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156 results for “long-term dataset”
SGS-LTER Ecosystem Stress Area - long-term density dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1975-2011, ARS Study Number 3 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/330/3, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/520/8. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persiste
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/331/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/521/7. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persiste
Dataset for "Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?"
<p>Datasets belonging to the paper "Long-term extreme response of an offshore turbine: How accurate are<br> contour-based estimates?" by Haselsteiner, Frieling, Mackay, Sander and Thoben.</p> <p>Available are:<br> * A 1000-year time series of hourly environmental conditions<br> * 516 1-hour time series of the mudline overturning moment, simulated using openFAST</p> <p> </p>
Dataset of physical, biological and chemical soil properties from 10 European long-term experiments
<p>This dataset contains all measurements that were conducted within the Workpackage #2 of the SoilX Project (2022-2024). The data contains physical, chemical and biological soil parameters that were measured in ten European long-term field experiments, as well as soil management indicators calculated with the SoilManageR packager for R. Each data table contains different parameters, and they can be linked by the identifying columns (LTE, treatment, depth, block, replicate). Further information can be found in the ReadMe.txt.</p> <pre> </pre>
The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data
<p>In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.</p>
Moreno et al_2023_Long-term_SDGsEU_Dataset
<p>This dataset contains the underlying IAM output data for the journal article by Moreno et al. currently in revision in Nature Communications Earth and Environment titled "Long-term sustainable development prospects of EU decarbonisation pathways: a multi-model study".</p>
LongPMInd: long-term (1980-2022) daily ground particulate matter datasets in India
<p>The LongPMInd dataset, including daily PM2.5 and PM10 concentration (10km) for India during 1980-2022, is publicly accessible. All data are provided with NetCDF format with a spatial resolution of 10 km.<br><br></p>
Synthesizing Multiple Long-Term Datasets to Test Flow Ecology Relationships
<p>This database is comprised of time series of fish abundances at stream and river sites across the contiguous United States, along with associated hydrologic and land cover metrics. The database has been assembled in order to test hypothesized fish flow-ecology relationships across diverse species assemblages and riverine contexts. Methods for compilation of the database and additional information can be found in two accompanying files: "SynthesizingDatasetsForFlowEcology_v1_0_0.pdf" contains detailed methodology and results, and "ReadMe_FishFlow_v1_0_0.csv" contains information about the variables found within each database file. This project was funded by a US Army Corps of Engineers contract to the US Geological Survey, with a subcontract to the University of Georgia. Please note, the contract number for this project is G21AC10482, not G21AS00529 as is stated in the summary document (SynthesizingDatasetsForFlowEcology_v1_0_0.pdf).</p>
Dataset for "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest"
<p>The final dataset used in manuscript "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest" by Vesala et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as meteorological drivers for SiB4 simulations and SiB4 simulated COS flux at the Hyytiälä grid cell from January 2013 to December 2017. Raw data are available upon request from the author.</p>
Dataset: Long-term Change in Metabolism Phenology in North Temperate Lakes
<p>Scripts, model configurations and outputs to process the data and recreate the figures from Ladwig, R., Appling, A., Delany, A., Dugan, H.A., Gao, L., Lottig, N., Stachelek, J., Hanson, P.C.: Long-term Change in Metabolism Phenology in North-Temperate Lakes.</p> <p>This repository includes the setup and output from the metabolism model ran on the lakes Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout. Scripts to run the model are located under /src (odem_min_indexed_sed.stan, run_odem_chtc_sed.R, stan_utility.R, stan_utility_chtc.R) together with the scripts to create the driver data, 0_Model_Input.R, and the processed results for the discussion of the paper, 1_Postprocessing.R. Addtionally, all scripts to recreate the figures from the manuscript are also included in /src.</p> <p>The figures are located under /Figures and processed output under /Processed_Output.</p>
Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night
<p><strong>General Description:</strong></p> <p>This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" (<em>"Novel methods of acoustic identification of birds migrating at night"</em>) by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>Development_Set_3.1.zip</p> <p>|_Development_Set_3.1/</p> <p> |__Training_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Validation_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p> |__Testing_Set/</p> <p> |____*.wav</p> <p> |____*.txt</p> <p>Training Set: 86 recordings</p> <p>Validation Set: 8 recordings</p> <p>Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b)</p> <p>Names of waveforms and annotations are matching.</p> <p><strong>Waveforms:</strong></p> <p>The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo).</p> <p><strong>Annotations:</strong></p> <p>Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general.</p> <p>Transcription format:</p> <p>[Starting time (sec)] [Ending time (sec)] [Label]</p> <p><strong>Meaning of the labels:</strong></p> <p>1. Positive classes – migrating passerine birds:</p> <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> <p>Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds).</p> <p>2. Negative classes – other marked sound events:</p> <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> <p>Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings.</p> <p>3. Labels to be excluded from analysis:</p> <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>
(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0008 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0001 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0003 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0002 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0007 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0009 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0006 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(10)-Strobl2022A-DS0005 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(10)-Strobl2022A-DS0004 – <em>Ceratitis capitata</em> TREhs43-hid<sup>Ala5</sup>_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
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.