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932 results for “Long term data”
Long-term minnow community data from the Bayou Pierre, Mississippi
<p>Long-term community monitoring is becoming an important tool for ecological studies. Historical data are present in a variety of sources but often require considerable effort to extract, filter, and verify prior to analyses. In this dataset we document minnow (sensu lato, Cyprinidae + Leuciscidae) communities in the Bayou Pierre, Mississippi. We include filtered community data from twelve localities and three time periods (1974-75, 1985-89, 2019-20) derived from a mixture of literature, museum collections, and contemporary collecting efforts. All collections were made using comparable methodologies. We also include an additional 29 contemporary collections which do not have historical representation but which cover the majority of the watershed. We further include a trait database coded from literature values which captures both reproductive and ecological traits for the species in these data. These data were used in preparation of a manuscript examining long-term taxonomic, ecological, and functional change within the minnows in the Bayou Pierre, and they are part of a broader research effort to study the effects of fluvial geomorphic evolution on aquatic ecosystems.</p>
Data and scripts from: Long term analysis of social structure: evidence of age-based consistent associations in male Alpine ibex
<p>The folder contains the data and scripts used to produce the manuscript: "Long term analysis of social structure: evidence of age-based consistent associations in Alpine ibex" by Alice Brambilla, Achaz von Hardenberg, Claudia Canedoli, Francesca Brivio, Cédric Sueur and Christina R Stanley.</p>
Improving citizen science data for long-term monitoring of plant species
<p>In 2012, a new volunteer-based recording scheme for vascular plants was launched in the Netherlands. Its purpose is to track the changes in the number of occupied 1-km grid cells for as many native plant species as possible between survey rounds of 8 years. We did not prescribe a strict field protocol to minimize variation in observer effort, but instead chose to statistically correct for this variation with occupancy models. These models require replicated visits to a grid cell per season, which was implemented by having two independent observers survey grid cells and record all plant species observed. Now that a first survey round has ended (2012–2019), we evaluate our approach, i.e. we tested whether the scheme has the potential to produce proper trend estimates. The number of occupied grid cells in the first round was estimated per species, using an occupancy model with day of year, visit duration and observer experience as covariates for detection. The detection probability, which was 0.43 on average, strongly depended on visit duration and day of year. It was possible to estimate the number of occupied grid cells quite precisely for several hundreds of species, such that the statistical power is expected to be high enough to detect changes of 10% between survey rounds. For rare species, however, the power to detect changes is expected to be quite low. We conclude that the approach works well, but further improvements are suggested.</p>
Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil
<p>This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.</p>
Data from: Long-term severe hypoxia adaptation induces non-canonical EMT and a novel Wilms Tumor 1 (WT1) isoform
<p>The majority of cancer deaths are caused by solid tumors, where the four most prevalent cancers (breast, lung, colorectal and prostate) account for more than 60% of all cases (1). Tumor cell heterogeneity driven by variable cancer microenvironments, such as hypoxia, is a key determinant of therapeutic outcome. We developed a novel culture protocol, termed the Long-Term Hypoxia (LTHY) time course, to recapitulate the gradual development of severe hypoxia seen in vivo to mimic conditions observed in primary tumors. Cells subjected to LTHY underwent a non-canonical epithelial to mesenchymal transition (EMT) based on miRNA and mRNA signatures as well as displayed EMT-like morphological changes. Concomitant to this, we report production of a novel truncated isoform of WT1 transcription factor (tWt1), a non-canonical EMT driver, with expression driven by a yet undescribed intronic promoter through hypoxia-responsive elements (HREs). We further demonstrated that tWt1 initiates translation from an intron-derived start codon, retains proper subcellular localization and DNA binding. A similar tWt1 is also expressed in LTHY-cultured human cancer cell lines as well as primary cancers and predicts long-term patient survival. Our study not only demonstrates the importance of culture conditions that better mimic those observed in primary cancers, especially with regards to hypoxia, but also identifies a novel isoform of WT1 which correlates with poor long-term survival in ovarian cancer.</p>
Data from: Is adaptation associated with long-term persistence beyond a geographic range limit?
<p>Adaptation to new habitats might facilitate species' range shifts in response to climate change. In 2005, we transplanted experimental populations of coastal dune plant <em>Camissoniopsis cheiranthifolia </em>into four sites within and one site beyond its poleward range limit. Beyond-range transplants had high fitness and often delayed reproduction. To test for adaptation associated with experimental range expansion, we transplanted descendants from beyond and within-range populations after 10 generations <em>in situ</em> into two sites within the range, one at the range edge, and two sites beyond the range. We expected to detect adaptation to beyond-range conditions due to substantial genetic variation within experimental populations and environmental variation among sites. However, individuals from beyond-range experimental populations were not fitter than those from within the range when planted at either beyond-range site, indicating no adaptation to the beyond-range site or beyond-range environments in general. Beyond-range descendants also did not suffer lower fitness within the range. Although reproduction was again delayed beyond the range, late reproduction was not favored more strongly beyond than within the range, and beyond-range descendants did not delay reproduction more than within-range descendants. Persistence in beyond-range environments may not require adaptation, which could allow a rapid response to climate change.</p>
Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case
Open the record for dataset details and reuse information.
Long-term data reveal fitness costs of anthropogenic prey depletion for a subordinate competitor, the African wild dog (Lycaon pictus
<p>Within carnivore guilds, dominant competitors (e.g., lions, <em>Panthera leo</em>) are limited primarily by the density of prey, while subordinate competitors (e.g., African wild dogs, <em>Lycaon pictus</em>) have been limited by the density of dominant competitors. Historically, the fitness and population density of subordinate competitors have not been tightly linked to prey density. However, populations of large herbivores have declined substantially across sub-Saharan Africa due to human impacts, and where prey depletion is severe, fitness costs for competitive subordinates may begin to outweigh the benefits of competitive release. Using long-term intensive monitoring of African wild dogs in Zambia's Luangwa Valley Ecosystem (LVE), we tested the effects of prey depletion on survival and reproduction. Our study area included four contiguous regions that varied in protection, prey density, and lion density. We fit Bayesian Cormack-Jolly-Seber and closed-capture models to estimate effects on survival and population density, and generalized linear models to estimate effects on reproductive success. We found that the LVE is a stronghold for wild dogs, with an estimated median density of 4.0 individuals/100 km<sup>2</sup>. Despite this high density, survival and reproduction differed among regions, and both components of fitness were substantially reduced in the region with the lowest prey density. Anthropogenic prey depletion is becoming an important limiting factor for African wild dogs. If prey depletion (or any other form of habitat degradation) becomes severe enough that its fitness costs outweigh the benefits of competitive release, such changes can fundamentally alter the balance between limiting factors for competitively subordinate species.</p>
Hydrologic data from long-term research catchments at the Santa Rita and Jornada Experimental Ranges
<p>This datasets contains ecohydrological data collected for the research catchments at the Santa Rita Experimental Range (SRER, 31.817° N, -110.851° W) in southern of Arizona and the Jornada Experimental Range (JER, 32.585° N, -106.603° W) in southern New Mexico. The SRER catchment correspond to a mesquite savanna of the Sonoran Desert and the JER catchment is a mixed shrubland of the Chihuahuan Desert.</p> <p>Dataset includes the following ecohydrological variables at a daily scale:</p> <ul> <li>Year of measurement.</li> <li>Day of year (DOY) of measurement.</li> <li>Precipitation (<em>P</em>).</li> <li>Evapotranspiration (<em>ET</em>).</li> <li>Runoff (<em>Q</em>).</li> <li>Soil moisture (<em>SM</em>).</li> <li>Net Radiation (<em>R<sub>n</sub></em>).</li> <li>Sensible heat flux (<em>H</em>).</li> <li>Latent heat flux (<em>λET</em>).</li> <li>Gross primary production (<em>GPP</em>).</li> <li>Ecosystem respiration (<em>R<sub>eco</sub></em>).</li> <li>Net ecosystem exchange (<em>NEE</em>).</li> <li>Air Temperature (<em>Tair</em>).</li> <li>Relative Humidity (<em>RH</em>).</li> <li>Vapour Pressure Deficit (<em>VPD</em>).</li> </ul> <p>Please cite the next data note together with the dataset:</p> <p>Vivoni, E. R., Perez-Ruiz, E. R., Keller, Z. T., Escoto, E. A., Templeton, R. C., Templeton, N. P., Anderson, C. A., Schreiner-McGraw, A. P., Mendez-Barroso, L. A., Robles-Morua, A., Scott, R. L., Archer, S. R., Peters, D. P. C. 2021. Long-term Research Catchments to Investigate Shrub Encroachment in the Sonoran and Chihuahuan Deserts: Santa Rita and Jornada Experimental Ranges. <em>Hydrological Processes</em>. 35: e14031. <a href="https://urldefense.com/v3/__http://doi.org/10.1002/hyp.14031__;!!IKRxdwAv5BmarQ!OTbdNyLUWzTumWFs0u8j8dW3TT9O3UzM6XrcR-ReiWzf7vxHHKdNTKy8wR27fiHR$">http://doi.org/10.1002/hyp.14031</a></p> <p>This new version incudes three new variables (<em>Tair</em>, <em>RH</em> and <em>VPD</em>), and some carbon and water fluxes were updated to match data used in publications 1 and 2.</p> <p>Related publications:</p> <ol> <li>Perez-Ruiz, E.R., Vivoni, E.R., and Sala, O.E. 2022. <a href="https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.4189" target="_blank" rel="noreferrer noopener">Seasonal Carryover of Water and Effects on Carbon Dynamics in a Dryland Ecosystem</a>. <em>Ecosphere.</em> 13(7): e4189.</li> <li>Vivoni, E.R., Perez-Ruiz, E.R., Scott, R.L., Naito, A.T., Archer, S.R., Biederman, J.A., and Templeton, N.P. 2022. <a href="http://www.sciencedirect.com/science/article/pii/S0168192321004494" target="_blank" rel="noreferrer noopener">A Micrometeorological Flux Perspective on Brush Management in a Shrub-encroached Sonoran Desert Grassland</a>. <em>Agricultural and Forest Meteorology</em>. 313: 108763.</li> <li>Schreiner-McGraw, A.P., and Vivoni, E.R. 2018. <a href="http://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018WR022842">On the Sensitivity of Hillslope Runoff and Channel Transmission Losses in Arid Piedmont Slopes.</a> <em>Water Resources Research</em>. 54(7): 4498-4518.</li> <li>Schreiner-McGraw, A.P., and Vivoni, E.R. 2017. <a href="http://onlinelibrary.wiley.com/doi/10.1002/ecs2.2000/full">Percolation Observations in an Arid Piedmont Watershed and Linkages to Historical Conditions in the Chihuahuan Desert.</a> <em>Ecosphere</em>. 8(11): e02000, <a href="http://dx.doi.org/10.1002/ecs2.2000">http://dx.doi.org/10.1002/ecs2.2000</a></li> <li>Anderson, C.A., and Vivoni, E.R. 2016. <a href="http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015WR018016/abstract">Impact of Land Surface States within the Flux Footprint on Daytime Land-Atmosphere Coupling in Two Semiarid Ecosystems of the Southwestern U.S.</a> <em>Water Resources Research.</em> 52: 4785-4800.</li> <li>Schreiner-McGraw, A.P., Vivoni, E.R., Mascaro, G., and Franz, T.E. 2016. <a href="http://www.hydrol-earth-syst-sci.net/20/329/2016/hess-20-329-2016.html">Closing the Water Balance with Cosmic-ray Soil Moisture Measurements and Assessing Their Relation with Evapotranspiration in Two Semiarid Watersheds.</a> <em>Hydrology and Earth System Sciences</em>. 20: 329-345.</li> <li>Pierini, N.A., Vivoni, E.R., Robles-Morua, A., Scott, R.L., and Nearing, M.A. 2014. <a href="http://onlinelibrary.wiley.com/doi/10.1002/2014WR015781/abstract">Using Observations and a Distributed Hydrologic Model to Explore Runoff Threshold Processes Linked with Mesquite Encroachment in the Sonoran Desert.</a> <em>Water Resources Research</em>. 50(10): 8191–8215.</li> <li>Templeton, R.C., Vivoni, E.R., Méndez-Barroso, L.A., Pierini, N.A., Anderson, C.A., Rango, A., Laliberte, A.S., and Scott, R.L. 2014. <a href="http://www.sciencedirect.com/science/article/pii/S0022169413008718">High-Resolution Characterization of a Semiarid Watershed: Implications on Evapotranspiration Estimates.</a> <em>Journal of Hydrology</em>. 509: 306-319.</li> <li>Vivoni, E.R., Rango, A., Anderson, C.A., Pierini, N.A., Schreiner-McGraw, A., Saripalli, S., and Laliberte, A.S. 2014. <a href="http://www.esajournals.org/doi/abs/10.1890/ES14-00217.1">Ecohydrology with Unmanned Aerial Vehicles.</a> <em>Ecosphere</em>. 5(10): art130. <a href="http://dx.doi.org/10.1890/ES14-00217.1">http://dx.doi.org/10.1890/ES14-00217.1</a></li> </ol> <p> </p>
Data for Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024
<p>Zip files contain voltage imaging traces and on-ball behavioral data from the five main figures in Huang, Luo, et al., Dopamine-mediated interactions between short- and long-term memory dynamics, Nature, 2024</p>
Long term bearded pig camera trap data across the SAFE landscape.
<p><strong>Description: </strong></p> <p>Data on camera trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.<br> Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by Charlie Davison in 2017.<br> Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/173"><strong>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=312">here</a></p> <p><strong>Files: </strong>This consists of 1 file: DavisonBeardedPigs.xlsx</p> <p><strong>DavisonBeardedPigs.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>Deployments</strong> (described in worksheet Deployments)</p> <p>Description: Data relating to all random camera trap deployments</p> <p>Number of fields: 5</p> <p>Number of data rows: 833</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement point on SAFE project core grids (Field type: Location)</li> <li><strong>Date.On</strong>: Date survey started (Field type: Date)</li> <li><strong>Date.Off</strong>: Date survey ended (Field type: Date)</li> <li><strong>CTNs</strong>: Length of survey (camera trap nights). Zero if camera was faulty. (Field type: Numeric)</li> <li><strong>Landuse</strong>: Land-use type (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Records</strong> (described in worksheet Records)</p> <p>Description: Data relating to all camera trap records of bearded pigs across all land-uses, and humans and domestic dogs in Oil palm; data generated from individual camera trap images</p> <p>Number of fields: 13</p> <p>Number of data rows: 4236</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement location on SAFE project core grids (Field type: Location)</li> <li><strong>Date</strong>: Date of photo capture (Field type: Date)</li> <li><strong>Time</strong>: Time of photo capture (Field type: Time)</li> <li><strong>Ambient.Temp</strong>: Temperature at the time of photo capture (Field type: Numeric)</li> <li><strong>Moon.Phase</strong>: Moonphase at time of photo capture (Field type: Categorical)</li> <li><strong>Species</strong>: Identity of the individual(s) (Field type: Taxa)</li> <li><strong>Soc.Str</strong>: Social structure (Field type: Categorical)</li> <li><strong>Sex</strong>: Sex of the individual (Field type: Categorical)</li> <li><strong>No.Individuals</strong>: Number of individuals in survey (Field type: Abundance)</li> <li><strong>No.Juveniles</strong>: Number of adults in survey (Field type: Abundance)</li> <li><strong>No.Subadults</strong>: Number of subadults in survey (Field type: Abundance)</li> <li><strong>No.Adults</strong>: Number of juveniles in survey (Field type: Abundance)</li> <li><strong>Land-use</strong>: Land use type (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-04-30 to 2018-04-01</p> <p><strong>Latitudinal extent: </strong>4.6350 to 4.7538</p> <p><strong>Longitudinal extent: </strong>116.9472 to 117.6253</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Chordata<br>  -  - Mammalia<br>  -  -  - Artiodactyla<br>  -  -  -  - Suidae<br>  -  -  -  -  - <em>Sus</em><br>  -  -  -  -  -  - <em>Sus barbatus</em><br>  -  -  - Carnivora<br>  -  -  -  - Canidae<br>  -  -  -  -  - <em>Canis</em><br>  -  -  -  -  -  - <em>Canis lupus</em><br>  -  -  -  -  -  -  - <em>Canis lupus familiaris</em><br>  -  -  - Primates<br>  -  -  -  - Hominidae<br>  -  -  -  -  - <em>Homo</em><br>  -  -  -  -  -  - <em>Homo sapiens</em></p> <p> </p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
Long-term data from field erosion plot studies in eastern Austria
<p>Long-term runoff plot experiments were conducted at three sites in eastern Austria to evaluate the impact of conservation tillage. Following soil tillage treatments were investigated: 1) conventional tillage with ploughing in fall (CT), 2) mulch tillage with cover crops during winter period (MT), and 3) no tillage with cover crops during winter period (NT). Soil textures range from silt loam to loam.</p> <p>Datasets include for each site and each year the period of operation of the plots, the planted crop, the annual precipitation, surface runoff, soil loss, surface losses of nitrogen, phopshorus and soil organic carbon as well as absolute and relative crop yields.</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem"
<p>The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem" Yu et al. </p>
Data for Paper: A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system
<p>Data containing the results and figures presented in the paper "A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted to the Journal Energy Reports.</p>
Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"
<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>
Code and Data for "Green Growth in the Mirror of History: Long-Term Evidence on Decoupling Emissions from GDP"
<p><span>The code and files contained in this repository allow for the reproduction of the results and figures from the paper <em>“Green Growth in the Mirror of History: Long-Term Evidence on Decoupling Emissions from GDP”</em> (submitted to <em>Nature Communications</em>). In particular, it generates the historical database of gross domestic product (GDP), population, and greenhouse gas emissions (GHGe) at the national, regional, and global levels from 1820 to 2022. It also includes the code required for data processing and allows reproduction of all figures and analyses in the manuscript.</span></p> <p><span>The repository is organized into a set of folders functioning as an R project. </span><span>These folders are as follows:</span></p> <p><span><span>·<span> </span></span></span><strong><span><span>data_input:</span></span></strong><span><span> Contains the databases and files necessary to run our code that have been preprocessed by ourselves. The databases which we have not preprocessed are downloaded directly from their original sources using the links provided in the R scripts. These databases were created by other researchers. Please cite the original databases if you use them directly.</span></span></p> <p><span><span><span>·<span> </span></span></span></span><strong><span><span>code:</span></span></strong><span><span> Contains R scripts used to process the datasets from data_input and to produce the figures. These scripts are:</span></span></p> <p><span><span>o<span> </span></span></span><strong><span><span>common.</span></span><span><strong>R</strong> </span></strong><span>contains code that is sourced in the other scripts and does not need to be run by the user.</span></p> <p><span><span>o<span> </span></span></span><strong><span>input_processing.R</span></strong><span> processes the data input to generate the main dataset saved in the folder data_output and used to make the figures.</span></p> <p><span><span>o<span> </span></span></span><strong><span>figures.R</span></strong><span> contains the code that processes the main dataset to produce each figure.</span></p> <p><span><span>·<span> </span></span></span><strong><span><span>data_output:</span></span></strong><span><span> Contains datasets generated by the scripts contained in<strong> </strong>input_processing.R that are used to produce the figures.</span></span></p> <ul> <li><strong><span>figures:</span></strong><span> This folder contains all the figures produced by figures.R. Note that some figures also underwent purely aesthetic edits in external software (Adobe Illustrator) for clarity purposes.</span></li> </ul>
Design of facilitated dissociation enables control over cytokine signaling duration - SMT raw data - unstimulated - calibration beads - long term tracking - labelled ligand
<p>This dataset contains the raw data for the Single-molecule tracking data of the manuscript: "Design of facilitated dissociation enables control over cytokine signaling duration". Presicely, it contains the calibration beads for all imaging experiments, the long term tracking experiments, the experiments with labelled ligand and the unstimulated probes</p>
Data from: Plant community responses to long-term fertilization: changes in functional group abundance drive changes in species richness
Declines in species richness due to fertilization are typically rapid and associated with increases in aboveground production. However, in a long-term experiment examining the impacts of fertilization in an early successional community, we found it took 14 years for plant species richness to significantly decline in fertilized plots, despite fertilization causing a rapid increase in aboveground production. To determine what accounted for this lag in the species richness response, we examined several potential mechanisms. We found evidence suggesting the abundance of one functional group—tall species with long-distance (runner) clonality—drove changes in species richness, and we found little support for other mechanisms. Tall runner species initially increased in abundance due to fertilization, then declined dramatically and were not abundant again until later in the experiment, when species richness and the combined biomass of all other functional groups (non-tall runner) declined. Over 86 % of the species found throughout the course of our study are non-tall runner, and there is a strong negative relationship between non-tall runner and tall runner biomass. We therefore suggest that declines in species richness in the fertilized treatment are due to high tall runner abundance that decreases the abundance and richness of non-tall runner species. By identifying the functional group that drives declines in richness due to fertilization, our results help to elucidate how fertilization decreases plant richness and also suggest that declines in richness due to fertilization can be lessened by controlling the abundance of species with a tall runner growth form.
ScienceDex guides
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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.