cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19630353
title: 1991-2021 Bibliographic records of Computer Science research in Nigeria extracted from Scopus

description: This dataset contained 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) extracted from the Scopus database. The data was extracted using the search string: SUBJ(“COMP”) AND AFFILCOUNTRY(“Nigeria”) AND PUBYEAR &gt; 1990 AND PUBYEAR &lt; 2022. The data can be used to examine the development of Computer Science research in Nigeria. This can help to gain insight into the conceptual, social, and intellectual structures of Computer Science research in Nigeria. The findings of the study will be useful for researchers conducting Computer Science related research in Nigeria.
DOI: 10.6084/m9.figshare.19630353.v2
title: 1991-2021 Bibliographic records of Computer Science research in Nigeria extracted from Scopus

description: This dataset contained 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) extracted from the Scopus database. The data was extracted using the search string: SUBJ(“COMP”) AND AFFILCOUNTRY(“Nigeria”) AND PUBYEAR &gt; 1990 AND PUBYEAR &lt; 2022. The data can be used to examine the development of Computer Science research in Nigeria. This can help to gain insight into the conceptual, social, and intellectual structures of Computer Science research in Nigeria. The findings of the study will be useful for researchers conducting Computer Science related research in Nigeria.
DOI: 10.6084/m9.figshare.19630353.v1
title: 1991-2021 Bibliographic records of Computer Science research in Nigeria extracted from Scopus

description: This dataset contained 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) extracted from the Scopus database. The data was extracted using the search string: SUBJ(“COMP”) AND AFFILCOUNTRY(“Nigeria”) AND PUBYEAR &gt; 1990 AND PUBYEAR &lt; 2022. The data can be used to examine the development of Computer Science research in Nigeria. This can help to gain insight into the conceptual, social, and intellectual structures of Computer Science research in Nigeria. The findings of the study will be useful for researchers conducting Computer Science related research in Nigeria.
DOI: 10.6084/m9.figshare.19635970
title: rstransa.cls from Aether, dark energy and string compactifications

description: The nineteenth-century aether died with special relativity but was resurrected by general relativity in the form of dark energy; a tensile material with tension equal to its energy density. Such a material is provided by the D-branes of string-theory; these can support the fields of supersymmetric particle-physics, although their energy density is cancelled by orientifold singularities upon compactification. Dark energy can still arise from supersymmetry-breaking anti-D-branes but it is probably time-dependent. Recent results on time-dependent compactifications to an FLRW universe with late-time accelerated expansion are reviewed.This article is part of the theme issue ‘The future of mathematical cosmology, volume 2’.
DOI: 10.6084/m9.figshare.19635970.v1
title: rstransa.cls from Aether, dark energy and string compactifications

description: The nineteenth-century aether died with special relativity but was resurrected by general relativity in the form of dark energy; a tensile material with tension equal to its energy density. Such a material is provided by the D-branes of string-theory; these can support the fields of supersymmetric particle-physics, although their energy density is cancelled by orientifold singularities upon compactification. Dark energy can still arise from supersymmetry-breaking anti-D-branes but it is probably time-dependent. Recent results on time-dependent compactifications to an FLRW universe with late-time accelerated expansion are reviewed.This article is part of the theme issue ‘The future of mathematical cosmology, volume 2’.
DOI: 10.5281/zenodo.6476919
title: Exoplanet imaging data challenge, phase 2

description: <strong>Datasets for the second phase of the Exoplanet Imaging Data Challenge</strong> (https://exoplanet-imaging-challenge.github.io). The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must follow two tasks (1) astrometry of the detected signals, (2) spectrophotometry of the detected signals. For this phase, we therefore provide with <strong>8</strong> high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in <em>.fits</em> format): (a) <em>image_cube_instxx.fits</em>: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode;<br> (b) <em>parallactic_angles_instxx.fits</em>: the corresponding parallactic angle and airmass variation during the observation sequence;<br> (c) <em>wavelength_vect_instxx.fits</em>: the corresponding wavelength vector for each spectral channel;<br> (d) <em>psf_cube_instxx.fits</em>: the non-coronagraphic (point spread function) multispectral image of the target star;<br> (e) <em>first_guess_astrometry_instxx.fits</em>: a first guess position w.r.t the star of the (2 or 3) injected planetary signals. Each data set has 2 to 3 injected planetary signals in various locations.<br> Each data set has very different observing conditions, from very good to very bad. <em>Additional information and ressources can be found on the website and dedicated Github repository.</em> <em>The results of the data challenge must be submitted directly by participants on the EvalAI platform.</em>
DOI: 10.5281/zenodo.6415023
title: Whole-genome genotype data for French Large White pigs from two distinct sampling times

description: Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909. Two different genotype datasets were obtained from the raw WGS: 1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90% 2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software. More details about these datasets and their use can be found in the following study: Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.
description: {"references": ["Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines."]}
DOI: 10.5281/zenodo.6415022
title: Whole-genome genotype data for French Large White pigs from two distinct sampling times

description: Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909. Two different genotype datasets were obtained from the raw WGS: 1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90% 2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software. More details about these datasets and their use can be found in the following study: Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.
description: {"references": ["Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines."]}
DOI: 10.34740/kaggle/dsv/3509974
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.3063822
title: TAU Urban Acoustic Scenes 2019, Evaluation dataset

description: TAU Urban Acoustic Scenes 2019 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes: Airport - <em>airport</em> Indoor shopping mall - <em>shopping_mall</em> Metro station - <em>metro_station</em> Pedestrian street - <em>street_pedestrian</em> Public square - <em>public_square</em> Street with medium level of traffic - <em>street_traffic</em> Travelling by a tram - <em>tram</em> Travelling by a bus - <em>bus</em> Travelling by an underground metro - <em>metro</em> Urban park - <em>park</em> The dataset contains in total 20 hours of audio.
DOI: 10.5281/zenodo.3063821
title: TAU Urban Acoustic Scenes 2019, Evaluation dataset

description: TAU Urban Acoustic Scenes 2019 evaluation dataset consists of 10-seconds audio segments from 10 acoustic scenes: Airport - <em>airport</em> Indoor shopping mall - <em>shopping_mall</em> Metro station - <em>metro_station</em> Pedestrian street - <em>street_pedestrian</em> Public square - <em>public_square</em> Street with medium level of traffic - <em>street_traffic</em> Travelling by a tram - <em>tram</em> Travelling by a bus - <em>bus</em> Travelling by an underground metro - <em>metro</em> Urban park - <em>park</em> The dataset contains in total 20 hours of audio.
DOI: 10.5281/zenodo.6473207
title: Clotho-AQA dataset

description: Clotho-AQA is an audio question-answering dataset consisting of 1991 audio samples taken from Clotho dataset [1]. Each audio sample has 6 associated questions collected through crowdsourcing. For each question, the answers are provided by three different annotators making a total of 35,838 question-answer pairs. For each audio sample, 4 questions are designed to be answered with 'yes' or 'no', while the remaining two questions are designed to be answered in a single word. More details about the data collection process and data splitting process can be found in our following paper. <em><strong>S. Lipping, P. Sudarsanam, K. Drossos, T. Virtanen ‘Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering.’ </strong></em>The paper is available online at 2204.09634.pdf (arxiv.org) If you use the Clotho-AQA dataset, please cite the paper mentioned above. A sample baseline model to use the Clotho-AQA dataset can be found at partha2409/AquaNet (github.com) To use the dataset, • Download and extract <strong>‘audio_files.zip’</strong>. This contains all the 1991 audio samples in the dataset. • Download <strong>‘clotho_aqa_train.csv’, ‘clotho_aqa_val.csv’, </strong>and <strong>‘clotho_aqa_test.csv’</strong>. These files contain the train, validation, and test splits, respectively. They contain the audio file name, questions, answers, and confidence scores provided by the annotators. <strong>License:</strong> The audio files in the archive ‘audio_files.zip’ are under the corresponding licenses (mostly CreativeCommons with attribution) of Freesound [2] platform, mentioned explicitly in the CSV file <strong>’clotho_aqa_metadata.csv’</strong> for each of the audio files. That is, each audio file in the archive is listed in the CSV file with meta-data. The meta-data for each file are: • File name • Keywords • URL for the original audio file • Start and ending samples for the excerpt that is used in the Clotho dataset • Uploader/user in the Freesound platform (manufacturer) • Link to the license of the file. The questions and answers in the files: • clotho_aqa_train.csv • clotho_aqa_val.csv • clotho_aqa_test.csv are under the MIT license, described in the LICENSE file. <strong>References:</strong> [1] K. Drossos, S. Lipping and T. Virtanen, "Clotho: An Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736- 740, doi: 10.1109/ICASSP40776.2020.9052990. [2] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245
DOI: 10.5281/zenodo.6473206
title: Clotho-AQA dataset

description: Clotho-AQA is an audio question-answering dataset consisting of 1991 audio samples taken from Clotho dataset [1]. Each audio sample has 6 associated questions collected through crowdsourcing. For each question, the answers are provided by three different annotators making a total of 35,838 question-answer pairs. For each audio sample, 4 questions are designed to be answered with 'yes' or 'no', while the remaining two questions are designed to be answered in a single word. More details about the data collection process and data splitting process can be found in our following paper. <em><strong>S. Lipping, P. Sudarsanam, K. Drossos, T. Virtanen ‘Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering.’ </strong></em>The paper is available online at 2204.09634.pdf (arxiv.org) If you use the Clotho-AQA dataset, please cite the paper mentioned above. A sample baseline model to use the Clotho-AQA dataset can be found at partha2409/AquaNet (github.com) To use the dataset, • Download and extract <strong>‘audio_files.zip’</strong>. This contains all the 1991 audio samples in the dataset. • Download <strong>‘clotho_aqa_train.csv’, ‘clotho_aqa_val.csv’, </strong>and <strong>‘clotho_aqa_test.csv’</strong>. These files contain the train, validation, and test splits, respectively. They contain the audio file name, questions, answers, and confidence scores provided by the annotators. <strong>License:</strong> The audio files in the archive ‘audio_files.zip’ are under the corresponding licenses (mostly CreativeCommons with attribution) of Freesound [2] platform, mentioned explicitly in the CSV file <strong>’clotho_aqa_metadata.csv’</strong> for each of the audio files. That is, each audio file in the archive is listed in the CSV file with meta-data. The meta-data for each file are: • File name • Keywords • URL for the original audio file • Start and ending samples for the excerpt that is used in the Clotho dataset • Uploader/user in the Freesound platform (manufacturer) • Link to the license of the file. The questions and answers in the files: • clotho_aqa_train.csv • clotho_aqa_val.csv • clotho_aqa_test.csv are under the MIT license, described in the LICENSE file. <strong>References:</strong> [1] K. Drossos, S. Lipping and T. Virtanen, "Clotho: An Audio Captioning Dataset," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736- 740, doi: 10.1109/ICASSP40776.2020.9052990. [2] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245
DOI: 10.5281/zenodo.6477629
title: Simulated 4D WSe2 STEM simulation

description: This file is a simulated 4D STEM data set of defective WSe2 with a slightly aberrated electron probe. It is a demo data for the software pyptychostem
DOI: 10.5281/zenodo.6477628
title: Simulated 4D WSe2 STEM simulation

description: This file is a simulated 4D STEM data set of defective WSe2 with a slightly aberrated electron probe. It is a demo data for the software pyptychostem
DOI: 10.6084/m9.figshare.16992337.v2
title: Sonmez_Extended_Data1

description: TriAnnot gene models and repeat annotations on Sonmez-specific contigs.
DOI: 10.6084/m9.figshare.16992337
title: Sonmez_Extended_Data1

description: TriAnnot gene models and repeat annotations on Sonmez-specific contigs.
DOI: 10.6084/m9.figshare.16992337.v1
title: Sonmez_Data_file4

description: TriAnnot gene models and repeat annotations on Sonmez-specific contigs.
DOI: 10.3886/icpsr23541.v2
title: National Incident-Based Reporting System, 2006: Extract Files
title: Version 2

description: The National Incident-Based Reporting System (NIBRS) is a part of the Uniform Crime Reporting Program (UCR), administered by the Federal Bureau of Investigation (FBI). The extract files version of NIBRS was created to simplify working with NIBRS data. Data management issues with NIBRS are significant, especially when two or more segment levels are being merged. These issues require skills separate from data analysis. NIBRS data as formatted by the FBI are stored in a single file. These data are organized by various segment levels (record types). There are six main segment levels: administrative, offense, property, victim, offender, and arrestee. Each segment level has a different length and layout. There are other segment levels that occur with less frequency than the six main levels. Significant computing resources are necessary to work with the data in its single-file format. In addition, the user must be sophisticated in working with data in complex file types. For these reasons and the desire to facilitate the use of NIBRS data, ICPSR created the extract files. The data are not a representative sample of crime in the United States.
description: Datasets: DS0: Study-Level Files DS1: Incident-Level File DS2: Victim-Level File DS3: Offender-Level File DS4: Arrestee-Level File
description: Law enforcement agencies in the United States participating in the National Incident-Based Reporting System.
description: self-enumerated questionnaire
DOI: 10.3886/icpsr23544.v1
title: Uniform Crime Reporting Program Data [United States]: Hate Crime Data, 2004 [Record-Type Files]
title: Version 1

description: In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.
description: Datasets: DS0: Study-Level Files DS1: Batch Header DS2: Incident Record
description: Hate crime incidents reported by law enforcement agencies in the United States. Smallest Geographic Unit: state, county, and jurisdiction
description: computer-assisted self interview (CASI); self-enumerated questionnaireThe original hierarchical file has been split into two files: a Batch Header file and an Incident Record file. The Batch Header file contains a record for each law enforcement agency whether or not they reported an incident. The Batch Header file variables are merged into the Incident Record File for those agencies that reported an incident.Each victim-type variable has been split into eight separate flag variables to record each victim type. Previously, the variables were eight-character strings that contained the first letter of each victim type in any order.
DOI: 10.6084/m9.figshare.17048549.v1
title: Data for: Towards the development of standardized bioassays for corals: 2 Acute toxicity of the UV filter benzophenone-3 to scleractinian 3 coral larvae

description: File 1: Raw data table consiting acute coral larvae bioassay data after oxybenzone exposure, as well as data for recruits. <br> File 2: Modified R code for estimating LC/EC50
DOI: 10.3886/icpsr23600.v1
title: Uniform Crime Reporting Program Data [United States]: Hate Crime Data, 2003 [Record-Type Files]
title: Version 1

description: In response to a growing concern about hate crimes, the United States Congress enacted the Hate Crime Statistics Act of 1990. The Act requires the attorney general to establish guidelines and collect, as part of the Uniform Crime Reporting (UCR) Program, data "about crimes that manifest evidence of prejudice based on race, religion, sexual orientation, or ethnicity, including where appropriate the crimes of murder and non-negligent manslaughter, forcible rape, aggravated assault, simple assault, intimidation, arson, and destruction, damage or vandalism of property." Hate crime data collection was required by the Act to begin in calendar year 1990 and to continue for four successive years. In September 1994, the Violent Crime Control and Law Enforcement Act amended the Hate Crime Statistics Act to add disabilities, both physical and mental, as factors that could be considered a basis for hate crimes. Although the Act originally mandated data collection for five years, the Church Arson Prevention Act of 1996 amended the collection duration "for each calendar year," making hate crime statistics a permanent addition to the UCR program. As with the other UCR data, law enforcement agencies contribute reports either directly or through their state reporting programs. Information contained in the data includes number of victims and offenders involved in each hate crime incident, type of victims, bias motivation, offense type, and location type.
description: Datasets: DS0: Study-Level Files DS1: Batch Header File DS2: Incident Record File
description: Hate crime incidents reported by law enforcement agencies in the United States. Smallest Geographic Unit: state, county, jurisdiction
description: computer-assisted self interview (CASI); self-enumerated questionnaireThe original hierarchical file has been split into two files: a Batch Header File and an Incident Record File. The Batch Header File contains a record for each law enforcement agency whether or not they reported an incident. The Batch Header File variables are merged into the Incident Record File for those agencies that reported an incident.Each victim-type variable has been split into eight separate flag variables to record each victim type. Previously, the variables were eight-character strings that contained the first letter of each victim type in any order.