves into the following five size classes for biomass analysis: 0.2−0.5, 0.5−1, 1−2, 2−5, and >5 mm. Individual size fractions were concentrated on preweighed 200 μm mesh filters and frozen at −20°C until analysis. Samples were thawed, weighed to determine wet biomass, dried at 60°C for at least 24 h, and weighed again to determine dry biomass. Zooplankton density varies across size groups, seasonally, among years, and between sampling stations. Units of biomass density are milligrams dry weight per cubic meter.
Adelie penguin chick fledging weights, 1991-2024
The fundamental long-term objective of the seabird component of the Palmer LTER (PAL) has been to identify and understand the mechanistic processes that regulate the mean fitness (population growth rate) of regional penguin populations. Since the inception of PAL, Adélie penguin populations have effectively collapsed, gentoo penguin populations have increased dramatically and chinstrap penguin populations have remained relatively stable. These trends are spatially and temporally coherent with regional warming and decreasing sea ice duration. Adélie penguins are an ice-obligate polar species whose life history is intimately linked to the presence of sea ice, while chinstrap and gentoo penguins are ice-intolerant species whose life histories evolved in the sub-Antarctic, where sea ice is a less permanent feature of the marine ecosystem. The PAL study region includes five main islands on which Adélie penguin colonies have historically occurred, with each island containing a different number of spatially segregated sub-colonies. These colonies are censused to determine the total number of nests and chicks produced each year, and breeding success. Diet samples are acquired to understand diet composition (e.g., krill, fish) and krill length-frequencies. In general, krill constitute the most important component of the summer diets by mass of these three penguin species, but changes in PAL krill abundances have exhibited no long-term trends and thus far, have failed to explain the divergent patterns in penguin populations evident in our time series. Chick fledging masses are recorded as a cumulative measure of climate, weather, diet, and parental influences on chick health at the end of the breeding season. These data have provided valuable insights into the marine and terrestrial factors that influence Adélie penguin population fitness. No data were collected during the 2021-2022 season due to the Palmer Station pier rebuild.
SBC LTER: Effect of algal diet on consumption, growth, and gonad weight of the purple sea urchin (Strongylocentrotus pupuratus)
Data are for an experiment evaluating the effects of algal diet on consumption, growth, and gonad weight of an important kelp forest grazer, the purple sea urchin, Strongylocentrotus pupuratus over a 13 week period (November 2010 - February 2011). Four co-occurring species of macroalgae known to be part of its diet were offered: two kelps Macrocystis pyrifera and Pterygophora californica, and the red algae Chondracanthus corymbiferus and Rhodymenia californica. During 9 consectutive trials in a controlled laboratory setting, we measured consumption, test (exoskeleton) growth, jaw growth, change in whole body wet weight and gonad weight of urchins fed one of five experimental diets. The algae chosen represent a large proportion (> 75%) of the algal biomass in Santa Barbara Channel reefs (California, USA). Changes in the availability of these four species of macroalgae could have large implications for the performance of purple sea urchins and consequently, for the structure of subtidal reef communities. These data are presented in a paper describing the effects of five southern California macroalgal diets on the consumption, growth and gonad weight of the purple sea urchin: Matthew C. Foster, M., Byrnes, J. E. K., D. C. Reed. 2015. Effects of five southern California macroalgal diets on consumption, growth and gonad weight in the purple sea urchin, Strongylocentrotus purpuratus. PeerJ. DOI: 10.7717/peerj.719
Net Primary Productivity (NPP) Weight Data at the Sevilleta National Wildlife Refuge, New Mexico
Several long-term studies at the Sevilleta LTER measure net primary production (NPP) across ecosystems and treatments. Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production (ANPP) is the change in plant biomass, including loss to death and decomposition, over a given period of time. To measure this change, vegetation variables, including species composition and the cover and height of individuals, are sampled up to three times yearly (winter, spring, and fall) at permanent plots within a study site. The weight data presented here is obtained by harvesting a series of covers for species observed during plot sampling. These species are always harvested from habitat comparable to the plots in which they were recorded. This data is then used to make volumetric measurements of species and build regressions correlating biomass and volume. From these calculations, seasonal biomass and seasonal and annual NPP are determined.
DWCox: A Density-Weighted Cox Model for Outlier-Robust Prediction of Prostate Cancer Survival
<p>This package, <strong>DWCox</strong>, implements a <strong>d</strong>ensity-<strong>w</strong>eighted <strong>Cox</strong> regression model that is more robust against outliers in the training data. DWCox gives more accurate predictions than the standard Cox regression on prostate cancer survival, especially in cases where the training data are expected to contain a lot of outliers. More details can be found in our paper (coming soon) and the README file inside this package.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
MSAesm_ddG model weights
<h1><strong>Enhancing predictions of protein stability changes induced by single mutations using MSA-based language models</strong></h1> <h3>Francesca Cuturello, Marco Celoria, Alessio Ansuini, Alberto Cazzaniga</h3> <p><a href="https://watermark.silverchair.com/btae447.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA3IwggNuBgkqhkiG9w0BBwagggNfMIIDWwIBADCCA1QGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMDeX2Rv76EqcpUA0rAgEQgIIDJf31PCzsk_E39sxLn0jPXKqlbHEb51MkH6p0AWogxrFV-I_20bEk7TnvK4r-Tk2Sp38X7GKK1p6cOrz3Z5Yb5huEASMvYIjfbJpsZIgNtSt-pMxGay_uu4LDB-sD823PtruIv2R5uDcsfLOuaDvyx60MfyfrwjAE1l1f1b5DECOnnphWsbhn62EBTSCxL0wQge--GPnwcqSVxI8tyHqR3U_uYf7ceCudLk6BddUvquJH2J_4k-J9oglGMHI3oouETwsyPTApBkhksBZMa9ngI0OkJSvByE5TIysMEF-G6ljxgYQkiJcN_-7vCvpjrGzUAPNCpLjkQkKdWHMhqjzEuqIg51pdikc0-WWUh0T3ovo2De_Xi57C1YV3oJ2we409OYQdOnEQ13DoMBtOnpNyQ3DpO_LWBAZ1viFQsjBcLg5r15nvZun7n3ZKcnuuBHE9vP2NzA_ncFY9tKWMlX5OuVRWCgwL8-zcA3oc7h4N8hkDVWN3Qe6PbNVSj05-kpgvpKzF8tplG5ah98Kze9vMdowM2Q67YHavtN7O9in6gSIAgJYAblOXlIgIEBmSvqP0MQ0VdoxCqZa5Lb93LJTEo83c4HOHlc38FpkPTbTXhvZwd2lyStSRYfvz2_TfaDox9hTrHT0_oLCs4ChQ_3N-IM318yrjgJr6H65jsS_BiXRWReqhz9PP4Al5vKhENBuUEDyeOP2lzryGCV97L9ilL_QjRTQkk0JC-zGuk1ofRe4lKjRYxmXqvtjdIkyoQnYMRX2jAtVsp_IgtO9ugFrThqCpRDC6hYi4BslDl_ELKVnjBCMe4BQ1RKVs_ruDam7t0HRemWcPUtQwZzrepBULnfUgUYge8fQE6KcRQU99LUSArmQO50Im27JHP84NQMhmu7sSk4WxkI7zwVDTIS7Gr4lGtyO-kfZfivIOT2zsl7ne7c_fxgPDYX3PyHvPL0uMJxVzHvCa_gjSpO3kbUD77NkDJOxYcpV2c74iLgNaNSOMwgAlbN3Z2tGCpsWeqAk8Scl-ca5JXpMpdoCaBRTyxpNQJ0wf0NnM09V9iWDrxi8Vghqyznk">Published in <em>Bioinformatics</em>.</a></p> <p>For the code and licensing of this work, please refer to the GitHub repository: <a href="https://github.com/RitAreaSciencePark/PLM4Muts">https://github.com/RitAreaSciencePark/PLM4Muts</a></p> <p>Here only the weights of the fine-tuned MSA Tranformer are reported.</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.