cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34943/426fe978-948c-4f4e-81a1-c6e826817dd2
title: Digby Island (VDIG) Oceanographic Radar System Deployed 2017-04-06

description: The CODAR SeaSonde 25MHz VDIG was deployed on 2017-04-06 at Digby Island (VDIG). The Digby Island CODAR station contributes to an array of CODARs to monitor surface currents in Chatham Sound. This device is a Oceanographic Radar System. Oceanographic radars measure the ocean surface currents and waves in near real-time, by timing reflected radio waves, and contribute to an accurate prediction of severe weather conditions. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.6084/m9.figshare.16621738
title: FLUX attention data

description: Attention data st for FLUX pipeline
DOI: 10.6084/m9.figshare.16621738.v2
title: FLUX attention data

description: Attention data st for FLUX pipeline
DOI: 10.5517/ccdc.csd.cc215tqs
title: CCDC 1882435: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215trt
title: CCDC 1882436: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215tnq
title: CCDC 1882433: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215snp
title: CCDC 1882402: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215tdg
title: CCDC 1882425: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215tfh
title: CCDC 1882426: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215tln
title: CCDC 1882431: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc215tmp
title: CCDC 1882432: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Dong Liu, Yue-Jian Lin, Zhen-Hua Li, Guo-Xin Jin|2020|National Science Review|7|1548|doi:10.1093/nsr/nwaa164
DOI: 10.5517/ccdc.csd.cc27h733
title: CCDC 2070214: Experimental Crystal Structure Determination

description: Related Article: Zohreh Hendi, Sirous Jamali, Seyed Mohamad J. Chabok, Ali Jamjah, Hamidreza Samouei, Zahra Jamshidi|2021|Inorg.Chem.|60|12924|doi:10.1021/acs.inorgchem.1c01302
DOI: 10.5517/ccdc.csd.cc27h744
title: CCDC 2070215: Experimental Crystal Structure Determination

description: Related Article: Zohreh Hendi, Sirous Jamali, Seyed Mohamad J. Chabok, Ali Jamjah, Hamidreza Samouei, Zahra Jamshidi|2021|Inorg.Chem.|60|12924|doi:10.1021/acs.inorgchem.1c01302
DOI: 10.5517/ccdc.csd.cc27m2cb
title: CCDC 2073911: Experimental Crystal Structure Determination

description: Related Article: Mahmut Yıldız, Nilüfer Bayrak, Hatice Yıldırım, Emel Mataracı-Kara, Deepak Shilkar, Venkatesan Jayaprakash, Amaç Fatih Tuyun|2021|Bioorg.Chem.|116|105316|doi:10.1016/j.bioorg.2021.105316
DOI: 10.5517/ccdc.csd.cc27z7fx
title: CCDC 2084639: Experimental Crystal Structure Determination

description: Related Article: Jing Zhao, Xin Li, Ying-Feng Han|2021|J.Am.Chem.Soc.|143|14428|doi:10.1021/jacs.1c06464
DOI: 10.5517/ccdc.csd.cc27z7hz
title: CCDC 2084641: Experimental Crystal Structure Determination

description: Related Article: Jing Zhao, Xin Li, Ying-Feng Han|2021|J.Am.Chem.Soc.|143|14428|doi:10.1021/jacs.1c06464
DOI: 10.5517/ccdc.csd.cc27z7j0
title: CCDC 2084642: Experimental Crystal Structure Determination

description: Related Article: Jing Zhao, Xin Li, Ying-Feng Han|2021|J.Am.Chem.Soc.|143|14428|doi:10.1021/jacs.1c06464
DOI: 10.5517/ccdc.csd.cc27z7k1
title: CCDC 2084643: Experimental Crystal Structure Determination

description: Related Article: Jing Zhao, Xin Li, Ying-Feng Han|2021|J.Am.Chem.Soc.|143|14428|doi:10.1021/jacs.1c06464
DOI: 10.5517/ccdc.csd.cc27z7l2
title: CCDC 2084644: Experimental Crystal Structure Determination

description: Related Article: Jing Zhao, Xin Li, Ying-Feng Han|2021|J.Am.Chem.Soc.|143|14428|doi:10.1021/jacs.1c06464
DOI: 10.6084/m9.figshare.16467531
title: Ontogenetic shape shifting drives changes in metabolic scaling: reconciling pace of life and metabolic theories

description: Body shape, measured as geometric morphometrics approach, standard metabolic rate (SMR) of one- and two-years old juvenile trout, body mass and centroid size.<br>
DOI: 10.6084/m9.figshare.16467531.v3
title: Ontogenetic shape shifting drives changes in metabolic scaling: reconciling pace of life and metabolic theories

description: Body shape, measured as geometric morphometrics approach, standard metabolic rate (SMR) of one- and two-years old juvenile trout, body mass and centroid size.<br>
DOI: 10.6084/m9.figshare.16467531.v2
title: Ontogenetic shape shifting drives changes in metabolic scaling: reconciling pace of life and metabolic theories

description: Body shape, measured as geometric morphometrics approach, standard metabolic rate (SMR) of one- and two-years old juvenile trout, body mass and centroid size.<br>
DOI: 10.6084/m9.figshare.16636265
title: Dataset from Human voice pitch measures are robust across a variety of speech recordings: methodological and theoretical implications

description: Fundamental frequency (<i>f</i><sub>o</sub>), perceived as voice pitch, is the most sexually dimorphic, perceptually salient and intensively studied voice parameter in human nonverbal communication. Thousands of studies have linked human <i>f</i><sub>o</sub> to biological and social speaker traits and life outcomes, from reproductive to economic. Critically, researchers have used myriad speech stimuli to measure <i>f</i><sub>o</sub> and infer its functional relevance, from individual vowels to longer bouts of spontaneous speech. Here, we acoustically analysed <i>f</i><sub>o</sub> in nearly 1000 affectively neutral speech utterances (vowels, words, counting, greetings, read paragraphs and free spontaneous speech) produced by the same 154 men and women, aged 18–67, with two aims: first, to test the methodological validity of comparing <i>f</i><sub>o</sub> measures from diverse speech stimuli and second, to test the prediction that the vast inter-individual differences in habitual <i>f</i><sub>o</sub> found between same-sex adults are preserved across speech types. Indeed, despite differences in linguistic content, duration, scripted or spontan­­eous production and within-individual variability, we show that 42–81% of inter-individual differences in <i>f</i><sub>o</sub> can be explained between any two speech types. Beyond methodological implications, together with recent evidence that inter-individual differences in <i>f</i><sub>o</sub> are remarkably stable across the lifespan and generalize to emotional speech and nonverbal vocalizations, our results further substantiate voice pitch as a robust and reliable biomarker in human communication.
DOI: 10.6084/m9.figshare.16636153
title: MRA-SUPP_AB1157_v_MG1655.xlsx

description: Cronan and Kuzminov - Microbiology Resource Announcements, 2021.<b>Supplemental Data:</b><br>Excel sheet containing all differences between <i>E. coli</i> strain AB1157 (CP076404.1) and MG1655 (NC_000913.3).
DOI: 10.3886/e149861
title: The Pleasure Study 2020-2021

description: The pleasure study was collected in 2020 and 2021. It collect data on Millennials gender and sexual experiences. The data set collects data on gender, sexuality, race, sexualexperiences, sexual partners, orgasm, religiosity, sexual function, gender dysphoria andsexual empowerment.
description: A focus on collecting trans, non-binary, and intersex respondents as well as cis men and cis women.
description: web-based survey;
DOI: 10.6084/m9.figshare.15022068
title: From Reddit to Wall Street: The role of committed minorities in financial collective action

description: The dataset consists of processed posts and comment IDs submitted to the r/wallstreetbets Reddit subreddit from 01-01-2016 up to 03-02-2021 related to the GameStop topic.<br>Topic relatedness is extracted from posts' title and body, requiring one of the following to appear: "GME" or "Gamestop" (both case insensitive).<br>All the comments published as answers to a GME-related post are considered as GME-related too.<br>Alongside the IDs, we share in this dataset the set of metrics used to assess the level of identitarian participation and commitment to the GME cause.IDs can be linked with data from the Reddit API to consistently match the processed metrics with the Reddit data.<br>For more details on data processing and content please refer to: https://arxiv.org/abs/2107.07361<br>Reddit data are not shared in this dataset because subjected to the Reddit API terms of use: https://www.reddit.com/wiki/api-terms.<br>
DOI: 10.6084/m9.figshare.15022068.v1
title: From Reddit to Wall Street: The role of committed minorities in financial collective action

description: The dataset consists of processed posts and comment IDs submitted to the r/wallstreetbets Reddit subreddit from 01-01-2016 up to 03-02-2021 related to the GameStop topic.<br><br>Topic relatedness is extracted from posts' title and body, requiring one of the following to appear: "GME" or "Gamestop" (both case insensitive).<br><br>All the comments published as answers to a GME-related post are considered as GME-related too.<br><br>Alongside the IDs, we share in this dataset the set of metrics used to assess the level of identitarian participation and commitment to the GME cause.<br>IDs can be linked with data from the Reddit API to consistently match the processed metrics with the Reddit data.<br><br>For more details on data processing and content please refer to: https://arxiv.org/abs/2107.07361<br><br>Reddit data are not shared in this dataset because subjected to the Reddit API terms of use: https://www.reddit.com/wiki/api-terms.<br><br><br>Below, find the list of metrics documenting the dataset.<br><br>'pic_res_0': commitment score<br>'pic_res_1': non-commitment score<br>'predicted_class': most probable class between 0 and 1<br>'committed': True if 'predicted_class'==0, False otherwise<br>'award_count': number of received awards (At the time the data were downloaded)<br>'award_price': dollars spent in total for awading the post/comment<br>'award_most_name': most commonly assigned award<br>'liwc_we': collective term count<br>'liwc_i': individual term count<br>'liwc_identity': liwc identity value<br>'body_support': 'support' score<br>'body_knowledge': 'knowledge score<br>'body_conflict': 'conflict' score<br>'body_power': 'power' score<br>'body_similarity': 'similarity' score<br>'body_fun': 'fun' score<br>'body_status': 'status' score<br>'body_trust': 'trust' score<br>'body_identity': 'identity' score<br>'body_romance': 'romance' score<br>'body_numsentences': count of terms<br>'body_identity_binary': identity class (1 identitarian, 0 otherwise)<br>'body_identity_q0.9': identity class thresholded at the 90%<br>'body_identity_q0.85': identity class thresholded at the 85%<br>'body_identity_q0.75': identity class thresholded at the 75%<br>'body_identity_q0.5': identity class thresholded at the 50%
DOI: 10.21231/s6k0-y614
title: Organic carbon dataset of paper submitted to Journal of Geophysical Research: Biogeosciences entitled "Seasonal and Spatial Variability of Dissolved Carbon Concentration and Composition in Lake Michigan Tributaries"
DOI: 10.5281/zenodo.5514035
title: Dataset HD-tDCS

description: This registry contains raw data regarding baseline and endpoint assessments of patients with COVID-19 in hospitals and in the ICU.
DOI: 10.5281/zenodo.4959930
title: Dataset HD-tDCS

description: This registry contains raw data regarding baseline and endpoint assessments of patients with COVID-19 in hospitals and in the ICU.
DOI: 10.7294/16587179
title: Simulation data for replicating the results and figures of the study, "A general metric for the similarity of both stochastic and deterministic system dynamics"

description: This dataset allows for the replication of all results and figures appearing in the paper "A general metric for the similarity of both stochastic and deterministic system dynamics" forthcoming in the journal <i>Entropy</i>. The scripts for running all experiments and for generating figures can be found at https://github.com/jantzen/distance_metric, and code for the methods tested in these experiments is attainable through the PyPI package "eugene" (e.g., via "pip install eugene") or at https://github.com/jantzen/eugene. However, by themselves these scripts will not exactly reproduce the data we reported. This is because most of our numerical experiments involve a stochastic component such that one cannot generate exactly the same data twice. We have therefore preserved in this dataset the specific results of the experimental runs reported in the published study. <br> To replicate a figure from the paper, extract the files in the relevant zip archive into the same directory as the figure generation script, and rename the extracted data file folder "data". So, for example, to run the generate_figures.py script, download the generate_data.zip file and extract in the same directory as the figure script. Change the name of the resulting "generate_data" folder to "data", and then run "python3 generate_figures.py".<br>The codes at the GitHub repository links provided above, available on 13 September 2021, are included in this dataset as zip files: eugene-master.zip and distance_metric-main.zip<br>
DOI: 10.5517/ccdc.csd.cc2804p4
title: CCDC 2085515: Experimental Crystal Structure Determination

description: Related Article: A. D. Yapryntsev, A. E. Baranchikov, A. V. Churakov, G. P. Kopitsa, A. A. Silvestrova, M. V. Golikova, O. S. Ivanova, Yu. E. Gorshkova, V. K. Ivanov|2021|RSC Advances|11|30195|doi:10.1039/D1RA05923H
DOI: 10.5517/ccdc.csd.cc281wjr
title: CCDC 2087184: Experimental Crystal Structure Determination

description: Related Article: Dalila Rocco, Samantha Novak, Alessandro Prescimone, Edwin C. Constable, Catherine E. Housecroft|2021|Polyhedron|208|115445|doi:10.1016/j.poly.2021.115445
DOI: 10.5517/ccdc.csd.cc282n23
title: CCDC 2087914: Experimental Crystal Structure Determination

description: Related Article: Guillaume Reynard, Hélène Lebel|2021|J.Org.Chem.|86|12452|doi:10.1021/acs.joc.1c01585
DOI: 10.5517/ccdc.csd.cc282n34
title: CCDC 2087915: Experimental Crystal Structure Determination

description: Related Article: Guillaume Reynard, Hélène Lebel|2021|J.Org.Chem.|86|12452|doi:10.1021/acs.joc.1c01585
DOI: 10.5517/ccdc.csd.cc280l52
title: CCDC 2085933: Experimental Crystal Structure Determination

description: Related Article: Arghya Dutta, Arnab Chatterjee, Tathagata Sarkar, Shubhamoy Chowdhury, Vinayak B Kamble, Rajarshi Ghosh|2021|Inorg.Chem.Commun.|133|108889|doi:10.1016/j.inoche.2021.108889
DOI: 10.5517/ccdc.csd.cc2890rc
title: CCDC 2094042: Experimental Crystal Structure Determination

description: Related Article: Mallesh Magini, Sampath Bitla, Muralidhar Reddy Puchakayala, Saritha Birudaraju, Ramulu Dhanavath, Krishnam Raju Atcha|2021|Results in Chemistry|3|100184|doi:10.1016/j.rechem.2021.100184
DOI: 10.21227/4wje-y777
title: Bioinformatics, Single Cell Sequencing

description: Test
DOI: 10.5066/p99qk6a4
title: SPARROW Model Simulated Nutrient and Suspended Sediment Loads in Streams when All Forests are Urbanized Across the Southeastern United States

description: The U.S. Geological Survey's SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Southeastern United States was used to simulate changes in total nitrogen, total phosphorus and suspended sediment load in streams under two scenarios: (1) where all forests are urbanized and (2) where all forests are urbanized and runoff is adjusted based on a non-forested landscape. This data release includes model input not published with the original model and used for scenario simulations, and model output for total nitrogen, total phosphorus, and suspended sediment under baseline conditions, scenario (1), and scenario (2). Original model input, output, and shapefiles are available (Roland and Hoos, 2020, https://https://doi.org/10.5066/p9a682gw) and original model development is described in Hoos and Roland (2019, https://doi.org/10.3133/sir20195135).
DOI: 10.21227/cqj6-3d11
title: Hackathon: Bioinformatics: Drug Target

description: Challenge DescriptionThe traditional drug discovery process is expensive and time-consuming. Accelerating thisprocess a significant challenge, for which taking a ML/AI-based pre-screening approach couldassist us with high-throughput virtual pre-screening of huge amount of drug candidates toidentify highly potent candidates for experimental testing and further validation.Goal:The goal of this challenge is to build effective ML/AI-based surrogate models that can accuratelypredict the docking scores of candidate drug molecules on SARS-CoV-2 protein targets.
DOI: 10.6084/m9.figshare.16636300
title: A file with lists of band chords for musical analysis

description: Band chords test data set
DOI: 10.6084/m9.figshare.16636300.v1
title: A file with lists of band chords for musical analysis

description: Band chords test data set
DOI: 10.21227/50q7-xv87
title: Hackathon: Imaging Informatics (CT Imaging)

description: Please see the descriptions below.
DOI: 10.21227/cp8m-x584
title: Hackathon: Sensor Informatics (Audio Signals)

description: Challenge Description and Goal:The pathogenesis of COVID-19 is increasingly suggesting impairments in the respiratory system. In this light, it is natural to ask – Can sound samples serve as acoustic biomarkers of COVID-19? If yes, an acoustics based COVID-19 diagnosis can provide a fast, contactless, and inexpensive testing scheme, with potential to supplement the existing molecular testing methods, such as RT-PCR and RAT. The present Challenge is an exploration of ideas to find answers to this question.
DOI: 10.21227/4vej-7w18
title: Hackathon: Public Health Informatics (Public Health Dynamics)

description: Data Challenge Description:COVID-19 has significantly impacted the entire world, changing the way of life for everyone. Knowing that the spread of the virus has been affecting almost all aspects of our day-to-day lives, it is critically important that we provide the relevant information, analyses, and predictions that can effectively inform the public so that everyone can make informed decisions during such epidemiological crisis.Data Challenge Goal:The goal of this data challenge is to predict the 7-day average of new COVID-19 cases and that of the positivity rate based on the historical public health data. Accurate prediction of such epidemiological trends can provide useful insights for the public, helping them make informed decisions regarding protection/mitigation measures, travel planning, and others.
DOI: 10.5281/zenodo.5514060
title: PQRI Elemental Impurity Interlaboratory Study Raw Data

description: The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters &lt;232&gt; and &lt;233&gt;, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations. The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.
DOI: 10.5281/zenodo.5514059
title: PQRI Elemental Impurity Interlaboratory Study Raw Data

description: The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters &lt;232&gt; and &lt;233&gt;, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations. The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.
DOI: 10.21227/aw2k-rk46
title: Hackathon: AI for Knowledge Discovery (NLP)

description: Challenge Description:Tens of thousands of research papers on the SARS-CoV-2 virus and the COVID-19 illness haveflooded journals and preprint servers, with many more added every day. This publication rate isleaving researchers unable to keep up with new findings and insights, even as they rely on theseto inform their own work in understanding and combating the disease. In particular, researchgroups investigating novel drug candidates with in-silico drug discovery efforts are in essencesearching for a proverbial needle in a haystack, and so rely on scientific insights from theliterature to guide their efforts toward the most fruitful directions. Thus, they are left to navigatethe current publication deluge for their primary guidance. In order to keep pace with the vastamount of relevant literature that continues to grow, researchers need more capable tools tolocate and filter the information they seek in these publications. To meet this pressing need, it isdesirable to develop and apply the latest Natural Language Processing (NLP) techniques forefficiently locating and filtering relevant information from a growing dataset of COVID-relatedliterature, to help accelerate drug discovery research. The proposed NLP challenge task is framedtowards designing and building a Question-Answering (QA) system to find answers to COVID-related questions in the scientific literature.Goal:A practical usage of NLP models with COVID relevant papers might be automated informationextraction from literature to facilitate drug discovery efforts. One of the crucial elements thatcan inform these efforts is the knowledge about viral proteins. The goal of this data challenge tobuild an NLP model to identify answers to protein-related questions from scientific papers.
DOI: 10.6084/m9.figshare.16637860
title: Biomolecules-Mitchell-Serum and Urine Compounds_all.xlsx

description: Serum and urine metabolites from hypertensive mice
DOI: 10.6084/m9.figshare.16637860.v2
title: Biomolecules-Mitchell-Serum and Urine Compounds_all.xlsx

description: Serum and urine metabolites from hypertensive mice