cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc272lkh
title: CCDC 2058076: Experimental Crystal Structure Determination

description: Related Article: Ming-Zhe Shao, Xiao-Yu Liu, Fang-Qing Li, Zili Chen|2021|Tetrahedron Lett.|71|153031|doi:10.1016/j.tetlet.2021.153031
DOI: 10.5517/ccdc.csd.cc2744wf
title: CCDC 2059574: Experimental Crystal Structure Determination

description: Related Article: Arumugan Kalaiselvan, Aswini Spergen, Isukapalli Sai Vamsi Krishna, Vennapusa Sivaranjana Reddy, Sabapathi Gokulnath|2021|Chem.Commun.|57|4420|doi:10.1039/D1CC00868D
DOI: 10.6084/m9.figshare.3761562.v1859
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.4737836
title: Apache Software Foundation Incubator Project Sustainability Dataset

description: The data set contains ASF incubator data and the associated scripts using Beautifulsoup, pg8000 packages.
DOI: 10.6084/m9.figshare.14478240
title: ResistanceGAFunction.R

description: Customized R function for setting up and running landscape resistance surface optimizations through ResistanceGA
DOI: 10.6084/m9.figshare.14478162
title: Calculate_PCbased_Genetic_Distance.R

description: R script to calculate individual-based genetic distance using principal components following:Shirk, A. J., E. L. Landguth, and S. A. Cushman. 2017. A comparison of individual-based genetic distance metrics for landscape genetics. Molecular Ecology Resources:1-10.<br>
DOI: 10.6084/m9.figshare.14478237
title: R_script_for_running_ResistanceGA.R

description: R script for running ResistanceGA optimizations
DOI: 10.6084/m9.figshare.14478240.v1
title: ResistanceGAFunction.R

description: Customized R function for setting up and running landscape resistance surface optimizations through ResistanceGA
DOI: 10.6084/m9.figshare.13646780
title: Resource selection surface ASCIIs

description: Resource selection surfaces (both the predicted surfaces and the exponentiated surfaces) used with ResistanceGA to optimize landscape resistance surfaces for eastern indigo snakes in Bauder et al. Please refer to our paper for the details of the Level II, Level III, and SRSF surfaces.
DOI: 10.6084/m9.figshare.14478147
title: Indigo_snake_microsatellite_genotypes.csv

description: Sample identification numbers, UTM coordinates (x/y in NAD83 UTM Zone 17N), and genotypes at 15 microsatellite loci for 102 eastern indigo snakes. Note that loci Dry14 and Dry68 were not included in analyses because of high null allele frequencies.
DOI: 10.6084/m9.figshare.13646768
title: Smoothed landscape covariates

description: This compressed 7z folder has all of the smoothed landscape covariate ASCIIs that were used in the analyses of Bauder et al. ResistanceGA uses these ASCII files to optimize the landscape resistance surfaces.
DOI: 10.6084/m9.figshare.13646768.v1
title: Smoothed landscape covariates

description: This compressed 7z folder has all of the smoothed landscape covariate ASCIIs that were used in the analyses of Bauder et al. ResistanceGA uses these ASCII files to optimize the landscape resistance surfaces.
DOI: 10.6084/m9.figshare.13646765
title: Indigo snake genetic sample locations

description: Location (x/y UTM coordinates NAD83 17N) of indigo snake genetic samples used in Bauder et al.
DOI: 10.6084/m9.figshare.14478147.v1
title: Indigo_snake_microsatellite_genotypes.csv

description: Sample identification numbers, UTM coordinates (x/y in NAD83 UTM Zone 17N), and genotypes at 15 microsatellite loci for 102 eastern indigo snakes. Note that loci Dry14 and Dry68 were not included in analyses because of high null allele frequencies.
DOI: 10.6084/m9.figshare.13646762
title: Indigo snake genetic matrix

description: The genetic distance matrix used in the landscape genetics analysis of Bauder et al.
DOI: 10.6084/m9.figshare.13646765.v1
title: Indigo snake genetic sample locations

description: Location (x/y UTM coordinates NAD83 17N) of indigo snake genetic samples used in Bauder et al.
DOI: 10.6084/m9.figshare.13646762.v1
title: Indigo snake genetic matrix

description: The genetic distance matrix used in the landscape genetics analysis of Bauder et al.
DOI: 10.6084/m9.figshare.14138066
title: Western Blot Overlays AGE/RAGE Diabetes-mediated Calcification

description: These are the raw western blots with lanes, antibodies, and cell type labeled.
DOI: 10.6084/m9.figshare.14138066.v1
title: Western Blot Overlays AGE/RAGE Diabetes-mediated Calcification

description: These are the raw western blots with lanes, antibodies, and cell type labeled.
DOI: 10.6084/m9.figshare.14138597
title: Excel Files Raw Data AGE/RAGE Calcification

description: These are the raw data files for western blots, calcification quantification, and animal body measurements.
DOI: 10.6084/m9.figshare.14138597.v1
title: Excel Files Raw Data AGE/RAGE Calcification

description: These are the raw data files for western blots, calcification quantification, and animal body measurements.
DOI: 10.34740/kaggle/dsv/2193836
title: Votos em Bolsonaro e excesso mortes BRA COVID data
DOI: 10.6084/m9.figshare.14538333
title: Conditioned Media Raw Western Data

description: These are the raw western blot values taken from densitometric analysis.
DOI: 10.6084/m9.figshare.14536023
title: Conditioned Media Western Blot Overlays

description: These are the western blot overlays for all of the blots analyzed for 'Calcified Conditioned Media Elicits Differential Signaling Responses of Primary Aortic Cells'.<b><i></i></b>
DOI: 10.25810/98zg-gz80
title: Passive and Active Spectrum Sharing (PASS)

description: 

        PASS is an NSF-funded project (ECCS-2030233)[1]. This page provides access to the noise-floor survey SigMF[2][3] data sets generated as part of the project.

         

        The University of Colorado Boulder (CU) Passive and Active Spectrum Sharing (PASS) project directly addresses the related problems of protecting passive users while enabling secure, dynamic spectrum sharing between passive and active systems. The PASS project is an interdisciplinary collaboration with deep expertise in spectrum science, spectrum sharing, wireless systems, and system security. 

         

        Radio frequency (RF) spectrum has become a scarce resource. The Passive and Active Spectrum Sharing (PASS) project will systematically survey and characterize RF noise, and explore and evaluate alternatives for enabling spectrum sharing between passive and active systems. The PASS research will be a significant step forward in understanding and mitigating RF noise, and will enable more dynamic and efficient sharing of spectrum.

         

        Our purpose behind a data standard is to enable data sharing and collaboration. Along with developing the standard and gathering data on RF noise in various environments, the PASS project will develop a prototype database for storing and retrieving RF data. Several sites exist which provide SigMF-based RF data for download. For example, researchers at the UC Berkeley SETI Institute have made available SigMF files from observations made with the Allen Telescope Array, an array of 42 dish antennas at the Hat Creek Observatory in California [4]. Additional datasets are available from Breakthrough Listen observations using the Green Bank Telescope. Deepsig.io has datasets in multiple formats, including hdf5, and Python ‘pickle’ files [5].

         

        [1] PASS NSF Award Page, https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030233&HistoricalAwards=false 

        [2] GNURadio/SigMF, Signal Metadata Format (SigMF), https://github.com/gnuradio/sigmf

        [3] GNURadio: the free and open software radio ecosystem, https://www.gnuradio.org/ 

        [4] SETI Institute, http://seti.berkeley.edu:8000/open-data/

        [5] DeepSig, Inc., RF Datasets for Machine Learning, https://www.deepsig.io/datasets
DOI: 10.15139/s3/3ycpwa
title: Establishing Mental Health

description: The Establishing Mental Health study was conducted from 2013-12-16 to 2015-07-10 in Tanzania. Data were collected on participants' HIV status and various mental health measures, including the PHQ9, SDQ, and UCLA PTSD assessment. The study population comprised adolescents and youth attending the Kilimanjaro Christian Medical Centre and later the Mawenzi Regional Referral Hospital Care and Treatment Clinic adolescent HIV clinics. These clinics are referred to as "Teen Club" or "Klubu ya Vijana". Participants were between 11 and 24 years of age and disclosed their HIV status. Not all youth were receiving antiretroviral therapy at the time of the study as the National Guidelines at the time did not offer ART to everyone; only those with CD4 350 or WHO Stage 3 or 4 disease. All adolescents and youth attending the clinic who were able to provide consent and/or assent (if 18 years old with parental consent) were eligible to enroll. Enrollment took place by announcing the study in clinic and interested participants signing up to state interest and later participating in the consent process. There was no formal power calculation. All data within the AMANI collection is restricted to protect confidentiality. You may request access to a dataset by contacting the data depositor at the email address provided under the Terms of Service.
DOI: 10.6084/m9.figshare.14478258
title: Categorical_Land_Cover_Surfaces.7z

description: Categorical land cover surfaces used to optimize landscape resistance surfaces for eastern indigo snakes.
DOI: 10.5281/zenodo.4728986
title: Research Artifact: Test-Case Prioritization for Configuration Testing

description: This is the artifact of the research paper, "Test-Case Prioritization for Configuration Testing", at the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), 2021. To use the artifact, please first download and decompress ctest_prio_art-main.zip. Please refer to the README.md here or inside ctest_prio_art/ for using our data, reproducing our experiment results, and running new experiments.
DOI: 10.6084/m9.figshare.14531631.v1
title: BetterHomesTO, research instruments, codes, and final report

description: <b>Includes: </b>BetterHomesTO Codes & Themes developed through reflexive thematic analysis<b><br></b>Conversational surveysDocument ReviewOnline survey resultsReport with findings<br> <b>Methods:</b> document review, conversational surveys featuring closed and open questions (transcribed), online survey, participant observation of meetings<br> <b>Reflexive Thematic Analytical Approach:</b> an initial set of primary, <i>semantic</i> codes were drawn from the literature review and aligned with the evaluation framework principles and criteria. Through co-production with study participants, a set of secondary codes emerged. In this phase, I hard-copy coded the entire dataset, then collated the codes and relevant data extracts in preparation for later stages of analysis (See Braun et al, 2019).<br> <b>Ethics:</b> The research detailed here was conducted with human subjects, approval for which was granted by the University of Toronto’s Research and Ethics Board (REB) per protocol no. 00037210
DOI: 10.6084/m9.figshare.14531631
title: BetterHomesTO, research instruments, codes, and final report

description: <b>Includes: </b>BetterHomesTO Codes & Themes developed through reflexive thematic analysis<b><br></b>Conversational surveysDocument ReviewOnline survey resultsReport with findings<br> <b>Methods:</b> document review, conversational surveys featuring closed and open questions (transcribed), online survey, participant observation of meetings<br> <b>Reflexive Thematic Analytical Approach:</b> an initial set of primary, <i>semantic</i> codes were drawn from the literature review and aligned with the evaluation framework principles and criteria. Through co-production with study participants, a set of secondary codes emerged. In this phase, I hard-copy coded the entire dataset, then collated the codes and relevant data extracts in preparation for later stages of analysis (See Braun et al, 2019).<br> <b>Ethics:</b> The research detailed here was conducted with human subjects, approval for which was granted by the University of Toronto’s Research and Ethics Board (REB) per protocol no. 00037210
DOI: 10.5281/zenodo.4737322
title: wiki-727K + wiki-50 (Text Segmentation as a Supervised Learning Task)

description: Dataset accompanying Text Segmentation as a Supervised Learning Task (Koshorek et al. 2018) For this work we have created a new dataset, which we name wiki-727K. It is a collection of 727,746 English Wikipedia documents, and their hierarchical segmentation, as it appears in their table of contents. We randomly partitioned the documents into a train (80%), development (10%), and test (10%) set. Different text segmentation use-cases require different levels of granularity. For example, for segmenting text by overarching topic it makes sense to train a model that predicts only top-level segments, which are typically vary in topic – for example, “<em>History</em>”, “<em>Geography</em>”, and “<em>Demographics</em>”. For segmenting a radio broadcast into separate news stories, which requires finer granularity, it makes sense to train a model to predict sub-segments. Our dataset provides the entire segmentation information, and an application may choose the appropriate level of granularity. To generate the data, we performed the following preprocessing steps for each Wikipedia document: Removed all photos, tables, Wikipedia template elements, and other non-text elements. Removed single-sentence segments, documents with less than three segments, and documents where most segments were filtered. Divided each segment into sentences using the PUNKT tokenizer of the NLTK library. This is necessary for the use of our dataset as a benchmark, as with-out a well-defined sentence segmentation, it is impossible to evaluate different models. Because our test set is large, it is difficult to evaluate some of the existing methods, which are computationally demanding. Thus, we introduce wiki-50, a set of 50 randomly sampled test documents from wiki-727K. We use wiki-50 to evaluate systems that are too slow to evaluate on the entire test set. We also provide human segmentation performance results on wiki-50 (P<sub>k</sub> = 14.97, Beeferman et al. 1999).
description: https://www.aclweb.org/anthology/N18-2075/
DOI: 10.5281/zenodo.4737321
title: wiki-727K + wiki-50 (Text Segmentation as a Supervised Learning Task)

description: Dataset accompanying Text Segmentation as a Supervised Learning Task (Koshorek et al. 2018) For this work we have created a new dataset, which we name wiki-727K. It is a collection of 727,746 English Wikipedia documents, and their hierarchical segmentation, as it appears in their table of contents. We randomly partitioned the documents into a train (80%), development (10%), and test (10%) set. Different text segmentation use-cases require different levels of granularity. For example, for segmenting text by overarching topic it makes sense to train a model that predicts only top-level segments, which are typically vary in topic – for example, “<em>History</em>”, “<em>Geography</em>”, and “<em>Demographics</em>”. For segmenting a radio broadcast into separate news stories, which requires finer granularity, it makes sense to train a model to predict sub-segments. Our dataset provides the entire segmentation information, and an application may choose the appropriate level of granularity. To generate the data, we performed the following preprocessing steps for each Wikipedia document: Removed all photos, tables, Wikipedia template elements, and other non-text elements. Removed single-sentence segments, documents with less than three segments, and documents where most segments were filtered. Divided each segment into sentences using the PUNKT tokenizer of the NLTK library. This is necessary for the use of our dataset as a benchmark, as with-out a well-defined sentence segmentation, it is impossible to evaluate different models. Because our test set is large, it is difficult to evaluate some of the existing methods, which are computationally demanding. Thus, we introduce wiki-50, a set of 50 randomly sampled test documents from wiki-727K. We use wiki-50 to evaluate systems that are too slow to evaluate on the entire test set. We also provide human segmentation performance results on wiki-50 (P<sub>k</sub> = 14.97, Beeferman et al. 1999).
description: https://www.aclweb.org/anthology/N18-2075/
DOI: 10.5517/ccdc.csd.cc2744xg
title: CCDC 2059575: Experimental Crystal Structure Determination

description: Related Article: Arumugan Kalaiselvan, Aswini Spergen, Isukapalli Sai Vamsi Krishna, Vennapusa Sivaranjana Reddy, Sabapathi Gokulnath|2021|Chem.Commun.|57|4420|doi:10.1039/D1CC00868D
DOI: 10.5517/ccdc.csd.cc2751t9
title: CCDC 2060440: Experimental Crystal Structure Determination

description: Related Article: Sofia Friães, Sara Realista, Clara S. B. Gomes, Paulo N. Martinho, Luis F. Veiros, Martin Albrecht, Beatriz Royo|2021|Dalton Trans.|50|5911|doi:10.1039/D1DT00444A
DOI: 10.5517/ccdc.csd.cc2751wc
title: CCDC 2060442: Experimental Crystal Structure Determination

description: Related Article: Sofia Friães, Sara Realista, Clara S. B. Gomes, Paulo N. Martinho, Luis F. Veiros, Martin Albrecht, Beatriz Royo|2021|Dalton Trans.|50|5911|doi:10.1039/D1DT00444A
DOI: 10.5517/ccdc.csd.cc2751vb
title: CCDC 2060441: Experimental Crystal Structure Determination

description: Related Article: Sofia Friães, Sara Realista, Clara S. B. Gomes, Paulo N. Martinho, Luis F. Veiros, Martin Albrecht, Beatriz Royo|2021|Dalton Trans.|50|5911|doi:10.1039/D1DT00444A
DOI: 10.5517/ccdc.csd.cc2751xd
title: CCDC 2060443: Experimental Crystal Structure Determination

description: Related Article: Sofia Friães, Sara Realista, Clara S. B. Gomes, Paulo N. Martinho, Luis F. Veiros, Martin Albrecht, Beatriz Royo|2021|Dalton Trans.|50|5911|doi:10.1039/D1DT00444A
DOI: 10.5517/ccdc.csd.cc2764rc
title: CCDC 2061492: Experimental Crystal Structure Determination

description: Related Article: Seonghwan Lee, Seok Jeong, Junmo Seong, Jaewoong Lim, Amitosh Sharma, Somi Won, Dohyun Moon, Seung Bin Baek, Myoung Soo Lah|2021|Mater. Chem. Front.|5|3621|doi:10.1039/D1QM00251A
DOI: 10.5517/ccdc.csd.cc277bc6
title: CCDC 2062627: Experimental Crystal Structure Determination

description: Related Article: Caroline G. Webster, Hyeri Park, Amanda F. Ennis, Jiyong Hong|2021|Tetrahedron Lett.|71|153055|doi:10.1016/j.tetlet.2021.153055
DOI: 10.5517/ccdc.csd.cc277j12
title: CCDC 2062803: Experimental Crystal Structure Determination

description: Related Article: Arumugan Kalaiselvan, Aswini Spergen, Isukapalli Sai Vamsi Krishna, Vennapusa Sivaranjana Reddy, Sabapathi Gokulnath|2021|Chem.Commun.|57|4420|doi:10.1039/D1CC00868D
DOI: 10.5517/ccdc.csd.cc277zt9
title: CCDC 2063261: Experimental Crystal Structure Determination

description: Related Article: Wangqin Ji, Hai-Hong Wu, Wenbo Li, Junliang Zhang|2021|Chem.Commun.|57|4448|doi:10.1039/D1CC00960E
DOI: 10.25919/tq9a-kg35
title: Parkes observations for project P1080 semester 2021APRS_01

description: We propose continued timing observations of a new pulsar discovered with the MWA. Thanks to the interferometric advantages of the MWA and our recent GMRT follow-up, we have been able to determine the pulsar's position to ~5 arcseconds precision, which may help a faster convergence to its timing solution. This will help determine the pulsar's spin and astrometric parameters. The analysis of Parkes and GMRT data suggests that the pulsar is likely a steep-spectrum, low-luminosity object. Observations with the ultra-wide band low-frequency receiver will help further reinforce this, besides allowing us to explore the frequency dependence of its polarisation and a large variability that is seen at multiple frequency bands. Most importantly, these observations will ensure continued timing of the pulsar through the next semester when the MWA is unlikely to be available for regular observations.
DOI: 10.5281/zenodo.4737890
title: R2D Tool Example E6Shakemap

description: R2D Tool Example E6Shakemap
DOI: 10.15468/kphmgc
title: A new hygrobatid genus from Australia (Acari: Hydrachnidia: Hygrobatidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Smit, H. (2015): A new hygrobatid genus from Australia (Acari: Hydrachnidia: Hygrobatidae). Acarologia 55 (4): 431-433, DOI: 10.1051/acarologia/20152181, URL: http://dx.doi.org/10.1051/acarologia/20152181
DOI: 10.34740/kaggle/dsv/2194087
title: Herbarium 2021 RBS
DOI: 10.5517/ccdc.csd.cc278rfq
title: CCDC 2063993: Experimental Crystal Structure Determination

description: Related Article: Wen-Jing Qin, Ji-Rui Zhang, Xu-Ke Tian, Xiao-Gang Yang, Yu-Ming Guo|2021|CrystEngComm|23|3094|doi:10.1039/D1CE00303H
DOI: 10.5517/ccdc.csd.cc278zvc
title: CCDC 2064223: Experimental Crystal Structure Determination

description: Related Article: Meng-Ru Yin, Qing-Qing Yan, Bin Li, Guo-Ping Yong|2021|CrystEngComm|23|3196|doi:10.1039/D1CE00257K
DOI: 10.5517/ccdc.csd.cc278zwd
title: CCDC 2064224: Experimental Crystal Structure Determination

description: Related Article: Meng-Ru Yin, Qing-Qing Yan, Bin Li, Guo-Ping Yong|2021|CrystEngComm|23|3196|doi:10.1039/D1CE00257K
DOI: 10.5517/ccdc.csd.cc278zxf
title: CCDC 2064225: Experimental Crystal Structure Determination

description: Related Article: Meng-Ru Yin, Qing-Qing Yan, Bin Li, Guo-Ping Yong|2021|CrystEngComm|23|3196|doi:10.1039/D1CE00257K
DOI: 10.5517/ccdc.csd.cc278zyg
title: CCDC 2064226: Experimental Crystal Structure Determination

description: Related Article: Meng-Ru Yin, Qing-Qing Yan, Bin Li, Guo-Ping Yong|2021|CrystEngComm|23|3196|doi:10.1039/D1CE00257K
DOI: 10.5517/ccdc.csd.cc26tl7w
title: CCDC 2050378: Experimental Crystal Structure Determination

description: Related Article: Wenxin Hao, Xiaoping Yang, Yanan Ma, Mengyu Niu, Dongliang Shi, Desmond Schipper|2021|Dalton Trans.|50|5865|doi:10.1039/D1DT00230A
DOI: 10.5281/zenodo.4737873
title: Apache Software Foundation Incubator Project Sustainability Dataset

description: The data set contains ASF incubator data and the associated scripts using Beautifulsoup, pg8000 packages.
DOI: 10.5281/zenodo.4737848
title: DeepCrime mutations for Speaker Recognition system

description: This is the dataset of mutations generated for Speaker Recognition subject system by DeepCrime mutation testing tool as part of the "DeepCrime: Mutation Testing of Deep Learning Systems based on Real Faults" paper published at ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2021. The second part of this dataset is stored under the DOI 10.5281/zenodo.4737850.
DOI: 10.5281/zenodo.4737847
title: DeepCrime mutations for Speaker Recognition system

description: This is the dataset of mutations generated for Speaker Recognition subject system by DeepCrime mutation testing tool as part of the "DeepCrime: Mutation Testing of Deep Learning Systems based on Real Faults" paper published at ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2021. The second part of this dataset is stored under the DOI 10.5281/zenodo.4737850.
DOI: 10.5281/zenodo.4737850
title: DeepCrime and DeepMutation++ mutations for Speaker Recognition system

description: This is the dataset of mutations generated for Speaker Recognition subject system by DeepCrime and DeepMutation++ mutation testing tools as part of the "DeepCrime: Mutation Testing of Deep Learning Systems based on Real Faults" paper published at ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2021. The first part of this dataset is stored under the DOI 10.5281/zenodo.4737848.