cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wunwcg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tephroseris longifolia subsp. moravica Holub",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112430-240626123714530/datasets/export for details.
DOI: 10.15468/dl.exkbf2
title: Occurrence Download

description: A dataset containing 62 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stipa zalesskii Wilensky",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 62 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112431-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qw7q7n
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Living Specimen)",
 "TaxonKey is Brassia ocanensis Lindl."
 ]
}

 The dataset includes 56 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112432-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5bt42c
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aconitum firmum subsp. moravicum Skalický",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 13 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112433-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.15178
title: Test pre audit
title: 测试AI预审

description: Testing AI Prequalification Testing AI Prequalification Testing AI Prequalification Testing AI Prequalification Testing 
description: Testing AI Prequalification Testing AI Prequalification Testing AI Prequalification Testing AI Prequalification Testing
DOI: 10.57760/sciencedb.02715
title: 状态5测试
title: 状态5测试

description: 状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试
description: 状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试状态5测试
DOI: 10.15468/dl.gwqkrg
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galium sudeticum Tausch",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112434-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6uq6u
title: Occurrence Download

description: A dataset containing 24499 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Orcinus orca (Linnaeus, 1758)"
 ]
}

 The dataset includes 24499 records from 393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112435-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.09988
title: 限制性获取测试数据
title: 限制性获取测试数据

description: 限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据
description: 限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据限制性获取测试数据
DOI: 10.5281/zenodo.163857
title: Human And Machine Judgements For Russian Semantic Relatedness

description: Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj , similarityij ). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy.

For more details see: 


	The web page of the RUSSE evaluation campaign: http://russe.nlpub.ru/downloads
	The original publication "Panchenko A., Ustalov D., Arefyev N., Paperno D. Konstantinova N., Loukachevitch N. and Biemann C. undefinedHuman and Machine Judgements about Russian Semantic Relatedness. In Proceedings of the 5th Conference on Analysis of Images, Social Networks and Texts (AIST'2016). Communications in Computer and Information Science (CCIS). Springler-Verlag Berlin Heidelberg": https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/aist_2016_hmj.pdf
DOI: 10.5281/zenodo.6757992
title: Lightning waveform data

description: The initials N and P in the name of each file represent negative and positive, respectively.
DOI: 10.57760/sciencedb.08154
title: Test_neo4j_Y_1
title: Test_neo4j_Y_1

description: • If necessary, please provide appropriate explanations on whether there are errors in the data, the range of errors, or the reasons for introducing errors (if applicable); • The content of each data file and its descriptive information (if applicable); If the format of the data file you submitted is relatively niche, please provide information such as the applicable software and the official download address of the software (if applicable). The above suggestions are for reference only. If there are any omissions, the author is kindly requested to supplement and improve your dataset introduction. Your dataset introduction should be organized into one or more natural segment shapes
description: • If necessary, please provide appropriate explanations on whether there are errors in the data, the range of errors, or the reasons for introducing errors (if applicable); • The content of each data file and its descriptive information (if applicable); If the format of the data file you submitted is relatively niche, please provide information such as the applicable software and the official download address of the software (if applicable). The above suggestions are for reference only. If there are any omissions, the author is kindly requested to supplement and improve your dataset introduction. Your dataset introduction should be organized into one or more natural segment shapes
DOI: 10.5281/zenodo.7709898
title: Retrieval of dominant methane (CH4) emission sources, the first high resolution (1-2m) dataset of storage tanks of China in 2000-2021

description: We presented a storage tank inventory of 92 typical cities with high volatile organic compounds, in response to the Three-Year Action Plan for Winning the Blue Sky Defense War, proposed by the State Council of China. It comprises of 14461 storage tanks, which are extracted based on high spatail resolution images of GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan satellites in year of 2021. It is the first inventory covering detailed distribution locations and boundaries of storage tanks. The inventory we aim to share provides a basic distribution of storage tanks in different sizes and will contribute to support environmentally friendly regulations proposal for more effective pollution control and energy resource management.
DOI: 10.5281/zenodo.4271144
title: Kenya Cropland Map and Non-Crop Labelled dataset

description: This dataset provides a 10m resolution map of cropland in Kenya for the 2019-2020 growing season (kenya_cropland_binary_2019.tif.zip) and a 10m resolution map of cropland in Busia County, Kenya for the 2020-2021 growing season (busia_cropland_binary_2020.tif.zip). Each pixel has a binary value, 0 if it does not contain crops and 1 if it does. These values were obtained by thresholding the predictions of an LSTM classifier trained on multi-spectral time series of Sentinel-2 satellite observations. A thresholding value of 0.5 was used. This dataset also provides the hand-labelled non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2020. Annual and in-season mapping of cropland at field scale with sparse labels. Tackling Climate Change with Machine Learning workshop at NeurIPS ’20: December 11th, 2020
DOI: 10.5281/zenodo.5866312
title: SWAM model from GTEx dataset

description: The SWAM models for each GTEx tissue v6 and v8 are publicly available at https://doi.org/10.5281/zenodo.5866500 . These models are trained from GTEx PrediXcan models across all tissues, targeting each tissue type, and formatted in a format that can be directly used by the PrediXcan software tool. See https://github.com/aeyliu/SWAM and https://github.com/aeyliu/SWAM-manuscript for the detailed code to reproduce these resources. More comprehensive information will be available when this resource is linked with a peer-reviewed publication.
description: {"references": ["Liu A, Kang HM. Meta-imputation of transcriptome from genotypes across multiple datasets using summary-level data. bioRxiv. 2021 Jan 1."]}
DOI: 10.5281/zenodo.56471
title: Adult Picky Eaters With Symptoms Of Avoidant/Restrictive Food Intake Disorder: Comparable Distress And Comorbidity But Different Eating Behaviors Compared To Those With Disordered Eating Symptoms

description: Picky eaters are people who avoid many new and familiar foods because they dislike the taste, smell, texture, or appearance of many foods. When it is severe, picky eating can lead to weight loss or difficulty maintaining a healthy weight, nutritional deficiencies, dependence on supplements to get adequate nutrition or calories, or shame, anxiety, or difficulty engaging in daily life. When severe picky eating leads to these consequences, it can be diagnosed as Avoidant/Restrictive Food Intake Disorder (ARFID). People who restrict the amount of food they consume (or engage in exercise, purging, or fasting behaviors to get rid of calories after eating) because they are afraid of gaining weight or being fat are diagnosed with anorexia or bulimia when their eating leads to weight loss, nutritional problems, or interferes with life. ARFID is a new diagnosis, and in this paper, we show that 1) adults with ARFID symptoms are just as distressed, and just as likely to have symptoms of depression, anxiety, and obsessive compulsive disorder, as those with anorexia or bulimia, but that 2) adults with ARFID show very different types of eating behavior from adults with symptoms of anorexia or bulimia.
DOI: 10.5281/zenodo.5919442
title: 1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series)

description: ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.
DOI: 10.5281/zenodo.6985649
title: CropHarvest Dataset

description: Dataset being developed for the CropHarvest package and benchmarks.
DOI: 10.15468/dl.jb3bfy
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Romanogobio belingi (Slastenenko, 1934)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 154 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112436-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.5864676
title: Suspended particulate matter concentrations in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI

description: The Suspended particulate matter (SPM) concentrations product in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI. We applied Landsat-5 TM and Landsat-8 OLI satellite data to track SPM concentration changes from 1984 to 2020 in Lake Taihu. Landsat-7 ETM+ data was not used due to its striping noise. The SPM estimation models were calibrated and validated using in situ SPM, remote sensing reflectance (Rrs) data and synchronous satellite data. After comparison, it was found that the accuracy of the model based on band combination ((𝑅𝑟𝑠(𝑟𝑒𝑑)+𝑅𝑟𝑠(𝑛𝑒𝑎𝑟−𝑖𝑛𝑓𝑟𝑎𝑟𝑒𝑑))/𝑅𝑟𝑠(𝑔𝑟𝑒𝑒𝑛) was the highest, with the average unbiased relative error &lt;35%. The spatiotemporal distribution pattern of the SPM analyses in Lake Taihu could facilitate local water resource conservation.
DOI: 10.5281/zenodo.4327118
title: UAV thermal image dataset

description: The thermal images dataset is captured by DJI Matrice 210 Drone equipped with FLIR thermal camera for SAR operation.
DOI: 10.5281/zenodo.5864677
title: Suspended particulate matter concentrations in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI

description: The Suspended particulate matter (SPM) concentrations product in Lake Taihu from 1984 to 2020 based on Landsat TM and OLI. We applied Landsat-5 TM and Landsat-8 OLI satellite data to track SPM concentration changes from 1984 to 2020 in Lake Taihu. Landsat-7 ETM+ data was not used due to its striping noise. The SPM estimation models were calibrated and validated using in situ SPM, remote sensing reflectance (Rrs) data and synchronous satellite data. After comparison, it was found that the accuracy of the model based on band combination ((𝑅𝑟𝑠(𝑟𝑒𝑑)+𝑅𝑟𝑠(𝑛𝑒𝑎𝑟−𝑖𝑛𝑓𝑟𝑎𝑟𝑒𝑑))/𝑅𝑟𝑠(𝑔𝑟𝑒𝑒𝑛) was the highest, with the average unbiased relative error &lt;35%. The spatiotemporal distribution pattern of the SPM analyses in Lake Taihu could facilitate local water resource conservation.
DOI: 10.5281/zenodo.6376160
title: Crop classification dataset for testing domain adaptation or distributional shift methods

description: In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: https://doi.org/10.1016/j.rse.2021.112488 (arXiv version: https://arxiv.org/pdf/2109.01246.pdf). In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts. More details on the dataset and processing steps can be found in Kluger et. al. (2021). Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in Yeh et. al. (2021), and the raw dataset from France can be made available upon request from the authors of this Zenodo upload. All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf". <strong>Preferred Citation:</strong> -Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488. -URL to this Zenodo post https://zenodo.org/record/6376160
description: {"references": ["Kluger D.M., Wang S., and Lobell, D.B. (2021). Two shifts for crop mapping: leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sensing of Environment. 262, 112488", "C. Yeh, C. Meng, S. Wang, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. Lobell, and S. Ermon, \"SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning,\" in Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (Round 2), Dec. 2021."]}
DOI: 10.5281/zenodo.2547428
title: Dataset _ Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor

description: This is the dataset used for the publication of the journal article title<em> “</em><strong>Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.
description: {"references": ["Alc\u00e1ntara et al (2015), Andriani et al (2014), APHA (2005), Bahr et al (2014), Bi and He (2013)", "Cai et al (2013), European Biogas Association (2016), Farroq et al (2018), Lebrero et al (2016)", "Meier et al (2015), Mendoza et al (2013), Metcalf (2003), Mu\u00f1oz et al (2015), Norvill et al (2017)", "Park et al (2011), Posadas et al (2017a), Posadas et al (2014), Posadas et al (2015)", "Posadas et al (2016), Posadas et al (2017b), Ryckebosch et al (2011), Sander (2015)", "Serejo et al (2015), Toledo-Cervantes et al (2016), Toledo-cervantes et al (2017a)", "Toledo-Cervantes et al (2017b), Torzillo el al (2003)"]}
DOI: 10.5281/zenodo.3266178
title: Brugada ECG Database

description: ECG measurements of a cohort of 119 Brugada syndrome patients Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee of the Hospital Authority. They have been anonymised and are deposited for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit Access is permitted for research purposes only. The use of these datasets should provide acknowledgements of such efforts by citing this DOI.
description: Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong – New Territories East Cluster Clinical Research Ethics Committee and/or the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster of the Hospital Authority. They are published for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit The use of these datasets should provide acknowledgements of such efforts by citing this DOI.
DOI: 10.5281/zenodo.4010620
title: Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms

description: This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs. <em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em> Features ordered as in the shared dataset: Family: DGA that the domain belongs to SLD: SLD of the Domain L-LEN: The length of Domain L-DIG: The number of digits in Domain L-CON-MAX: The maximum number of consecutive consonants Domain R-CON-VOW: Number of consonants divided by L-LEN L-SYM: The number of special characters R-SYM-LEN: L-SYM divided by L-LEN R-Dom-3G: Ratio of benign grams in Dom-3G R-Dom-4G: Ratio of benign grams in Dom-4G R-Dom-5G: Ratio of benign grams in Dom-5G L-W2: Number of words with more than 2 characters in Domain L-W3: Number of words with more than 3 characters in Domain R-WS-LEN: Dom-WS divided by L-LEN R-WDS-LEN: Dom-WDS divided by L-LEN R-W2-LEN: Dom-W2 divided by L-LEN R-W3-LEN: Dom-W3 divided by L-LEN M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS E-Dom-WS: Entropy of Dom-WS E-Dom-WDS: Entropy of Dom-WDS E-Dom-W2: Entropy of Dom-W2 E-Dom-W3: Entropy of Dom-W3
DOI: 10.5281/zenodo.5637338
title: Barchan Dunes: Geomorphometric Data

description: <strong>Purpose of the Dataset</strong> These data were gathered as the foundation for a geomorphometric study of barchan dunes. The intent is for the data to be used in bulk (for the law of large numbers) because of randomly distributed measurement errors (see error analysis section in the BarchanDataDescription.docx file). Data for individual dunes should be used with caution. More details are provided in the BarchanDataDescription.docx file. <strong>Dataset heading</strong> For the BarchanDunes-GeomorphometricData.xlsx file, Column A, name and/or country of dune field; Column B, sample (dune) number (ED and MD refer to Earth dune and Mars dune); W<sub>1</sub> and W<sub>2</sub> are body width and horn-to-horn width; L<sub>1</sub> and L<sub>2</sub> are body length and total length; H<sub>1</sub> and H<sub>2</sub> are the horn lengths; and latitude and longitude are in decimal degrees. Negative values for latitude and longitude represent coordinates in the southern or western hemispheres, respectfully.
DOI: 10.5281/zenodo.6548090
title: GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

description: Supplementary data for the paper "GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations" by the GPUMD developers.