cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rcdvm6y7sm.2
title: Real Instances for Aircraft Landing Planning

description: 149 instances of flight data (category, sector, cruise speed, remaining distance, release and due dates), presented and used in the paper "Aircraft Landing Planning under Uncertain Conditions" by M.-S. Vié, N. Zufferey and R. Leus, and provided by Eurocontrol.
DOI: 10.17632/r4vvpbtgsw.1
title: LINF_100005600

description: ribosomal protein L35a; Leishmania infantum
DOI: 10.17632/wwzcysbpdy.1
title: LINF_100005800

description: dehydrogenase-like protein; Leishmania infantum
DOI: 10.17632/87wg9r7ss5.1
title: LINF_100006200

description: hypothetical protein - conserved; Leishmania infantum
DOI: 10.17632/3p2ctv5dgk.1
title: LINF_100006500

description: hypothetical protein - conserved; Leishmania infantum
DOI: 10.17632/wtbb5py458.4
title: Bayanno

description: This dataset is published for Bengali continuous speech recognition. The dataset has three files "Script Files" contain the Bengali text for continuous speech and "Speech Files" contain the recorded audio speech. "Speakers_information" CSV file has stored the speaker's information.
DOI: 10.17632/6732npdk5y.1
title: HSAP9 and CUL2 in prostate tissue and adenocarcinoma

description: In the present study, we evaluated whether HSPA9 and CUL2 immunoexpression is greater in carcinoma than in non-neoplastic prostate tissue and whether Gleason patterns 3 and 4 increased the risk of Gleason score 6 and 7 (3+4) adenocarcinoma, biochemical persistence (BP), biochemical recurrence (BR), biochemical failure after adjuvant therapy (FB-ADJ), biochemical failure after prostatectomy (FB-PRS), biochemical failure after salvage radiotherapy (FB-RT), biochemical failure after salvage hormone and radiotherapy (FB-HTRT), biochemical failure after salvage therapy (BF-SAL) and systemic disease (SD).
DOI: 10.17632/3jswtm5kzm.1
title: Complete data and Results of Bi-LSTM for ZWD

description: These are the full results from "Predicting Sub-daily Zenith Wet Delay using Bidirectional Long Short-Term Memory" for the selected 505 stations. They are divided into: 1- Data for the 505 stations containing the date and the ZWD for each stations during 2019 with six-hour time resolution. 2- Excel file for each stations contains all the statistics 3- Regression figure for each station 4- MSE, RMSE and STD figures for the tested data for each stations. 5- MSE, RMSE and STD figures for the trained data for each stations..
DOI: 10.17632/xmp29vsb5d.1
title: Bullshit and credibility

description: The Effect of Source Credibility on Bullshit Receptivity: Data for Pilot, Study 1 and Study 2
DOI: 10.17632/b6xh96rxtc.3
title: El status ontológico de la esencia

description: Was ist das Wesen? ¿A qué se reduce la esencia o eidos?
DOI: 10.17632/xpmrc6c85n.1
title: LÓGICA

description: ...
DOI: 10.17632/kw2jypm3cv.1
title: Research Dataset of The Rural Entrepreneurship (1976-2020)

description: The rural entrepreneurship research & publication dataset, which was indexed by Scopus from 1976 to 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/w9hz7k8j3j.1
title: Radial Distraction Plating for Distal Radius Fracture in Ulnar Positive Wrist

description: Radial Distraction Plating for Distal Radius Fracture in Ulnar Positive Wrist
DOI: 10.17632/bd63njzyjf.1
title: Supplementary table A

description: ROBIN A criteria
DOI: 10.17632/gh4xfyzwfg.1
title: Suppementary table B

description: List of studies
DOI: 10.17632/tvksvtdbs7.1
title: Final raw data Docking and Molecular dynamics

description: These data correspond to the results of molecular docking tests between the E envelope protein of the dengue virus and the GLUN2A subunit of the NMDA receptor and also contain the results of molecular dynamics between the two proteins evaluated.
DOI: 10.7488/ds/7461
title: Focus group transcripts from employees and line managers on sedentary behaviour when working at home

description: This data relates to research conducted as part of a MRC PHIND funded study (Medical Research Council Public Health Intervention Development Award - MR/W003511/1) to develop health-enhancing solutions to reduce sedentary behaviour when working at home. The purpose of the study was to collaborate with employees and line managers with experience of working at home or leading a team of staff working at home. To do this we conducted a series of six focus groups focused on [add]. Three focus groups were conducted with employee participants (18-40 year olds, working full time and for more than 60% of their week at home, in a desk-based role (e.g secretarial / administrative / sales) and three focus groups with line manager participants (with team members in the same population as the employee group). At the time of the focus groups (Jan to March 2022) there were still some lockdown restrictions in place and the majority of people in Scotland who could work from home were still working at home for most of their normal working week. Aligning with this, we conducted the focus group online using MS Teams. Each focus group lasted for approx. one (1) hour, was recorded and later transcribed verbatim by a University of Edinburgh approved transcription service. Data analysis was guided by the COM-B model for understanding capability, opportunity, and motivation to change behaviour. Findings from the study were used to inform the development of an evidence-informed toolkit to support those working at home to move more across the course of the working day. The dataset includes the six transcripts - one from each focus group. Each transcript is labelled to show which focus group it relates to. All participant names have been replaced by a pseudonym. Where geographical locations, organisation names, job titles demonstrated potential to identify a participant these were replaced with generic codes. The dataset relates to the upcoming publication Morton, Fitzsimons, Sivaramakrishnan, Jepson, Niven (in preparation), "Are we working (too) comfortably?: Developing intervention strategies to support employees aged 18-40 to reduce their sedentary behaviour when working at home".
DOI: 10.25377/sussex.7970513
title: Dataset for Everyday Childhoods research project. Gabriel - Favourite Things Interview (embargo)

description: This data was collected as part of Gabriel's favourite things interview, carried out on the 05/12/2013 with Sue Sharpe. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.25377/sussex.7970513.v1
title: Dataset for Everyday Childhoods research project. Gabriel - Favourite Things Interview (embargo)

description: This data was collected as part of Gabriel's favourite things interview, carried out on the 05/12/2013 with Sue Sharpe. This item belongs to the Everyday Childhoods project dataset. For more information, click on the project or collection link.
DOI: 10.3886/icpsr38140.v1
title: Survey of Consumer Attitudes and Behavior, January 2019
title: Version 1

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning. Other topics in this series typically include economic attitudes, home price expectations, savings and financial investments, political affiliation, and opinion on the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (HOUSEHOLD HEAD WEIGHT), <b>WT_HH</b> (HOUSEHOLD HEAD WEIGHT), and <b>WT_AD</b> (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Web site.
description: National sample of telephone numbers from cell (RDD) sampling frame
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Web site.
DOI: 10.3886/icpsr38140
title: Survey of Consumer Attitudes and Behavior, January 2019
title: Archival Version

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning. Other topics in this series typically include economic attitudes, home price expectations, savings and financial investments, political affiliation, and opinion on the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (HOUSEHOLD HEAD WEIGHT), <b>WT_HH</b> (HOUSEHOLD HEAD WEIGHT), and <b>WT_AD</b> (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Web site.
description: National sample of telephone numbers from cell (RDD) sampling frame
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Web site.
DOI: 10.5281/zenodo.4012193
title: DeepScoresV2

description: The DeepScoresV2 Dataset for Music Object Detection contains digitally rendered images of written sheet music, together with the corresponding ground truth to fit various types of machine learning models. A total of 151 Million different instances of music symbols, belonging to 135 different classes are annotated. The total Dataset contains 255,385 Images. <strong>For most researches, the dense version, containing 1714 of the most diverse and interesting images, should suffice.</strong> The dataset contains ground in the form of: Non-oriented bounding boxes Oriented bounding boxes Semantic segmentation Instance segmentation The accompaning paper <em>The DeepScoresV2 Dataset and Benchmark for Music Object Detection </em>published at ICPR2020 can be found here: https://digitalcollection.zhaw.ch/handle/11475/20647 A toolkit for convenient loading and inspection of the data can be found here: https://github.com/yvan674/obb_anns Code to train baseline models can be found here: https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old
description: The authors are grateful for the support through Innosuisse grant No. 34301.1 IP-ICT "RealScore", EuropeanResearch Council Advanced Grant 742870, and the continued fruitful collaboration with ScorePad AG.
DOI: 10.5281/zenodo.4012192
title: DeepScoresV2

description: The DeepScoresV2 Dataset for Music Object Detection contains digitally rendered images of written sheet music, together with the corresponding ground truth to fit various types of machine learning models. A total of 151 Million different instances of music symbols, belonging to 135 different classes are annotated. The total Dataset contains 255,385 Images. <strong>For most researches, the dense version, containing 1714 of the most diverse and interesting images, should suffice.</strong> The dataset contains ground in the form of: Non-oriented bounding boxes Oriented bounding boxes Semantic segmentation Instance segmentation The accompaning paper <em>The DeepScoresV2 Dataset and Benchmark for Music Object Detection </em>published at ICPR2020 can be found here: https://digitalcollection.zhaw.ch/handle/11475/20647 A toolkit for convenient loading and inspection of the data can be found here: https://github.com/yvan674/obb_anns Code to train baseline models can be found here: https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old
description: The authors are grateful for the support through Innosuisse grant No. 34301.1 IP-ICT "RealScore", EuropeanResearch Council Advanced Grant 742870, and the continued fruitful collaboration with ScorePad AG.
DOI: 10.18112/openneuro.ds004585.v1.0.0
title: Brain surface fingerprints
DOI: 10.25345/c5794145x
title: MassIVE MSV000092122 - Vitamin C (ascorbate) modulates the levels of several proteins in the heart of mice
title: In the present study, we used the Gulo-/- female and male mice model which depends entirely on ascorbate derived from the diet. Importantly, the levels of ascorbate found in the serum of Gulo-/- mice reflect the amounts of ascorbate provided in drinking water. We tested different concentrations of ascorbate ranging from 0 to 0.4% (w/v) ascorbate, added into drinking water, until the age of four months. We performed label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) global quantitative proteomic profiling to identify and quantify proteins in whole heart extracts from our different ascorbate treated cohorts of Gulo-/- females and males.
DOI: 10.5281/zenodo.8014694
title: IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.

description: IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.
DOI: 10.5281/zenodo.8014693
title: IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.

description: IDs of Tweets from 1/1/2019-31/7/2022 that contain the hashtag #burnout.
DOI: 10.5281/zenodo.8014709
title: EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis

description: In recent years there has been a shift from heuristics-based malware detection towards machine learning, which proves to be more robust in the current heavily adversarial threat landscape. While we acknowledge machine learning to be better equipped to mine for patterns in the increasingly high amounts of similar-looking files, we also note a remarkable scarcity of the data available for similarity-targeted research. Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass. We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER — one of the largest malware classification data sets. We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space. Our contribution is threefold: (1) we publish EMBERSim, an augmented version of EMBER, that includes similarity-informed tags; (2) we enrich EMBERSim with automatically determined malware class tags using the open-source tool AVClass on VirusTotal data and (3) we describe and share the implementation for our class scoring technique and leaf similarity method.
description: {"references": ["Anderson, H. S., & Roth, P. (2018). Ember: an open dataset for training static pe malware machine learning models. arXiv preprint arXiv:1804.04637.", "Sebasti\u00e1n, S., & Caballero, J. (2020, December). Avclass2: Massive malware tag extraction from av labels. In Annual Computer Security Applications Conference (pp. 42-53).", "Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785-794."]}
DOI: 10.5281/zenodo.8014708
title: EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis

description: In recent years there has been a shift from heuristics-based malware detection towards machine learning, which proves to be more robust in the current heavily adversarial threat landscape. While we acknowledge machine learning to be better equipped to mine for patterns in the increasingly high amounts of similar-looking files, we also note a remarkable scarcity of the data available for similarity-targeted research. Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass. We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER — one of the largest malware classification data sets. We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space. Our contribution is threefold: (1) we publish EMBERSim, an augmented version of EMBER, that includes similarity-informed tags; (2) we enrich EMBERSim with automatically determined malware class tags using the open-source tool AVClass on VirusTotal data and (3) we describe and share the implementation for our class scoring technique and leaf similarity method.
description: {"references": ["Anderson, H. S., & Roth, P. (2018). Ember: an open dataset for training static pe malware machine learning models. arXiv preprint arXiv:1804.04637.", "Sebasti\u00e1n, S., & Caballero, J. (2020, December). Avclass2: Massive malware tag extraction from av labels. In Annual Computer Security Applications Conference (pp. 42-53).", "Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785-794."]}
DOI: 10.5281/zenodo.7495487
title: SapphireEnergyIABR: Data from the Sapphire Energy Integrated Algal BioRefinery

description: This dataset includes previously unpublished information collected during the operation of the Sapphire Energy Integrated Algal BioRefinery. <strong>Terms of Use:</strong> These data are provided as is, without any warranties of any kind. A bibliographic citation should be included in the References section of publications and other media to acknowledge the authors of this dataset. Proper citations include the authors, title, publisher, and Digital Object Identifier (DOI) and will allow the products to be discovered and re-used by others.
DOI: 10.5281/zenodo.8014712
title: SapphireEnergyIABR: Data from the Sapphire Energy Integrated Algal BioRefinery

description: This dataset includes previously unpublished information collected during the operation of the Sapphire Energy Integrated Algal BioRefinery. <strong>Terms of Use:</strong> These data are provided as is, without any warranties of any kind. A bibliographic citation should be included in the References section of publications and other media to acknowledge the authors of this dataset. Proper citations include the authors, title, publisher, and Digital Object Identifier (DOI) and will allow the products to be discovered and re-used by others.
DOI: 10.5281/zenodo.7495486
title: SapphireEnergyIABR: Data from the Sapphire Energy Integrated Algal BioRefinery

description: This dataset includes previously unpublished information collected during the operation of the Sapphire Energy Integrated Algal BioRefinery. <strong>Terms of Use:</strong> These data are provided as is, without any warranties of any kind. A bibliographic citation should be included in the References section of publications and other media to acknowledge the authors of this dataset. Proper citations include the authors, title, publisher, and Digital Object Identifier (DOI) and will allow the products to be discovered and re-used by others.
DOI: 10.12751/g-node.mk9n4h
title: Association of verbal and non-verbal theory of mind abilities with non-coding variants of OXTR in youth with autism spectrum disorder and typically developing individuals: a case control study

description: In the present study, we examined the association between rs2268498 and rs53576 and social functioning in individuals with ASD and TD group. We found preliminary evidence that rs2268498 and rs53576 are associated with ToM related abilities in healthy individuals as well as in autistic individuals. Accordingly, rs2268498 may play an important role in predicting ToM capabilities. It will be necessary to conduct further research to address the association of genetic variants with a deficit in ToM in individuals with ASD.
description: IsSupplementTo: Rana Ghamari, Mohammad Tahmaseb, Atiye Sarabi-Jamab, Seyed-Alireza Etesami, Azar Mohammadzadeh, Fatemeh Alizadeh, Mehdi Tehrani-Doost: Association of verbal and non-verbal theory of mind abilities with non-coding variants of OXTR in youth with autism spectrum disorder and typically developing individuals: a case control study. BMC Psychiatry Journal, submitted.