cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2535418
title: Data: Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy

description: We provide the generalised fractional anisotropy connectometry database used in our study titled: <em>Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy.</em>
DOI: 10.5281/zenodo.4073861
title: Data for the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter"

description: This contains the data used for the paper by Ahmed et. al in the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter". Each of the four CSV files corresponds to one of the quarters discussed in the paper, and each has a metadata file containing information about the query that produced them. The CSV files contain the 'raw' log messages, and an eventID that identifies which pattern the log entry matched; the eventID is used to group together log messages of the same type. These logfiles were collected on the CloudLab facility (https://cloudlab.us/) from Jan 1 - Dec 30, 2019. The violated_unviolated_sessions_*.txt files each contain 20 randomly-selected sessions: half of the sessions were labeled by the invariant miner as being 'normal', and the other half 'anomalous'. CloudLab developers and system administrators were asked to label these sessions manually (and were not given the invariant miner's labels). The corresponding *_manual_labels.txt contain the labels that the administrators assigned, and in some cases additional correspondence with the administrators and information about which manual labels matched the invariant miner and which did not.
DOI: 10.5281/zenodo.4593860
title: Data_WP2_5_Life_cycle_data for relevant processes and activities data set

description: The dataset includes life cycle emission data relevant to the activities and processes identified in WP5. This includes life cycle emission factors for the road transport, heating sector as well as electricity consumption. Lifecycle emission factors for electricity consumption (filename: LCDB_Electricity_FINAL.xlsx) comprise several possible future developments of the European electricity market, including higher penetration of renewable energy.<br> For the buildings, separate emission factors are generated for insulation (filename: LCDB_Buildings_insulation_FINAL.xlsx)<br> and heating technologies (filename: LCDB_Buildings_heating_FINAL.xlsx). Lifecycle emissions comprise emissions from production,<br> transport (of goods/fuels) and disposal. For transport (filename: LCDB_Transport_Final.xlsx), separate lifecycle emission factors for different vehicle types are collected, split into production, operation, disposal, infrastructure and fuel cycle. All data are documented in deliverable D2.3 freely downloadable from the ICARUS website.
DOI: 10.5281/zenodo.4775291
title: Russian Index of the Research Organizations (RIRO)

description: The RIRO dataset contains standardized official information, geodata, and various identifiers of research organizations in Russia, all interlinked via unified primary key. Currently covered data sources include ROR\GRID, WIkiData, Scopus\Scival IDs, InCites\Web of Science names, and the IDs assigned by the Russian Ministry of Science & Higher Education. RIRO lists not only the head organizations, but also their branches and last predecessors, which allows to build an hierarchy and analyze a history of mergers\liquidations using the linked IDs. RIRO is aimed to become a valuable tool for both research managers and academic scholars in the fields of science policy, scientometrics, science and technology studies. www: RIRO Project site
DOI: 10.5281/zenodo.400776
title: Data For Article: Time-Resolved Spectroscopic Investigation Of Charge Trapping In Carbon Nitrides Photocatalysts For Hydrogen Generation

description: This is the data presented in the article titled 'Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation', published in the Journal of the American Chemical Society. DOI:10.1021/jacs.7b01547

http://pubs.acs.org/doi/abs/10.1021/jacs.7b01547
DOI: 10.17632/hnkn8kgvsh.2
title: Source code for：Research on vision-based tile recognition algorithms for robot grasping task in EAST tokamak

description: source code of section 3.1 and 3.2
DOI: 10.5281/zenodo.3639923
title: Phenotype data (composition, functionality, end-product quality) of wheat grown in Flanders, Belgium

description: Data for the Ph.D. dissertation entitled: Outline of four degrees of diversification for understanding bread wheat (<em>Triticum aestivum</em> L.) quality. For further details on the content of the files, the meaning of abbreviations and used techniques, it is referred to the Ph.D. manuscript. For further details, please contact the author.
DOI: 10.5281/zenodo.4660670
title: DAPS (Device and Produced Speech) Dataset

description: The DAPS (Device and Produced Speech) dataset is a collection of aligned versions of professionally produced studio speech recordings and recordings of the same speech on common consumer devices (tablet and smartphone) in real-world environments. It has 15 versions of audio (3 professional versions and 12 consumer device/real-world environment combinations). Each version consists of about 4 1/2 hours of data (about 14 minutes from each of 20 speakers). Please see this paper for a detailed description of the dataset: Gautham J. Mysore, “Can We Automatically Transform Speech Recorded on Common Consumer Devices in Real-World Environments into Professional Production Quality Speech? - A Dataset, Insights, and Challenges”, in the IEEE Signal Processing Letters, Vol. 22, No. 8, August 2015 The primary goal of the dataset is to help develop methods to automatically convert real-world device recordings into professional sounding recordings. It can be also used for various other applications like voice conversion, traditional speech enhancement, and automatic production of studio recordings.
DOI: 10.5281/zenodo.3678385
title: Exploring archaeal and bacterial diversity and co-occurrence in Lake Geneva

description: Fastq files, OTU tables, tags list.
DOI: 10.5281/zenodo.4601174
title: Public MetaPath database (EFSA project)

description: This MTB file contains a collection of maps publicly available that can be used with the MetaPath platform to search and analyse experimental data on metabolism or catabolism. For more details and background information please consult EFSA website <strong>Intellectual Property Rights Notice</strong> The reproduction, distribution, redistribution, exploiting, making commercial or further use of information, documents and data posted or otherwise made available on this website or in the websites linked to it may be subject to protection under intellectual property rights regulations, data exclusivity clauses or other applicable law, and their utilisation without obtaining the prior permission from the right(s)holder(s) of the respective information, documents and data might violate the pre-existing rights of the respective right(s)holder(s). For materials subject to intellectual property rights or other rights of a third party, the User must comply with the terms of use associated with such material or obtain the necessary and written permission for reproduction, distribution or any other use from the right(s)holder(s). EFSA does not accept any responsibility, and shall not be held liable, for any violation of any pre-existing rights or other infringements related to information, documents and data made available on this website or in the websites linked to it.
description: IT; MTB; pesticides.MRL@efsa.europa.eu
DOI: 10.5281/zenodo.4662453
title: View-Dependent Progressive Transmission Method for 3D Building Models

description: 这是论文“ 3D建筑模型的视图相关渐进传输方法”中的实验数据
DOI: 10.5281/zenodo.3370810
title: Publication analysis on MDD

description: Supplementary material 1: original data of MDD publication, Supplementary material 2: python code for analysis.
DOI: 10.5281/zenodo.6571168
title: A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

description: Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p&lt;0.05) and MOD10C2 during 2001–2019 (r=0.97, p&lt;0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if filled by IMS snow climatology. Spatial Coverage: N: 90, S: 0, E: 180, W: -180<br> Spatial Resolution: 0.05 deg x 0.05 deg<br> Samples = 7200<br> Lines = 1800<br> Temporal Coverage: September 1981 to December 2019<br> Temporal Resolution: 8-day
DOI: 10.5281/zenodo.13290
title: Questionnaires And Raw Data For The Paper "Operationalised Product Quality Models And Assessment: The Quamoco Approach"

description: This paper contains the quantitative data from several interviews along with a used questionnaire for empirically investigating the software quality models created in the project Quamoco.
DOI: 10.34740/kaggle/dsv/5290183
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.5281/zenodo.7792125
title: Clean-MusicXML-CRE-TheSession

description: This dataset contains the cleaned version of MusicXML files converted from two datasets: CRE and The Session. Both datasets are about Irish tunes.
DOI: 10.5281/zenodo.7792124
title: Clean-MusicXML-CRE-TheSession

description: This dataset contains the cleaned version of MusicXML files converted from two datasets: CRE and The Session. Both datasets are about Irish tunes.
DOI: 10.6084/m9.figshare.21430500
title: Data availability Gut Microbes last version.xlsx

description: Dataset of the paper "Gut microbiota modulates visceral sensitivity through CGRP production"
DOI: 10.5281/zenodo.7792129
title: Original-MusicXML-CRE-TheSession

description: This dataset contains the original version of MusicXML files converted from two datasets: CRE and The Session. Both datasets are about Irish tunes.
DOI: 10.5281/zenodo.7792128
title: Original-MusicXML-CRE-TheSession

description: This dataset contains the original version of MusicXML files converted from two datasets: CRE and The Session. Both datasets are about Irish tunes.
DOI: 10.5281/zenodo.7792131
title: The knowledge graphs generated from CRE and The Session datasets

description: This dataset contains the knowledge graphs generated from CRE and the Session datasets. All the knowledge graphs are in TTL format.
DOI: 10.5281/zenodo.7792130
title: The knowledge graphs generated from CRE and The Session datasets

description: This dataset contains the knowledge graphs generated from CRE and the Session datasets. All the knowledge graphs are in TTL format.
DOI: 10.3886/e187941
title: Experience of Sharing Life Story

description: These data include the dialogue between interviewer and participant about how the participant experienced telling a life story. Study participants included 118 women who had extensive criminal histories and substance involvement, and thus whose backgrounds included many adverse effects.<br>
description: Not applicable.<br>
description: Response Rates: Not applicable. This is a purposive sample from a hard to reach group.D<br>
description: Presence of Common Scales: Not applicable.<br>
description: This is a subsample from a larger study of 402 substance-involved women, who are the most typical subgroup of women offenders.To recruit the 402 women, first 73<br>probation and parole agents were recruited from 16 Michigan counties located within an hour and a half drive from the research office. The counties contained a mix of urban, suburban, and rural locations. The agents identified 846 women who met the criteria: convicted of a felony, had been supervised for about three months, and had a history of substance misuse. Recruitment stopped after 402 women agreed to<br>participate. For the present data collection and analyses, women in the subsample of 118 were chosen if they had at least five convictions prior to the first interview.<br>We started with a list of 168 women randomly selected from all those with a history of at least five convictions. Of the 168 women, 32 could not be located, nine had moved out of state, three declined to participate, two had corrupted audiotapes, two were deceased, one was in a jail that would not allow an interview, and one discontinued the interview to keep an appointment.<br><br>
description: 118 women with extensive criminal histories and drug involvement and high prevalence of abuse, loss, and other harms in their life histories<br>. Smallest Geographic Unit: Not applicable
description: face-to-face interview;
DOI: 10.3886/e187941v1
title: Experience of Sharing Life Story

description: These data include the dialogue between interviewer and participant about how the participant experienced telling a life story. Study participants included 118 women who had extensive criminal histories and substance involvement, and thus whose backgrounds included many adverse effects.<br>
description: Not applicable.<br>
description: Response Rates: Not applicable. This is a purposive sample from a hard to reach group.D<br>
description: Presence of Common Scales: Not applicable.<br>
description: This is a subsample from a larger study of 402 substance-involved women, who are the most typical subgroup of women offenders.To recruit the 402 women, first 73<br>probation and parole agents were recruited from 16 Michigan counties located within an hour and a half drive from the research office. The counties contained a mix of urban, suburban, and rural locations. The agents identified 846 women who met the criteria: convicted of a felony, had been supervised for about three months, and had a history of substance misuse. Recruitment stopped after 402 women agreed to<br>participate. For the present data collection and analyses, women in the subsample of 118 were chosen if they had at least five convictions prior to the first interview.<br>We started with a list of 168 women randomly selected from all those with a history of at least five convictions. Of the 168 women, 32 could not be located, nine had moved out of state, three declined to participate, two had corrupted audiotapes, two were deceased, one was in a jail that would not allow an interview, and one discontinued the interview to keep an appointment.<br><br>
description: 118 women with extensive criminal histories and drug involvement and high prevalence of abuse, loss, and other harms in their life histories<br>. Smallest Geographic Unit: Not applicable
description: face-to-face interview;
DOI: 10.25582/data.2021-07.2277667/1964212
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2281127/1964213
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271451/1964214
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2277592/1964215
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2277619/1964216
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2185665/1964217
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2278974/1964218
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2278990/1964219
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2282309/1964220
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2167390/1964221
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2271591/1964222
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271786/1964223
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271921/1964224
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271705/1964225
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2277414/1964226
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2282325/1964227
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2178996/1964228
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2271554/1964229
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2283910/1964230
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2286588/1964231
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2167880/1964232
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2280576/1964233
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2286556/1964234
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2167339/1964235
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2282838/1964236
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2167619/1964237
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2271803/1964238
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2020-11.2095871/1964239
title: Data for EMSL Project 51467 from November 2020
DOI: 10.25582/data.2021-07.2290631/1964240
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2166802/1964241
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2278864/1964242
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2185538/1964243
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2277679/1964244
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271871/1964245
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2166926/1964246
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-02.2165722/1964247
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-03.2175225/1964248
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2277281/1964249
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2179838/1964250
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2277603/1964251
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2167883/1964252
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2176210/1964253
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-08.2302222/1964254
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-02.2167505/1964255
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-02.2166702/1964256
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-03.2185584/1964257
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2282744/1964258
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2167876/1964259
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2175554/1964260
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2176297/1964261
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2185855/1964262
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2279055/1964263
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271689/1964264
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2280990/1964265
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-08.2302205/1964266
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-03.2185818/1964267
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2271728/1964268
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2278930/1964269
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-08.2308197/1964270
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-02.2167362/1964271
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2280695/1964272
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2280760/1964273
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2277719/1964274
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2282378/1964275
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-02.2166991/1964276
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2271798/1964277
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2278988/1964278
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2271919/1964279
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-07.2280912/1964280
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2020-11.2095879/1964281
title: Data for EMSL Project 51467 from November 2020
DOI: 10.25582/data.2021-02.2148249/1964282
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-02.2166774/1964283
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-07.2279094/1964284
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-08.2296751/1964285
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-03.2176744/1964286
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2271808/1964287
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-03.2175665/1964288
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2176219/1964289
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-03.2185878/1964290
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2280649/1964291
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-08.2307138/1964292
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-03.2176196/1964293
title: Data for EMSL Project 51467 from March 2021
DOI: 10.25582/data.2021-07.2271528/1964294
title: Data for EMSL Project 51467 from July 2021
DOI: 10.25582/data.2021-08.2305487/1964295
title: Data for EMSL Project 51467 from August 2021
DOI: 10.25582/data.2021-02.2167450/1964296
title: Data for EMSL Project 51467 from February 2021
DOI: 10.25582/data.2021-02.2147858/1964297
title: Data for EMSL Project 51467 from February 2021