cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/ducuxknivnhggq-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2-(4-bromophenyl)-5-(trifluoromethyl)-1,3-benzothiazole)
title: InChI=1S/C14H7BrF3NS/c15-10-4-1-8(2-5-10)13-19-11-7-9(14(16,17)18)3-6-12(11)20-13/h1-7H

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2924 (w), 2853 (w), 1884 (w), 1783 (w), 1744 (w), 1612 (w), 1588 (w), 1568 (w), 1507 (w), 1475 (s), 1419 (w), 1397 (m), 1332 (vs), 1317 (vs), 1258 (vs), 1221 (m), 1211 (m), 1179 (vs), 1145 (vs), 1096 (vs), 1077 (vs), 1068 (vs), 1055 (vs), 1010 (s), 963 (vs), 943 (s), 909 (s), 891 (vs), 826 (vs), 806 (vs), 764 (m), 735 (m), 710 (m), 696 (vs), 667 (s), 623 (w), 606 (m), 582 (w), 554 (m), 537 (m), 480 (vs), 455 (m), 408 (w), 392 (s), 384 (s) cm–1.
DOI: 10.14272/ducuxknivnhggq-uhfffaoysa-n.1
title: 2-(4-bromophenyl)-5-(trifluoromethyl)-1,3-benzothiazole
title: InChI=1S/C14H7BrF3NS/c15-10-4-1-8(2-5-10)13-19-11-7-9(14(16,17)18)3-6-12(11)20-13/h1-7H

description: This dataset contains experimental data from a sample (2-(4-bromophenyl)-5-(trifluoromethyl)-1,3-benzothiazole, InChI=1S/C14H7BrF3NS/c15-10-4-1-8(2-5-10)13-19-11-7-9(14(16,17)18)3-6-12(11)20-13/h1-7H). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 19F nuclear magnetic resonance spectroscopy (19F NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.21227/ph6b-d031
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.21227/v83e-bh07
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.21227/aqrd-rm62
title: Drone Remote Controller RF Signal Dataset

description: This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
DOI: 10.7910/dvn/nic4od
title: India Coal Mines Dataset - December 2020

description: The dataset includes information on each of the 459 operational coal mines in India such as 1) Mine name; 2) State name, 3) District name; 3) Coal production in 2019-2020; 4) Operator name; 5) Type of mine i.e. open cast or underground, among others. We obtained information on coal mines in India, their production, and location by filing applications under the Right to Information (RTI) with leading coal companies such as Coal India Limited its subsidiary companies, The Singareni Collieries Company Limited, and NLC India Limited, and the Coal Controller Organization (India’s coal sector regulator). The RTI Act in India is similar to the Freedom of Information Act in many other countries. The RTI replies were in the form of PDFs with details of the mines. We then individually entered the mine names in excel to create the dataset. PDFs can be made available upon reasonable request and on a case by case basis as the PDFs contain "personal data" of the researcher. (2020-11-26) (2020-11-26)
DOI: 10.34740/kaggle/ds/997793
title: Have You Ever Eaten These Foods?
DOI: 10.6084/m9.figshare.13271819
title: Emergency Settlement Data

description: The document comprises of a catalogue of five hundred Emergency Settlements, open and closed, devided in refugee camps, IDP settlements, and Community Settlements. Each settlement has a fact sheet that includes information, such as name, location, size, and data references.<br>
DOI: 10.34740/kaggle/ds/486699
title: Have You Ever?
DOI: 10.6084/m9.figshare.13239515.v2
title: COVID-19 Wikipedia data

description: COVID-19 related Wikipedia items lists, networks, and views in 11 languages (English, Chinese, Korean, Italian, German, French, Spanish, Portuguese, Russian, Japanese, and Arabic)
DOI: 10.17632/m2ccbv7rcr.1
title: Data for: ‘On the Road Again’: A 118 Country Panel Analysis of Gasoline and Diesel Demand

description: Road fuel (motor gasoline & diesel) real price index (base year 2005). Prices weighted by motor gasoline and diesel in final road transport consumption. Unbalanced data for 118 countries, 1978-2016. This index was used in Appendix B of Liddle, B. and H. Huntington (2020). ‘On the Road Again’: A 118 Country Panel Analysis of Gasoline and Diesel Demand. Transportation Research A: Policy and Practice. Please cite accordingly.
DOI: 10.17632/3nmbz2jyd2.1
title: Data for: Leap-Frogging and Energy Consumption: A Country Panel Analysis

description: This is the expanded dataset used in the addendum to Liddle & Huntington 2020. Please cite this data as: Liddle, B. & Huntington, H. 2020. There’s Technology Improvement, but is there Economy-wide Energy Leap-Frogging? A Country Panel Analysis. Accepted at World Development.
DOI: 10.5519/qd.1fqbvpmd
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.4292736
title: Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study

description: This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F<sub>2</sub> population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F<sub>6</sub>) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10,0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
DOI: 10.5281/zenodo.4292735
title: Quantile regression in genomic selection for oligogenic traits in autogamous plants: a simulation study

description: This study assessed the efficiency of Genomic selection (GS) or genome‐wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F<sub>2</sub> population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F<sub>6</sub>) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10,0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.
DOI: 10.5281/zenodo.4292662
title: Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.

description: The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (&gt;=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.
DOI: 10.6084/m9.figshare.13293419
title: Emergency Settlements - Raw Data

description: This document comprises of an Excel spreadsheet with data on five humdred Emergency Settlements (refugee camps, IDP settlements, Community settlements), open and closed, including name location, size (physical and population), demographics, and background.<br>
DOI: 10.3886/e123601v1-70840
title: Risk and protective factors for psychological distress during Covid-19 in Israel

description: This folder contains the data and all necessary files to replicate all results from the article “ <br>"Risk and protective factors for psychological distress during Covid-19 in Israel”
description: Response Rates: 655 participants took part in the study, after excluding 200 participants who did not complete the questionnaires
description: Presence of Common Scales: PHQ-9 - 0 (not at all) to 3 (nearly every day).<br>Gratitude- 1 (do not agree at all) to 7 (very much agree) <br>life orientation test (LOT)- 0 (do not agree at all) 4 (very much agree)<br>COPE - 0 (not at all) to 5 (very much).<br>MSPSS - 0 (not at all) to 7 (very much)<br>GAD-7- s 0 (not at all to) 3 (almost every day).<br>
description: web-based survey;
description: Persons aged 18 and over, living in Israel with fluent Hebrow.<br>. Smallest Geographic Unit: Israel
description: Participants were recruited by snowball sampling, selected to represent the adult population in isreal.
DOI: 10.3886/e124681v6-70860
title: Supplementary materials for report “Shining a Brighter Light: Comprehensive Evidence on Adoption and Diffusion of CGIAR-Related Innovations in Ethiopia”

description: The report presents an unprecedented stocktaking of all CGIAR-related innovations in a given country as well as new estimates of adoption of those innovations from a nationally representative dataset generated through a partnership among the Ethiopian Central Statistics Agency (CSA), the World Bank Living Standards Measurement Study (LSMS) team, and the CGIAR Standing Panel on Impact Assessment (SPIA). Ethiopia was chosen for this exercise because it is a hotspot of CGIAR research, with almost all the CGIAR centers represented in Addis Ababa. <br> <br>The report documents the reach of CGIAR-related agricultural innovations in a comprehensive manner across the core domains of CGIAR research activity: animal agriculture; crop germplasm improvement; natural resource management; and policy research. In order to identify the right innovations to collect data on, SPIA conducted more than 90 interviews with CGIAR research leaders, scientists, government officials, and colleagues from the Ethiopian Institute for Agricultural Research (EIAR), all the while compiling documented evidence to support claims made by these key informants. The output of that work is a stocktaking of 52 agricultural innovations and 26 claims of policy influence.<br><br>Quantitative evidence on the adoption of 18 of these innovations was obtained through the incorporation of measurements of the reach of these innovations in the Ethiopian Socioeconomic Survey (ESS), a regionally and nationally representative panel survey of households. We report some data from the third wave (ESS3, carried out in 2015/16), but our major focus is on ESS4 (2018/19).
DOI: 10.3886/e127461
title: Evaluating the impact of COVID-19 on rare disease support groups.

description: The impact of the current COVID-19 pandemic has been felt worldwide. Many vulnerable populations rely heavily on peer support provided by individual or collaborative groups. This project aimed to evaluate the impact of COVID-19 on groups supporting patients with a rare disease(s). Anecdotally the current pandemic significantly changed the way in which these groups operate and the services they can provide. A targeted survey was conducted online with rare disease individual or collaborative groups. The results of the survey highlight the challenges individual and collaborative groups are facing during this pandemic and help to identify what support should be put in place to enable them to sustain their much-valued service through these trialing times. Groups have experienced an increase in calls to their helplines as well as followers to their websites and social media feeds. Groups are no longer meeting in person and so online meetings, webinars and zoom chats have become a regular occurrence. Fundraising was highlighted as an area of concern for such groups. It is hoped that this data might be used to highlight the support individual and collaborative groups require and raise awareness of the value they bring to many.
description: Response Rates: Six rare disease individual or collaborative groups completed the online survey.
description: web-based survey;
description: Those over 18 years old who are part of a collaborative group working in the area of rare disease. .
description: The survey was uploaded onto SmartSurvey (www.smartsurvey.co.uk), and shared on the associated social media pages and websites of Northern Ireland Rare Disease Partnership (NIRDP www.nirdp.org.uk), Health Research Charities Ireland (HRCI, http://hrci.ie/) and Rare Disease Ireland (RDI, http://rdi.ie/) as well as on the Queen’s University Belfast (QUB) rare disease website (https://www.qub.ac.uk/sites/RareDisease/). Rare disease collaborative groups who have been involved in research conducted by the Rare Disease Team at QUB previously, and provided consent, were emailed directly and invited to participate in this study.
DOI: 10.3886/e127461v1
title: Evaluating the impact of COVID-19 on rare disease support groups.

description: The impact of the current COVID-19 pandemic has been felt worldwide. Many vulnerable populations rely heavily on peer support provided by individual or collaborative groups. This project aimed to evaluate the impact of COVID-19 on groups supporting patients with a rare disease(s). Anecdotally the current pandemic significantly changed the way in which these groups operate and the services they can provide. A targeted survey was conducted online with rare disease individual or collaborative groups. The results of the survey highlight the challenges individual and collaborative groups are facing during this pandemic and help to identify what support should be put in place to enable them to sustain their much-valued service through these trialing times. Groups have experienced an increase in calls to their helplines as well as followers to their websites and social media feeds. Groups are no longer meeting in person and so online meetings, webinars and zoom chats have become a regular occurrence. Fundraising was highlighted as an area of concern for such groups. It is hoped that this data might be used to highlight the support individual and collaborative groups require and raise awareness of the value they bring to many.
description: Response Rates: Six rare disease individual or collaborative groups completed the online survey.
description: web-based survey;
description: Those over 18 years old who are part of a collaborative group working in the area of rare disease. .
description: The survey was uploaded onto SmartSurvey (www.smartsurvey.co.uk), and shared on the associated social media pages and websites of Northern Ireland Rare Disease Partnership (NIRDP www.nirdp.org.uk), Health Research Charities Ireland (HRCI, http://hrci.ie/) and Rare Disease Ireland (RDI, http://rdi.ie/) as well as on the Queen’s University Belfast (QUB) rare disease website (https://www.qub.ac.uk/sites/RareDisease/). Rare disease collaborative groups who have been involved in research conducted by the Rare Disease Team at QUB previously, and provided consent, were emailed directly and invited to participate in this study.
DOI: 10.3886/e127482
title: Product Datasets from the MWPD2020 Challenge at the ISWC2020 Conference (Task 1)

description: The goal of Task 1 of the Mining the Web of Product Data Challenge (MWPD2020) was to compare the performance of methods for identifying offers for the same product from different e-shops. The datasets that are provided to the participants of the competition contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) from the product category computers. The data is available in the form of training, validation and test set for machine learning experiments. The Training set consists of ~70K product pairs which were automatically labeled using the weak supervision of marked up product identifiers on the web. The validation set contains 1.100 manually labeled pairs. The test set which was used for the evaluation of participating systems consists of 1500 manually labeled pairs. The test set is intentionally harder than the other sets due to containing more very hard matching cases as well as a variety of matching challenges for a subset of the pairs, e.g. products not having training data in the training set or products which have had typos introduced. These can be used to measure the performance of methods on these kinds of matching challenges. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites, marking up their offers with schema.org vocabulary. For more information and download links for the corpus itself, please follow the links below.
DOI: 10.3886/e127482v1
title: Product Datasets from the MWPD2020 Challenge at the ISWC2020 Conference (Task 1)

description: The goal of Task 1 of the Mining the Web of Product Data Challenge (MWPD2020) was to compare the performance of methods for identifying offers for the same product from different e-shops. The datasets that are provided to the participants of the competition contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) from the product category computers. The data is available in the form of training, validation and test set for machine learning experiments. The Training set consists of ~70K product pairs which were automatically labeled using the weak supervision of marked up product identifiers on the web. The validation set contains 1.100 manually labeled pairs. The test set which was used for the evaluation of participating systems consists of 1500 manually labeled pairs. The test set is intentionally harder than the other sets due to containing more very hard matching cases as well as a variety of matching challenges for a subset of the pairs, e.g. products not having training data in the training set or products which have had typos introduced. These can be used to measure the performance of methods on these kinds of matching challenges. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites, marking up their offers with schema.org vocabulary. For more information and download links for the corpus itself, please follow the links below.
DOI: 10.3886/e127481
title: Web Data Commons Training and Test Sets for Large-Scale Product Matching - Version 2.0

description: Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.