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.
53
datasets available to search
ShareScore release 0.9.0
Dataset results
53 results for “Long-term potentiation”
SALTEx soil oxidation-reduction (redox) potential measurements from the GCE LTER Seawater Addition Long-Term Experiment (SALTEx) between July 2016 and March 2017
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots , each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. Response measurements include porewater chemistry, specifically concentrations of chloride, sulfate, sulfide, dissolved organic carbon (DOC), ammonium-N, nitrate/nitrite-N, dissolved reactive phosphorus, total phosphorus, total nitrogen, organic nitrogen, carbon:nitrogen ratio, organic-carbon:organic-nitrogen ratio, and pH.
Functional potential and evolutionary response to long-term heat selection of bacterial associates of coral photosymbionts
<p>Sequencing reads were assembled using the genome assembler pipeline Shovill v1.1.0. Briefly, the Shovill pipeline included read trimming using Trimmomatic v0.39, de novo assembly with SPAdes v3.15.5 and genome polishing with Pilon v1.24. After the pipeline, additional polishing was performed by mapping the reads back to the contigs with BWA v0.7.17 and sorting the resulting SAM/BAM files using SAMtools v1.15.1. Pilon v1.24 was then used to correct bases, fix mis-assemblies and fill gaps. The reformat.sh script from the Bbmap package v38.76 (-minlength=1000) was used to filter out contigs less than 1000bp. The draft genome assemblies were then annotated with Bakta v1.7.0. </p> <p>Single nucleotide polymorphism (SNP) detection between WT (WTref) and SS (SSref) samples were then performed using snippy v4.6.0, where both WT and SS samples were inputted as the reference genome in turn.</p> <p>A subset of the snippy output files are uploaded here and contain all variants found.</p>
Ensemble Machine Learning Prediction of Potential FAPAR: Monthly time-series 2021 and Long-Term Comparison with Actual FAPAR
<p><strong>General Description</strong></p> <p>The dataset contains composites at 250 m spatial resolution of (1) monthly potential FAPAR for the year 2021 from ensemble ML model predictions, (2) the model deviance for each prediction, (3) the yearly average of potential FAPAR, (4) the yearly average of actual FAPAR and (5) the yearly average of the difference between actual and potential (actual minus potential) FAPAR. The dataset is based on the <a href="https://zenodo.org/record/8392976">95th percentile of the monthly aggregated FAPAR</a> derived from <a href="http://glass.umd.edu/Overview.html">250 m 8 d GLASS V6 FAPAR</a>. Potential FAPAR was predicted by fitting an ensemble ML model using globally distributed training points (cca 3 Mio) and a set of 52 biophysical covariates including several layers related to human pressure. The code for modeling potential FAPAR is openly available at <a href="http://github.com/Open-Earth-Monitor/Global_FAPAR_250m">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m</a>. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. </p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2021 - December 2021</li> <li><strong>Type of data: </strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR</li> <li><strong>Statistical methods used: </strong>Ensemble machine learning</li> <li><strong>Limitations or exclusions in the data: </strong>The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size: </strong>172,800 x 71,698</li> <li><strong>File format: </strong>Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p> </p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> pot.fapar = Potential Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination: </strong>eml = ensemble machine learning</li> <li><strong>Position in the probability distribution / variable type:</strong> m = mean</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference: </strong>s = surface</li> <li><strong>Time reference begin time:</strong> 20210101 = 2021-01-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2021-12-31</li> <li><strong>Bounding box: </strong>go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230924 = 2023-09-24 (creation date)</li> </ol>
Polyadenylation landscape of in vivo long-term potentiation in the rat brain
<h3><strong>This repository contains data published along our corresponding manuscript and additional data resources generated/used throughout this work. <br>______________________________________________________________________________</strong></h3> <h2><strong>Source data accompanying the manuscript:<br></strong></h2> <p><strong>Supplementary Table 1. Key resource table. (A) </strong>Characteristics of analyzed material (e.g. sample identifiers, number of animals used, reads produced, accession numbers). <strong>(B)</strong> Key resources (antibodies, reagents, software).<strong><br><br>Supplementary Table 2. Summary of dentate gyri DRS data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 3. Summary of dentate gyri cDNA data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 4.</strong> <strong>High-confidence PASs.</strong> <strong>(A)</strong> PASs predicted for datasets obtained 10 min after LTP induction by TAPAS. <strong>(B)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 10 min after LTP induction. <strong>(C)</strong> PASs predicted for datasets obtained 60 min after LTP induction. <strong>(D)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 60 min after LTP induction.</p> <p><strong>Supplementary Table 5.</strong> <strong>Nonadenosine profiling upon LTP induction. (A)</strong> Summary of Ninetails pipeline for dentate gyrus. <strong>(B)</strong> List of genes containing semi-templated poly(A) tails with their adjacent nucleotide contexts.</p> <p><strong>Supplementary Table 6. Summary of synaptoneurosomal DRS/cDNA data per gene.</strong> <strong>(A)</strong> Differential expression and differential adenylation data for unfractionated synaptoneurosomes DRS sequencing. (B) <strong> </strong>Summary of Ninetails pipeline for unfractionated synaptoneurosomes DRS sequencing. (C) Differential expression and differential adenylation data for monoribosome-bound mRNA synaptoneurosomes cDNA sequencing. (D) Differential expression and differential adenylation data for polyribosome-bound mRNA synaptoneurosomes cDNA sequencing. (E) Differential expression and differential adenylation data for unfractionated synaptoneurosomes cDNA sequencing.<br><br><strong>Supplementary Information</strong> - supplementary figures and captions.<br><br><strong>______________________________________________________________________________</strong></p> <h2><strong>Additional data resources:</strong></h2> <p><strong>CPEB1_motif.meme </strong>- CPE1 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<br><strong><br>CPEB2_4_motif.meme</strong> - CPE2,4 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<strong><br></strong></p> <p><strong>mRatBN7.2_TAPAS_ref_flat.txt</strong> - mRatBN7.2 reference annotation in format required by TAPAS<br><br><strong>mRatBN7.2_LAPA.gtf </strong>- mRatBN7.2 reference annotation in format required by LAPA</p> <p><strong>FIMO_output.zip</strong> - compressed folder with motif predictions provided by FIMO software.<strong><br><br>LAPA_output_dentate_gyrus.zip </strong>- compressed folder with poly(A) clusters predicted by LAPA software. Each timepoint is represented by separate output. <br><strong><br>TAPAS_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by TAPAS software. Each timepoint is represented by separate output. <strong><br><br>Ninetails_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from dentate gyrus. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<br><strong><br>Ninetails_output_synaptoneurosomes.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from synaptoneurosomes. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<strong><br><br>PolyA_clusters_high_confident.bed </strong>- High-confidence poly(A) clusters annotation in bed format.<strong><br></strong></p>
Long-term simulation of snow cover and its potential impacts on seasonal frost dynamics in croplands across southern Canada
<p><em>In northern climes, accurate simulation of thermal and hydrological budgets for farmlands during overwintering conditions is crucial to both an accurate prediction of spring flooding and the successful management of nutrient losses. As snow cover influences soil freezing dynamics, it has been hypothesized that reduced snow cover due to warmer winters might increase the depth and duration of frozen soil conditions. Nonetheless, such impacts remain poorly understood and, given the difficulty in measuring the depth of frozen soil, no long-term field experiment has documented these potential effects. The present study was designed to test this hypothesis. Drawing upon observed snow depth and soil temperature data collected from six research farms across Southern Canada over various time spans from 1989 to 2020, the Root Zone Water Quality Model, integrated with the Simultaneous Heat and Water model, was calibrated and validated. The potential influence of warmer winter on shifts in soil frost dynamics was evaluated by estimating the depth and duration of frozen soil for each farmland site under various RCP temperature scenarios using the RZ-SHAW model. Soil frozen depth in Eastern site increased with the increase of RCP temperature scenarios in some years, but decreased under the highest RCP temperature scenario. The monthly relationship between snow depth and soil frozen depth was determined through partial correlation analysis. Snow was most effective in alleviating soil freezing in the months of January and February, a period when snow cover depth was least affected by warming air temperatures. This paper suggests that Global warming induced-snow cover reduction would be site-specific and is </em>more likely to occur in <em>regions where energy lost through reduced snow cover would outweigh the energy gained through warmer air temperature.</em></p>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Dataset for Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands
<p>Soil organic carbon dynamics for Ecuadorian croplands on the period 2020-2070, under RCP4.5, for various carbon sequestration alternatives, relative to a business as usual situation. Results obtained from adapted version of RothC for carbon sequestration technologies.</p>
Long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, a modeling analysis.
A study investigating the mechanisms that control long-term response of tussock tundra to fire and to increases in air temperature, CO2, nitrogen deposition and phosphorus weathering. The MBL MEL was used to simulate the recovery of three types of tussock tundra, unburned, moderately burned, and severely burned in response to changes in climate and nutrient additions. The simulations indicate that the recovery of nutrients lost during wildfire is difficult under a warming climate because warming increases nutrient cycles and subsequently leaching within the ecosystem. The study was published in Ecological Applications (in press, 2016). This dataset is the long term archive of the results published in the paper. The full dataset has been broken into two parts because of the number and size of the files. Part 1 contains MBL MEL executable, a model description file in word, and the input files to run the simulations. Part 2 contains the output files for all simulations. Both Part 1 and Part 2 contain several different types of files. In Part 1 the comma separated ascii file included with the dataset is one of the many driver files used for the simulations. The variable descriptions below describe the variables in that file and all the driver files. In Part 2 the comma separated ascii file included with the dataset is one of the many output files from the simulations. The variable descriptions below describe the variables in that file and all the output files. To access all the files in the dataset be sure to download the two zip files described in the Methods section below. Note that the full download is large, over 700 MB for each part. Permanent Archive of the data published in Jiang, et al., in press, Modeling long-term changes in tundra carbon balance following wildfire, climate change and potential nutrient addition, Ecological Applications.
Can short-term data accurately model long-term environmental exposures? Investigating the multigenerational adaptation potential of Daphnia magna to environmental concentrations of organic ultraviolet filters
<p>Organic ultraviolet filters (UVFs) are contaminants of concern, ubiquitously found in many aquatic environments due to their use in personal care products to protect against ultraviolet radiation. Research regarding the toxicity of UVFs such as avobenzone, octocrylene and oxybenzone indicates that these chemicals may pose a threat to invertebrate species; however, minimal long-term studies have been conducted to determine how these UVFs may affect continuously exposed populations. The present study modeled the effects of a 5-generation exposure of <em>Daphnia</em> <em>magna</em> to these UVFs at environmental concentrations. Avobenzone and octocrylene resulted in minor, transient decreases in reproduction and wet mass. Oxybenzone exposure resulted in > 40% mortality, 46% decreased reproduction and 4-fold greater reproductive failure over the F0 and F1 generations; however, normal function was largely regained by the F2 generation. These results indicate that <em>Daphnia</em> are able to acclimate over long-term exposures to concentrations of 6.59 μg/L avobenzone, ~0.6 μg/L octocrylene or 16.5 μg/L oxybenzone. This suggests that short-term studies indicating high toxicity may not accurately represent long-term outcomes in wild populations, adding additional complexity to risk assessment practices at a time when many regions are considering or implementing UVF bans in order to protect these most sensitive invertebrate species.</p>
Can short-term data accurately model long-term environmental exposures? Investigating the multigenerational adaptation potential of Daphnia magna to environmental concentrations of organic ultraviolet filters
Open the record for dataset details and reuse information.
Data from: The long-term evolutionary potential of four yeast species and their hybrids in extreme temperature conditions
Open the record for dataset details and reuse information.
Long-term Dynamics in Soil Field Available Nitrogen and Potentially Mineralizable Nitrogen in a Chihuahuan Desert Grassland at the Sevilleta National Wildlife Refuge, New Mexico (1989-2014)
Associated with a project that was based upon the assumption that nitrogen may limit net primary plant production in desert grasslands, this project began measuring available inorganic soil N and potentially mineralizable N of soils at two desert grassland locations. Both available N and potentially mineralizable N were greatest following a drought period in 1989, declined during wetter periods that followed and remained relatively stable until another extended drought period. After drought in 1995-6, both forms of soil N increased, indicating the potential for greater NPP following drought and lower potential NPP during periods of normal precipitation.
Data from: Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions
The importance of long-term environmental monitoring and research for detecting and understanding changes in ecosystems and human impacts on natural systems is widely acknowledged. Over the last decades a number of critical components for successful long-term monitoring have been identified. One basic component is quality assurance/quality control protocols to ensure consistency and comparability of data. In Norway, the authorities require environmental monitoring of the impacts of the offshore petroleum industry on the Norwegian continental shelf, and in 1996 a large-scale regional environmental monitoring program was established. As a case study, we used a sub-set of data from this monitoring to explore concepts regarding best practices for long-term environmental monitoring. Specifically, we examined data from physical and chemical sediment samples and benthic macro-invertebrate assemblages from 11 stations from six sampling occasions during the period 1996-2011. Despite the established quality assessment and quality control protocols for this monitoring program, we identified several data challenges, such as, missing values and outliers, discrepancies in variable and station names, changes in procedures without calibration, and different taxonomic resolution. Furthermore, we show that the use of different laboratories over time makes it difficult to draw conclusions with regard to some of the observed changes. We offer recommendations to facilitate comparison of data over time. We also present a new procedure to handle different taxonomic resolution so valuable historical data is not discarded. These topics have a broader relevance and application than for our case study.
A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex
<p>This dataset contains all (>1000) cell pairs as the Blue Brain Projects <a href="https://github.com/BlueBrain/EModelRunner">EModelRunner</a> packages as well as analysis code and analysed data used for the figures of our <a href="https://www.biorxiv.org/content/biorxiv/early/2020/04/20/2020.04.19.043117.full.pdf">preprint</a>: <em><strong>"A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex"</strong></em>.</p> <p>More documentation will follow in the upcoming days.</p> <p><strong>Updates:</strong><br> v1.1 (31/12/2021): updated READMEs within cell_packages, added 2 extra authors for their contribution in EModelRunner.<br> v1.2 (31/12/2021): same as v1.1 but w/o MacOS junk<br> v1.3 (11/01/2022): added analysis notebooks<br> v2.0 (11/01/2022): same as v1.3 but w/o MacOS junk and proper version number<br> v2.1 (14/03/2022): fetching data from websites when possible instead of providing the downloaded csv files.</p>
Potentially Inappropriate Prescribing (PIP) in Long-Term Care (LTC) Patients
ClinicalTrials.gov study NCT02523482. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions
Open the record for dataset details and reuse information.
Prolonged water-only fasting followed by a whole-plant-food diet is a potential long-term management strategy for hyper-tension and obesity
Open the record for dataset details and reuse information.
Long-term Potentiation Disruption Underlying Cognitive Impairment in ECT
ClinicalTrials.gov study NCT06733558. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Long-term shifts in the seasonal abundance of adult Culicoides biting midges and their impact on the potential for arbovirus outbreaks
Open the record for dataset details and reuse information.
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.