cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4571628
title: Corn-Soy Data Layer

description: <strong>Dataset Abstract:</strong> Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download on Zenodo. <strong>Summary of Methods:</strong> Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the <em>pixel_qa</em> band provided with Landsat Surface Reflectance products. <strong>Map Legend:</strong> 0 = outside study area 1 = corn 5 = soy 9 = other crop 255 = non-crop (masked by NLCD) Values were chosen to be consistent with CDL values when possible. <strong>Usage Notes:</strong> We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R<sup>2</sup> with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl. <strong>Updates:</strong> <strong>March 1, 2021:</strong> Fixed an issue where 255 (non-crop) values were represented as NAs instead. CSDL now contains the 255 values representing non-crop. <strong>October 20, 2020:</strong> Fixed projection issues in the previous version. The CSDL projection now matches that of CDL. <strong>July 13, 2020:</strong> We revised how we used NLCD to mask out non-crop pixels from our maps. Instead of using one cropland mask (the union of cropland across all NLCD maps) for all years of CSDL, we used a different cropland mask (the last available NLCD) for each year of CSDL. We also reprojected the CSDL maps to the same projection as CDL to make it easier for users to transition between or combine the two datasets.
DOI: 10.5281/zenodo.5732280
title: Vineyard Dataset

description: Vineyard Dataset for training the DIscriminator and the FC Layer
DOI: 10.5281/zenodo.6907373
title: CH4 and CO2 dataset for the Qinghai-Tibetan Plateau rivers

description: 该数据集整理了青藏高原河流CH4和CO2浓度和通量的直接现场测量值,以及28个采样点的位置、水文、物理和化学条件信息。这些数据对于平衡区域和全球C预算将非常有价值。
DOI: 10.5281/zenodo.3600422
title: Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"

description: The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day. The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence". The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2. The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3 <strong>end</strong><strong> </strong> 15
DOI: 10.15468/dl.57b3vf
title: Occurrence Download

description: A dataset containing 184336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Testudines",
 "Year 2019-2022"
 ]
}

 The dataset includes 184336 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044981-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.3375589
title: Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean dataset

description: Dataset for the paper "Eulerian modelling of the three-dimensional distribution of seven popular microplastic types in the global ocean" by A. S. Mountford and M. A. Morales Maqueda. ORCA2_5d_00010101_00011231_ptrc_T_con.nc - control experiment (year 50) ORCA2_5d_00010101_00011231_ptrc_T_30m.nc - 30 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50) ORCA2_5d_00010101_00011231_ptrc_T_90m.nc - 90 m year<sup>-1</sup> piston velocity sensitivity experiment (year 50) ORCA2_5d_00010101_00011231_ptrc_T_50.nc - neutrally buoyant sensitivity simulation (year 50) plastic_1_ts.nc - positively buoyant time series plastic_2_ts.nc - neutrally buoyant time series plastic_3_ts.nc - negatively buoyant time series plastic_input_ORCA2.nc - plastic input data file ORCA2_5d_00010101_00011231_grid_U.nc & ORCA2_5d_00010101_00011231_grid_V.nc - ocean velocity files
DOI: 10.5281/zenodo.4242969
title: Temperature response of water-saturated compact Longmenshan limestone and porous Rajasthan sandstone to changes in confining pressure

description: Three data sets are stored in this data submission. The first data set includes the temperature response of water-saturated compact Longmenshan limestone (L27) and porous Rajasthan sandstone (RJS) to changes in confining pressure under drained/undrained conditions. The second data set includes the thermal properties of rock-forming minerals and estimations of thermal characteristic time/distance. The third data set includes the internal temperature evolution of the water-saturated sample within 1 s after instantaneous loading in models M-01, M-02 and M03, in which the thermal properties of the solid grains are set to be that of gypsum, average values of the main rock-minerals and <em>α</em>-quartz, respectively.
description: This data set will be used in the following manuscript: Xiaoqiu Yang, Weiren Lin, Osamu Tadai, Xin Zeng, Junfeng Zhao, Hehua Xu. Theoretical and experimental analyses of temperature responses of water-saturated rocks to changes in confining pressure. Ready to submit to Journal of Geophysical Research: Solid Earth.
DOI: 10.5281/zenodo.1094808
title: Raw Diffraction Images Of Human Muscarinic Acetylcholine Receptor

description: Raw data for 5ZK8 (M<sub>2</sub>-BRIL–NMS), 5ZKC (S110R-BRIL–NMS), 5ZKB(S110R-BRIL–AF-DX 384), and 5ZK3 (S110R-BRIL–QNB).
DOI: 10.5281/zenodo.6451104
title: Rapeseed Map Example data

description: This dataset provides 30m-grid rapeseed map example data. *** The data file is in “.tif" format *** Temporal Resolution: Yearly *** Pixel size: 30 m *** Projection information: EPSG: 4326
description: beijing normal university
DOI: 10.5281/zenodo.6907374
title: CH4 and CO2 dataset for the Qinghai-Tibetan Plateau rivers

description: 该数据集整理了青藏高原河流CH4和CO2浓度和通量的直接现场测量值,以及28个采样点的位置、水文、物理和化学条件信息。这些数据对于平衡区域和全球C预算将非常有价值。
DOI: 10.5281/zenodo.3251643
title: Ionic liquid Properties

description: The set contains experimental data for 12 different properties: melting point, glass transition temperature, thermal decomposition temperature, viscosity, density, heat capacity, CO2 capacity, electrical and thermal conductivity, cytotoxicity towards the leukemia rat cell line IPC-81, surface tension and refractive index. The accompanying PDF file contains the literature references from which the data has been obtained.  The structures for all the cations and anions are provided in the SMILES format.
DOI: 10.5281/zenodo.13160651
title: Binary black-hole simulation SXS:BBH:0807

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160652
title: Binary black-hole simulation SXS:BBH:0807

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.1027689
title: Ax1743 Npr-4(Tm1782)X | 2010-01-22T10:55:54+00:00

description: This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org



<b>preview link</b> : https://www.youtube.com/watch?v=yvj_zl95O1c
<b>strain</b> : AX1743
<b>timestamp</b> : 2010-01-22T10:55:54+00:00
<b>gene</b> : npr-4
<b>chromosome</b> : X
<b>allele</b> : tm1782
<b>strain_description</b> : npr-4(tm1782)X
<b>sex</b> : hermaphrodite
<b>stage</b> : adult
<b>ventral_side</b> : anticlockwise
<b>media</b> : NGM agar low peptone
<b>arena</b> :   
  <b>style</b> : petri
  <b>size</b> : 35
  <b>orientation</b> : away
  

<b>food</b> : OP50
<b>habituation</b> : 30m wait
<b>who</b> : Laura Grundy
<b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.
<b>lab</b> :   
  <b>name</b> : William R Schafer
  <b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
  

<b>software</b> :   
  <b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)
  <b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219
  <b>featureID</b> : @OMG
  

<b>base_name</b> : npr-4 (tm1782)X on food R_2010_01_22__10_55_54___8___3
<b>total time (s)</b> : 898.608
<b>frames per second</b> : 25.7732
<b>video micrometers per pixel</b> : 4.55526
<b>number of segmented skeletons</b> : 19621
DOI: 10.5281/zenodo.4515433
title: Smoke_Cholec80 - Endoscopic Smoke Detection Dataset

description: This dataset is an addition to the Cholec80 cholecystectomy dataset (https://doi.org/10.1109/TMI.2016.2593957). The archive contains mapping files for specific video segments containing <strong>smoke</strong> in various intensities and they are split up into two classes (see labels.csv): no smoke no visual signs of smoke<br> smoke smoke in various intensities The dataset altogether comprises 101054 images (50501 no smoke + 50553 smoke)<br> and the archive's subfolder cholec80_mappings is organized as follows: labels.csv mapping: label number -&gt; class<br> index.csv index linking source videos of Cholec80 with corresponding<br> mapping files. Contains video path (relative to Cholec80<br> archive), fps, mapping file path (relative to index.csv) and<br> frame counts per video for both labels.<br> videoXX.mp4.csv frame mapping for video XX: start frame, end frame, label You are kindly requested to cite the original work that led to the creation of the dataset: https://doi.org/10.1145/3126686.3126690.<br> Additionally, please adhere to the usage rights of the base dataset that was used to create these mappings, i.e. Cholec80 (http://camma.u-strasbg.fr/datasets). For the latest updates, please visit the dataset's homepage: http://ftp.itec.aau.at/datasets/Smoke_cholec80/.
description: {"references": ["Leibetseder et al.(2017), Real-Time Image-based Smoke Detection in Endoscopic Videos, https://doi.org/10.1145/3126686.3126690"]}
DOI: 10.15468/dl.hnksvp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.240872 -32.605042, 149.240872 -32.607288, 149.243118 -32.607288, 149.243118 -32.605042, 149.240872 -32.605042)), ((149.243118 -32.607288, 149.243118 -32.611779, 149.247609 -32.611779, 149.247609 -32.614025, 149.254347 -32.614025, 149.254347 -32.609533, 149.249855 -32.609533, 149.249855 -32.607288, 149.243118 -32.607288)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044988-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13160628
title: Open-Ended Dynamics of Coevolutionary Games Datasets
DOI: 10.5281/zenodo.13160629
title: Open-Ended Dynamics of Coevolutionary Games Datasets
DOI: 10.5281/zenodo.13160653
title: Binary black-hole simulation SXS:BBH:0645

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160654
title: Binary black-hole simulation SXS:BBH:0645

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.px2qaa
title: Occurrence Download

description: A dataset containing 9142 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["3021850"],"matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["Preserved Specimen","Material Citation"],"matchCase":false}]}

 The dataset includes 9142 records from 334 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044989-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpr9es
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.164515 -32.607288, 149.164515 -32.616271, 149.169006 -32.616271, 149.169006 -32.620762, 149.171252 -32.620762, 149.171252 -32.611779, 149.16676 -32.611779, 149.16676 -32.607288, 149.164515 -32.607288))"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044990-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13159877
title: Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"

description: This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.

Subdirectory "training" contains unnormalized (in physical units) training data.  Subdirectories "validation" and "testing" contain unnormalized validation and testing data.  Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper.  Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.

Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).

Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).
DOI: 10.5281/zenodo.13159878
title: Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"

description: This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.

Subdirectory "training" contains unnormalized (in physical units) training data.  Subdirectories "validation" and "testing" contain unnormalized validation and testing data.  Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper.  Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.

Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).

Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).
DOI: 10.5281/zenodo.13160655
title: Binary black-hole simulation SXS:BBH:0789

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160656
title: Binary black-hole simulation SXS:BBH:0789

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.aq7h2d
title: Occurrence Download

description: A dataset containing 307 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.557529 -32.607288, 149.571004 -32.607288, 149.571004 -32.598304, 149.568758 -32.598304, 149.568758 -32.605042, 149.557529 -32.605042, 149.557529 -32.607288)), ((149.557529 -32.607288, 149.550792 -32.607288, 149.550792 -32.614025, 149.557529 -32.614025, 149.557529 -32.607288)), ((149.571004 -32.607288, 149.571004 -32.614025, 149.577742 -32.614025, 149.577742 -32.611779, 149.57325 -32.611779, 149.57325 -32.607288, 149.571004 -32.607288)), ((149.577742 -32.614025, 149.577742 -32.618517, 149.579987 -32.618517, 149.579987 -32.614025, 149.577742 -32.614025)), ((149.579987 -32.618517, 149.579987 -32.620762, 149.584479 -32.620762, 149.584479 -32.618517, 149.579987 -32.618517)))"
 ]
}

 The dataset includes 307 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044991-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13160658
title: Binary black-hole simulation SXS:BBH:0651

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160659
title: Binary black-hole simulation SXS:BBH:0651

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.8ybgxf
title: Occurrence Download

description: A dataset containing 1192 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["2913914"],"matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["Preserved Specimen","Material Citation"],"matchCase":false}]}

 The dataset includes 1192 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044992-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g6mf4e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.371128 -32.611779, 149.371128 -32.618517, 149.377866 -32.618517, 149.377866 -32.614025, 149.373374 -32.614025, 149.373374 -32.611779, 149.371128 -32.611779))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044993-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.13160662
title: Binary black-hole simulation SXS:BBH:0739

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160663
title: Binary black-hole simulation SXS:BBH:0739

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160665
title: Binary black-hole simulation SXS:BBH:0712

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160666
title: Binary black-hole simulation SXS:BBH:0712

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.2uekrc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.133073 -32.614025, 149.133073 -32.620762, 149.135319 -32.620762, 149.135319 -32.614025, 149.133073 -32.614025)), ((149.135319 -32.620762, 149.135319 -32.629746, 149.139811 -32.629746, 149.139811 -32.6275, 149.144302 -32.6275, 149.144302 -32.623008, 149.142057 -32.623008, 149.142057 -32.620762, 149.135319 -32.620762)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044994-240626123714530/datasets/export for details.
DOI: 10.15468/dl.nyhatu
title: Occurrence Download

description: A dataset containing 7412 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"in","key":"TAXON_KEY","values":["5372647"],"matchCase":false},{"type":"equals","key":"HAS_COORDINATE","value":"true","matchCase":false},{"type":"in","key":"BASIS_OF_RECORD","values":["Preserved Specimen","Material Citation"],"matchCase":false}]}

 The dataset includes 7412 records from 188 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044995-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13160667
title: Binary black-hole simulation SXS:BBH:0783

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13160668
title: Binary black-hole simulation SXS:BBH:0783

description: Simulation of a black-hole binary system evolved by the SpEC code.