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5,393 results for “weight”
Aeolian dust weights sampled by BSNE collectors quarterly from the CSIS study at Jornada Basin LTER, 2012-ongoing
This dataset contains weights of windblown dust collected by BSNE collectors at long-term observation plots that are part of the Jornada Basin LTER Cross-Scale Interaction Study (CSIS) located at the Jornada Experimental Range. There are 15 experimental blocks (or sites) in this study. Within each block, there are 4 plots with different experimental treatments: 1 control, 1 with mesquite herbicide applied, 1 with connectivity modifiers (Conmods) installed, and 1 with Conmods AND mesquite herbicide applied. The intent of Conmods is to decrease gap size between perennial vegetation. The plots are 8 x 8 meters and have an 8 x 8 meter buffer zone on both the upwind and downwind sides of the plot. There are two BSNE (aeolian dust collector) stands per experimental plot positioned at the edge of the upwind and downwind 8m x 8m buffers. Each stand has 3 collectors positioned at heights of 10 cm, 30 cm, and 50 cm, and all collector openings face the prevailing wind direction. Upwind BSNEs collected the amount of dust entering the plot, and the downwind BSNEs collected 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 ongoing with data collected quarterly each year.
Synthesized Dataset of Length-Weight Regression Coefficients for Delta Fish
This dataset is a compilation of length-weight regression coefficients for fish species commonly found in the freshwater tidal habitats of the San Francisco Estuary. This effort was born out of the Delta Smelt Resiliency Strategy Aquatic Weed Control Action study, which, in order to calculate fish biomass, needed to calculate individual fish weights from their measured lengths. The Aquatic Weed Control study was supported by Interagency Ecological Program through the Endangered Species Act and is included in the Interagency Ecological Program 2017-2019 workplan. Weight is estimated from length using the exponential function W=a\ L^b. These can be calculated using the linear regression of the log-transformed equation (log(W)=log(a)+b log(L)). This dataset provides the species-specific a and b parameters. Associated publication(s) and relevant metadata information are included. Data was obtained either via database (fishbase.us) or peer-reviewed scientific papers.
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
Open the record for dataset details and reuse information.
Resources to compute TF-IDF weightings on press articles and tweets
<p>These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the <a href="https://pypi.org/project/compute-tf-idf-vectors/">compute-tf-idf-vectors</a> program written in Python and available on Pypi.org.</p> <p>- features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english.</p> <p>- features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.</p>
Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</p>
T2-weighted Kidney MRI Segmentation
<p>A dataset containing 100 T<sub>2</sub>-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from Chronic Kidney Disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p> <p>For more information about the dataset please refer to <a href="https://doi.org/10.1002/mrm.28768">this article.</a></p> <p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to <a href="https://github.com/alexdaniel654/Renal_Segmentor">this software.</a></p>
Weight, sex, age, beam diameter, antler points and teat length for harvested deer from 1984-2025 in Black Rock Forest, Cornwall, NY.
Data from white-tailed deer harvested within Black Rock Forest, Cornwall, New York are collected annually. Trained staff measure mass, antler beam diameter, and teat length (since 2010), estimate age via dentition, count antler points, and assess sex on all field-dressed deer. Heart girth, measured as chest circumference, was recorded from 1984 to 1998.
Arctic Grayling length, weight and tag data from Arctic LTER Streams project, Toolik Filed Station Alaska, 1985 to 2018
Since 1983, the Streams Project at the Toolik Field Station has monitored physical, chemical, and biological parameters in a 5-km, fourth-order reach of the Kuparuk River near its intersection with the Dalton Highway and the Trans-Alaska Pipeline. In 1989, similar studies were begun on a 3.5-km, third-order reach of a second stream, Oksrukuyik Creek. Fish were collected on each river. Station locations, representing kilomter values certain distances from original phosphorus dripper (see method) were noted. 1985 to 2012 long-term tagging file for Arctic Grayling (Thymallus arcticus) on the Kuparuk River. All grayling adults and juveniles captured during the field season are measured, weighed, tagged and released. Grayling were tagged originally with a colored tag with a number. In 1993, researchers started pit tagging the grayling. These pit tags can be read with an antenna to track the migration of the grayling throughout the Kuparuk River system. Arctic grayling young-of-the-year (YOY) were caught multiple times during each summer and measured and weighed as well. This file combines the data from the following data sets: Dataset ID Short name 10325 1985-2012_Kuparuk_Grayling_Tags 10327 1986-2012_Kuparuk_YOY 10329 1989-2011_Oksrukuyik_Grayling_Tags 10330 1989-2012_Oksrukuyik_YOY
Fish tagging data (length, weight, tag number) from the Kuparuk, the Sagavanirktok (primarily Oksrukuyik Creek) and the Itkillik (primarily the I-Minus outlet stream) watersheds, 2009 - 2017
Since 2009, the FISHSCAPE Project (grant number 1719267, 1417754, and 0902153), based at Toolik Field Station, has monitored physical, chemical, and biological parameters within three watersheds: The Kuparuk (including Toolik Lake and Toolik outlet stream); The Sagavanirktok (primarily Oksrukuyik Creek, but also including sections of the Ailish and Atigun Rivers and the Galbraith Lakes); and The Itkillik (primarily the I-Minus outlet stream, a tributary that that feeds into the Itkilik River). Target species were primarily Arctic grayling and Lake trout, although Arctic char, Burbot, Dolly varden, round whitefish, and slimey sculpin were also captured. Fish were collected on each river/lake. Coordinates and/or specific station locations were noted. All fish captured during the field season are measured, weighed, tagged (if large enough) and released. If fish were not previously tagged, they were tagged with Passive Integrated Transponder (PIT) tags which can be read with a whole stream antenna to track the migration of the fish, predminately Arctic grayling, throughout the systems.
Consumer Stocks: Wet weights from Everglades National Park (FCE), South Florida from March 2003 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Standard Lengths and Mean Weights for Prey-base Fishes from Taylor River and Joe Bay Sites, Everglades National Park (FCE), South Florida from January 2000 to April 2004
Prey-base fishes. The small demersal fishes of the coastal wetlands are a keystone element in this ecosystem. They are the primary and secondary consumers of the plants mentioned above and they are the primary food resource for myriad piscine (e.g. game species of fish), reptilian (e.g. juvenile crocodiles) and avian (e.g. wading birds) predators. The community dynamics of these fishes are dictated by hydrologic and hydrographic parameters so they also respond predictably to water management practices. Because they are a bottle-neck in the food web, their abundance and availability dictate the success of higher trophic levels. Fish are sampled in June, September and monthly from November through April at five locations. A 9m2 drop trap designed specifically for this habitat are used to quantify fish use. Nine traps are used at each site.
Aeolian dust weights sampled by BSNE collectors before and after the windy season from the NEAT study at Jornada Basin LTER, 2008-ongoing
This data package contains weights of windblown dust collected by BSNE collectors at long-term vegetation-removal plots that are part of the Jornada Basin LTER Nutrient and Ecosystem impacts of Aeolian Transport (NEAT) study located at the Jornada Experimental Range. The dataset can be used to estimate horizontal dust flux in vegetation removal treatment plots (different percentage vegetation removed) and contiguous downwind plots. Year 2008 was the initial collection and collections in subsequent years occur before and after the windy season. The experiment was designed to test the effects of increases in wind erosion on soil and vegetation properties on the sand sheet geomorphic unit for different levels of herbaceous cover. In order to increase wind erosion rates, vegetation was removed each spring to increase bare surface area and stimulate erosion (the less vegetation present the greater the wind erosion). The experimental design includes three blocks located in one pasture, each with four treatment plots that are maintained at one of four levels of herbaceous vegetation and small shrubs removed (25, 50, 75, 100%) and a control. Treatment plots are 25x50m with 25m buffers between. The vegetation removal includes grasses and small shrubs (like Gutierrezia sarothrae and Zinnia grandiflora), but not mesquite or yucca or any of the larger shrubs. Also, contiguous downwind plots are monitored for soil and vegetation properties, but no removal treatments are performed in these areas. A control treatment where no vegetation was removed that was not downwind of any treatment is also included. This study is ongoing and is updated twice per year - before and after the spring windy season.
Aeolian dust weights sampled by BSNE collectors in 18 locations at the Jornada Basin LTER site, 1998-ongoing
This data package contains aeolian dust weights from BSNE collectors at 18 locations at the Jornada Basin LTER. Collections are obtained at the 15 NPP study locations, the Geomet location, Scrape study location (now known as GROWES study), and Pasture 13 Burn study location. The collectors are turned into the wind with wind vanes. The amount of material collected corresponds to the horizontal flux at the height of the collector and the opening area of the collector and the duration of the sampling time. The five heights of the BSNE collectors above the soil surface are 5, 10, 20, 50, and 100 centimeters for every location where samples are taken. The vertical flux of the particles smaller than 10 micrometers is assumed to be a constant ratio of the horizontal sand flux. The objectives of the study are to find patterns of sand flux rates as related to soil and vegetation. Site info: The NPP sites were established to estimate patterns of aboveground primary production. The Geomet site is within a mesquite-dune area that has had long-term protection from cattle grazing. The scrape site (now known as the GROWES site) was originally designed to measure the abrasion of surface crust. The Pasture 13 Burn site is located in a pasture that was originally burned in 1998. Contact the data manager for additional information and site locations. This data collection is ongoing with new data added quarterly.
WDNR Yahara Lakes Fisheries: Fish Lengths and Weights 1987-1998
These data were collected by the Wisconsin Department of Natural Resources (WDNR) from 1987-1998. Most of these data (1987-1993) precede 1995, the year that the University of Wisconsin NTL-LTER program took over sampling of the Yahara Lakes. However, WDNR data collected from 1997-1998 (unrelated to LTER sampling) is also included. In 1987 a joint project by the WDNR and the University of Wisconsin-Madison, Center for Limnology (CFL) was initiated on Lake Mendota. The project involved biomanipulation of fish communities within the lake, which was acheived by stocking game fish species (northern pike and walleye). The goal was to induce a trophic cascade that would improve the water clarity of Lake Mendota. See Lathrop et al. 2002. Stocking piscivores to improve fishing and water clarity: a synthesis of the Lake Mendota biomanipulation project. Freshwater Biology 47, 2410-2424. In collecting these data, the objective was to gather population data and monitor populations to track the progress of the biomanipulation. The data is dominated by an assesssment of the game fishery in Lake Mendota, however other Yahara Lakes and non-game fish species are also represented. A combination of gear types was used to gather the population data including boom shocking, fyke netting, mini-fyke netting, seining, and gill netting. Not every sampling year includes length and weight data from all gear types. The WDNR also carried out randomized, access-point creel surveys to estimate fishing pressure, catch rates, harvest, and exploitation rates. Five data files each include length-weight data, and are organized by the type of gear or method which was used to collect the data: 1) fyke, mini-fyke, and seine netting 2) boom shocking 3) gill netting (1993 only) 4)walleye age as determined by scale and spine analysis (1987 only), and 5) creel survey. The final data file contains creel survey information: number of anglers fishing the shoreline, and number of anglers that started and complete
Marsh plant species shoot height, weight and diameters for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.
Marsh plant species shoot height, weight and diameters for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2012 and at Clubhead Creek in 2005 and from 2009 till 2019.
Plant aboveground biomass dry weight record for Space for Time plots in PIE LTER.
Aboveground biomass measurements were conducted annually near peak biomass to evaluate aboveground plant production and determine differences in relation to other biotic and abiotic factors. In a 0.053 m2 plot, aboveground biomass was clipped to the soil surface at Space For Time plots, dried, and weighed to capture dry weight.
Random Simple Undirected Weighted Graphs
<div> <div> <div> <div> <div> <div dir="auto"> <div> <div> <h3>Dataset Description</h3> <p>We introduce a dataset consisting of <em><strong>over 60 flow matrices</strong></em> representing <strong>simple</strong>, <strong>undirected</strong>, <strong>weighted</strong> <strong>graphs</strong>. This dataset is designed to support empirical studies in graph algorithms, clustering, and network analysis.</p> <p>Each graph is characterized by</p> <ul> <li> <p><strong>Order (|V|):</strong><br><span><span>{20, 50, 100, 300, 500, 700, 800, 900, 1000, 2000, 3000}</span></span></p> </li> <li> <p><strong>Density:</strong><br>For each graph order, 10 instances are generated with edge densities from the following set:<br><span><span>{0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0}</span></span></p> </li> <li> <p><strong>Clustered Structure:</strong><br>Nodes are partitioned into predefined clusters, where intra-cluster edges have significantly higher weights, while all inter-cluster edges are uniformly weighted (weight = 1). This structure simulates modular graphs commonly encountered in real-world networks.</p> </li> <li> <p><strong>Number of Clusters:</strong><br>The number of clusters varies according to graph order:<br><span><span>{5, 5, 10, 30, 50, 70, 80, 90, 100, 400, 500}</span></span></p> </li> </ul> <p>This dataset enables systematic testing of algorithms under varying structural conditions, including <strong>scale</strong>, <strong>sparsity</strong>, and <strong>community strength.</strong></p> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div dir="auto"> <div> <div> <p> </p> </div> </div> </div> </div> </div> </div> </div> </div>
T1-weighted brain MRI acquired from awake and unrestrained sheep
<p>This dataset contains T1-weighted brain MRI images acquired from 6 awake sheep, 1 anesthetized sheep and the MRI acquisition parameters.</p> <p><strong>When using this data please cite: </strong>Pluchot, C., Adriaensen, H., Parias, C. <em>et al.</em> Sheep (<em>Ovis aries</em>) training protocol for voluntary awake and unrestrained structural brain MRI acquisitions. <em>Behav Res</em> (2024). <a href="https://doi.org/10.3758/s13428-024-02449-6" target="_blank" rel="noopener">https://doi.org/10.3758/s13428-024-02449-6</a> </p> <p><strong>Note:</strong> A "Version v2" was created because the original "13332_anesthetized_T1.nii" file was corrupted.</p>
AnDy suit: human weight lifting wearable data
<p>This dataset comprises wearable data, collected using <a href="https://andy-project.eu/results/andysuit">An.Dy. suit</a>, from two weight lifting experiments of a human subject. Wearable data include kinematic measurements acquired with the <a href="https://www.xsens.com/">Xsens Motion Tracking system</a> (composed by 17 IMUs) and <a href="https://ifeeltech.eu/">iFeel shoes</a> (force/torque sensorized shoes developed by Istituto Italiano di Tecnologia).</p> <p>The experimental design is the following:</p> <p><strong>Experiment 01</strong></p> <p>Lifting task Geometry, accordingly to NIOSH convention:</p> <ul> <li>H = 63 cm</li> <li>V = 30 cm</li> <li>D = 40 cm</li> <li>CM = 0.9</li> <li>Load = 7 kg</li> </ul> <p>The task is executed 10 times.</p> <p><strong>Experiment 02</strong></p> <p>Lifting task Geometry, accordingly to NIOSH convention:</p> <ul> <li>H = 31 cm</li> <li>V = 66 cm</li> <li>D = 42 cm</li> <li>CM = 1</li> <li>Load = 5 kg</li> </ul> <p>The task has been executed:</p> <ul> <li>5 minutes: Lifting with back only</li> <li>5 minutes: Lifting with back plus leg</li> </ul> <p><strong>Data Structure</strong></p> <p>Data structure is the following:</p> <p>- experiment0x</p> <p> - wearable_data</p> <p> - FTshoes</p> <p> - xsens</p> <p>- subject_model</p> <p> </p> <p><strong>Data Interpretation</strong></p> <p>Data have been collected using <a href="https://www.yarp.it//v3.5/yarpdatadumper.html">YARP datadumper tool</a> using the thrift message implemented in <a href="https://github.com/robotology/wearables">wearables library</a>.</p> <p><strong>Data Usage</strong></p> <p>Data can be used by <a href="https://github.com/robotology/human-dynamics-estimation">human-dynamics-estimation</a> devices for replicating the results presented in:</p> <ul> <li>Rapetti, L.; Tirupachuri, Y.; Darvish, K.; Dafarra, S.; Nava, G.; Latella, C.; Pucci, D. Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics. <em>Algorithms</em> 2020, <em>13</em>, 266. https://doi.org/10.3390/a13100266</li> <li>Latella, C.; Traversaro, S.; Ferigo, D.; Tirupachuri, Y.; Rapetti, L.; Andrade Chavez, F.J.; Nori, F.; Pucci, D. Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques. <em>Sensors</em> 2019, <em>19</em>, 2794. https://doi.org/10.3390/s19122794</li> <li>Tirupachuri, Y. ; Ramadoss, P. ; Rapetti, L. ; Latella, C. ; Darvish, K. ; Traversaro, S. ; Pucci D. Online Non- Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment. <em>IEEE Access</em>, <em>pp. 1–1, Aug. </em>2021, https://ieeexplore.ieee.org/document/9526592.</li> </ul> <p> </p>
A dataset of 150000 terminal weighted projective spaces
<p><strong>Weighted projective spaces with at worst terminal singularities</strong></p> <p>A dataset of 150000 randomly generated weighted projective spaces with at worst terminal singularities, in dimensions 1 to 10.</p> <p>The data consists of the plain text files "rank_1_dim_N.txt" where N, which is the dimension of the weighted projective space, is in the range 1 to 10. Each line of the file is a sequence of weights of length N+1. For example, the first line of "rank_1_dim_4.txt" is:</p> <p>[1,2,5,14,21]</p> <p>and this corresponds to the 4-dimensional weighted projective space P(1,2,5,14,21).</p> <p>For details, see the paper:</p> <p>"Machine learning the dimension of a Fano variety", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale, <em>Nature Communications</em>, <strong>14:</strong>5526 (2023). doi:10.1038/s41467-023-41157-1</p> <p>Magma code capable of generating this dataset is in the file "generate_rank_1.m".</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5790079</p>
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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.