cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc252tx0
title: CCDC 1998722: Experimental Crystal Structure Determination

description: Related Article: Tao Kang, Hai-Tao Qu, Cheng-Guo Liu|2021|Ind.J.Heterocycl.Chem.|31|259|
DOI: 10.5517/ccdc.csd.cc28n8w3
title: CCDC 2104865: Experimental Crystal Structure Determination

description: Related Article: Allen T. Gordon, Olufunso O. Abosede, Songeziwe Ntsimango, Eric C. Hosten, Nonzuzo Myeza, Armorel van Eyk, Leonie Harmse, Adeniyi S. Ogunlaja|2022|Polyhedron|214|115649|doi:10.1016/j.poly.2022.115649
DOI: 10.5517/ccdc.csd.cc289cjh
title: CCDC 2094376: Experimental Crystal Structure Determination

description: Related Article: Gen Li, Wan-Shan Liu, Shuai-Liang Yang, Lin Zhang, Ran Bu, En-Qing Gao|2022|Inorg.Chem.|61|902|doi:10.1021/acs.inorgchem.1c02741
DOI: 10.6084/m9.figshare.18631901.v1
title: ClinicalTrials_Prediction_IntegratedData_27.12.21.zip

description: The zip file contains the integrated data that shall be kept in Neo4j data folder. <br>
DOI: 10.5281/zenodo.5874207
title: Mormyrus proboscirostris decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5874206
title: Mormyrus proboscirostris decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.18648473
title: Supplemental_Dataset_1

description: Excel file of marine jumbo phage genomic features (length, contigs, population representatives), host predictions, network cluster membership, Iyer group designation, jumbo phage detection results
DOI: 10.6084/m9.figshare.18648473.v1
title: Supplemental_Dataset_1

description: Excel file of marine jumbo phage genomic features (length, contigs, population representatives), host predictions, network cluster membership, Iyer group designation, jumbo phage detection results
DOI: 10.5519/qd.vhufmc22
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (800 records)
DOI: 10.6084/m9.figshare.18648815
title: Supplemental_Dataset_2

description: VOG matrix used to generate network and cluster membership of all sequences in network and table of features of phage sequences (i.e. cluster membership) used to annotate network
DOI: 10.5281/zenodo.5874325
title: Greenheugh capping unit ChemCam derived data products

description: The ChemCam derived data products used in Bedford et al. (2022) "An insight into ancient aeolian processes and post-Noachian aqueous alteration in Gale crater; Mars, using ChemCam geochemical data from the Greenheugh capping unit."
DOI: 10.6084/m9.figshare.18649391
title: Supplemental_Dataset_3

description: Protein annotation file; sheet 1 contains marine jumbo phage protein hits to EggNOG, VOG,and Pfam; sheet 2 contains category descriptions; sheet 3 specifies virion structure VOGs
DOI: 10.6084/m9.figshare.18649391.v1
title: Supplemental_Dataset_3

description: Protein annotation file; sheet 1 contains marine jumbo phage protein hits to EggNOG, VOG,and Pfam; sheet 2 contains category descriptions; sheet 3 specifies virion structure VOGs
DOI: 10.21227/a167-q711
title: Fig6_data

description: This data contains a FFT of the motion of a levitated particle, extracted with particle tracking software.
DOI: 10.6084/m9.figshare.18649502
title: Supplemental_Dataset_4

description: Read mapping results (counts, fraction covered, RPKM, presence/absence); sample metadata (i.e. longitude, latitude, biome)
DOI: 10.6084/m9.figshare.18649502.v1
title: Supplemental_Dataset_4

description: Read mapping results (counts, fraction covered, RPKM, presence/absence); sample metadata (i.e. longitude, latitude, biome)
DOI: 10.17632/3pdvskgs86.1
title: Distanciamento social por Covid 19: repercussão na rotina de universitários

description: Introdução: Necessitando de medidas de combate à pandemia por SARS-CoV-2, a Organização Mundial da Saúde orientou o distanciamento social como estratégia para mitigar seus impactos. Dentre as medidas adotadas para evitar aglomerações, universidades se adaptaram para modalidade de ensino online. O objetivo é refletir sobre a influência do distanciamento social por covid-19, na rotina de estudantes de ciências da saúde. Materiais e Métodos: É uma revisão integrativa, com buscas nas bases de dados Pubmed, MEDLINE, LILACS, WHO COVID, bioRxiv, e medRxiv, nos sites da Pubmed, BVS e Portal de buscas da Organização Mundial da Saúde para covid-19, com descritores nas seguintes combinações: “Infecções por coronavírus e estudantes de ciências de saúde” e “Covid-19 e estudantes de ciências de saúde”. Inicialmente encontrou-se 1069 artigos. Resultados: Após refinamento 23 artigos compuseram o estudo. Os resultados foram categorizados como 1-desordens nas competências emocionais, 2-ensino, 3-alimentação, sono e atividade física e 4-efeitos de natureza diversas. Manifestações frequentes: ansiedade, depressão, estresse, incerteza e angústia, mudanças no sono e atividade física. Vantagem ou desvantagem do ensino online, teve percentual de 50% cada. Conclusão: Conhecer como o distanciamento social influenciou na rotina dos estudantes é relevante para que as universidades desenvolvam programas de suporte para as necessidades apresentadas na realidade atual, e consigam projetar programas de enfrentamento para futuras crises epidêmicas, auxiliando na redução dos impactos resultantes.
DOI: 10.5281/zenodo.5874456
title: Bitstream Compaction iCE40 - Bitstreams and Reconfiguration Time Measurements

description: The HDF5 file contains data from an experiment concerning bitstream compaction for Lattice iCE40 FPGAs. Four projects were synthesized with two different toolchains and afterwards compacted in two different levels, resulting in 24 bitstreams. The creation of each bitstream was repeated 100 times to guard against variations caused by errors in the toolchains or by the compaction. No such errors occured and the configuration is the same for every repetition. The reconfiguration time for each of the 24 bitstreams was measured 10.000 times. Toolchains: Lattice iCEcube2 Open Source Toolchain (Yosys, nextpnr, Project Icestorm) Projects: empty: Completely empty project, that uses no FPGA resources. b12: A project from the ITC’99 benchmark suite. aes: A simple AES-128 encryption module. picosoc: RISC-V system on a chip Compaction levels: original: uncompacted bitstream builtin: compacted with two methods also available in iCEcube2 compact: compacted with all five methods File structure: The data is organzied hierarchical. It is first split between bitstreams and measurements, then by toolchain, then by project and finally by compaction level.
description: This work was supported by the German Federal Ministry of Education and Research (BMBF, 01IS18026B) by funding the competence center for Big Data and AI "ScaDS.AI Dresden/Leipzig".
DOI: 10.5281/zenodo.5874455
title: Bitstream Compaction iCE40 - Bitstreams and Reconfiguration Time Measurements

description: The HDF5 file contains data from an experiment concerning bitstream compaction for Lattice iCE40 FPGAs. Four projects were synthesized with two different toolchains and afterwards compacted in two different levels, resulting in 24 bitstreams. The creation of each bitstream was repeated 100 times to guard against variations caused by errors in the toolchains or by the compaction. No such errors occured and the configuration is the same for every repetition. The reconfiguration time for each of the 24 bitstreams was measured 10.000 times. Toolchains: Lattice iCEcube2 Open Source Toolchain (Yosys, nextpnr, Project Icestorm) Projects: empty: Completely empty project, that uses no FPGA resources. b12: A project from the ITC’99 benchmark suite. aes: A simple AES-128 encryption module. picosoc: RISC-V system on a chip Compaction levels: original: uncompacted bitstream builtin: compacted with two methods also available in iCEcube2 compact: compacted with all five methods File structure: The data is organzied hierarchical. It is first split between bitstreams and measurements, then by toolchain, then by project and finally by compaction level.
description: This work was supported by the German Federal Ministry of Education and Research (BMBF, 01IS18026B) by funding the competence center for Big Data and AI "ScaDS.AI Dresden/Leipzig".
DOI: 10.15787/vtt1/jnuwwa
title: test

description: test
DOI: 10.6084/m9.figshare.12597926
title: Continuing Current Data

description: `Figure2_video_efield_cc_durations.xlsx` contains the dataset consisting of 50 current durations estimated from high speed video and HAMMA electric field waveforms used to create Figure 2. Provided are the dates of each high speed video, CG stroke times in UTC, estimated luminosity duration from the high speed video in milliseconds, estimated current duration from the HAMMA waveform in milliseconds, and the approximate distance between the CG stroke and the closest HAMMA sensor available in kilometers. <br>`Section3_ic_leader_cg_stroke.xlsx` contains the 366 manually classified CG strokes and IC leaders used to train and test the logistic regression model. Provided are the CG stroke/IC leader dates, CG stroke/initial IC leader times in UTC, estimated current durations for CG strokes from the HAMMA waveform in milliseconds, and the manually classified flash type (where C = intracloud and G = cloud-to-ground).<br>`TableS4.xslx` contains the Table S4 from the Supporting Information to the main manuscript.
DOI: 10.6084/m9.figshare.12597926.v3
title: Continuing Current Data

description: `Figure2_video_efield_cc_durations.xlsx` contains the dataset consisting of 50 current durations estimated from high speed video and HAMMA electric field waveforms used to create Figure 2. Provided are the dates of each high speed video, CG stroke times in UTC, estimated luminosity duration from the high speed video in milliseconds, estimated current duration from the HAMMA waveform in milliseconds, and the approximate distance between the CG stroke and the closest HAMMA sensor available in kilometers. <br>`Section3_ic_leader_cg_stroke.xlsx` contains the 366 manually classified CG strokes and IC leaders used to train and test the logistic regression model. Provided are the CG stroke/IC leader dates, CG stroke/initial IC leader times in UTC, estimated current durations for CG strokes from the HAMMA waveform in milliseconds, and the manually classified flash type (where C = intracloud and G = cloud-to-ground).<br>`TableS4.xslx` contains the Table S4 from the Supporting Information to the main manuscript.
DOI: 10.6084/m9.figshare.18646118.v1
title: ClinicalTrials_Prediction_SampleData

description: The attachment contains few samples of data curated in house for Machine learning model development<br>
DOI: 10.6084/m9.figshare.18646118.v2
title: S2_ClinicalTrials_Prediction_SampleData

description: The attachment contains few samples of data curated in house for Machine learning model development<br>
DOI: 10.6084/m9.figshare.18646118.v3
title: ClinicalTrials_Prediction_SampleData

description: The attachment contains few samples of data curated in house for Machine learning model development<br>
DOI: 10.6084/m9.figshare.18631901
title: Annexure_ClinicalTrials_Prediction_IntegratedData.zip

description: The zip file contains the integrated data that shall be kept in Neo4j data folder. <br>
DOI: 10.6084/m9.figshare.18631901.v2
title: ClinicalTrials_Prediction_IntegratedData.zip

description: The zip file contains the integrated data that shall be kept in Neo4j data folder. <br>
DOI: 10.34740/kaggle/dsv/3059139
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.6084/m9.figshare.18646118
title: Annexure_ClinicalTrials_Prediction_MLSampleData

description: The attachment contains few samples of data curated in house for Machine learning model development<br>
DOI: 10.6084/m9.figshare.18646118.v4
title: Annexure_ClinicalTrials_Prediction_SampleData

description: The attachment contains few samples of data curated in house for Machine learning model development<br>
DOI: 10.35111/4fw7-wy71
title: 2017 NIST OpenSAT Pilot - SSSF 

description: <h3>Introduction</h3><br>
<p>2017 NIST OpenSAT Pilot - SSSF was developed by <a href="https://www.nist.gov/">NIST</a> (National Institute of Standards and Technology) and contains approximately one hour of operational speech data, transcripts and annotation files used in the speech activity detection, automatic speech recognition (ASR), and keyword search (KWS) tasks of the 2017 <a href="https://www.nist.gov/itl/iad/mig/opensat">OpenSAT</a> Pilot evaluation. The source audio consists of radio and telephone dispatches during the Sofa Super Store fire (Charleston, South Carolina) in June 2007 (SSSF), which claimed the lives of nine firefighters. These recordings contain content that some may find disturbing.</p><br>
<p>The NIST Open Speech Analytic Technologies (OpenSAT) Evaluation Series was designed to bring together researchers developing different types of technologies to address speech analytic challenges present in some of the most difficult acoustic conditions with the end goal of improving the state-of-the-art through objective, large-scale common evaluations. The 2017 pilot focused on the public safety communications domain. The SSSF audio represents real-world, fire response, operational data with multiple challenges for system analytics, such as land-mobile-radio transmission effects, significant background noise, speech under stress and variable decibel levels. See the <a href="https://sat.nist.gov/">OpenSAT website</a> for more information.</p><br>
<h3>Data</h3><br>
<p>This dataset was created from the audio and logs of SSSF radio and telephone dispatches and transcripts of those dispatches. The transcripts were re-annotated and transformed by NIST into the formats required to provide a reference key for scoring system output in the pilot OpenSAT evaluation.</p><br>
<p>The data is divided into a 30-minute development set and a 30-minute evaluation set. Audio is presented as 16 bit, 8kHz, NIST SPHERE format files. Accompanying reference files are divided by analytic tasks utilized in the OpenSAT Pilot and are UTF-8 encoded text or XML files. ASR and KWS scoring tools are also included.</p><br>
<h3>Samples</h3><br>
<p>Please view the following samples:</p><br>
<ul><br>
<li><a href="desc/addenda/LDC2022S01.sph">Audio Sample (SPH)</a></li><br>
<li><a href="desc/addenda/LDC2022S01_annotation.txt">Transcript Sample (TXT)</a></li><br>
<li><a href="desc/addenda/LDC2022S01.stm">STM Sample (TXT)</a></li><br>
<li><a href="desc/addenda/LDC2022S01.txt">Annotation Sample (TXT)</a></li><br>
</ul><br>
<h3>Updates</h3><br>
<p>None at this time.</p></br>
Portions © 2022 Trustees of the University of Pennsylvania
DOI: 10.5281/zenodo.5873717
title: Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

description: <strong>Power Generation Data Set</strong> This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. <br> <strong>Description</strong><br> Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> . This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. <br> <strong>Content</strong> The following files are contained in this repository: README.md - this file images.zip [2.0GB] - contains 2131 GeoTIFF images segmentation_labels.zip [1.5MB] - contains 2131 JSON files labels.csv [310KB] - contains additional labels for each image: Generation output rate [4],[5] Country Type of fired fuel Latitude and longitude of the power plant Concurrent weather information (temperature, humidity and wind vector) <strong>Acknowledgement</strong> If you use this data set, please cite our publication: Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation. <br> <strong>Author</strong> Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch <br> <strong>References</strong><br> [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566
DOI: 10.5517/ccdc.csd.cc219cj8
title: CCDC 1885839: Experimental Crystal Structure Determination

description: Related Article: Hui-Sheng Wang, Cheng-Ling Yin, Zhao-Bo Hu, Yong Chen, Zhi-Quan Pan, You Song, Yi-Quan Zhang, Zai-Chao Zhang|2019|Dalton Trans.|48|10011|doi:10.1039/C9DT00774A
DOI: 10.5517/ccdc.csd.cc22fc74
title: CCDC 1919465: Experimental Crystal Structure Determination

description: Related Article: Mi-Hee Jung, Kyoung Chul Ko, Woo Ram Lee|2019|Dalton Trans.|48|15074|doi:10.1039/C9DT03469B
DOI: 10.5517/ccdc.csd.cc295xt7
title: CCDC 2120859: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5517/ccdc.csd.cc295xv8
title: CCDC 2120860: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5517/ccdc.csd.cc295xw9
title: CCDC 2120861: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5517/ccdc.csd.cc295xxb
title: CCDC 2120862: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5517/ccdc.csd.cc295xyc
title: CCDC 2120863: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5517/ccdc.csd.cc295xzd
title: CCDC 2120864: Experimental Crystal Structure Determination

description: Related Article: Michaël Teixeira, Renata A. Maia, Lydia Karmazin, Benoît Louis, Stéphane A. Baudron|2022|CrystEngComm|24|601|doi:10.1039/D1CE01497H
DOI: 10.5281/zenodo.5794867
title: Seroprevalence of anti-SARS-CoV-2 antibodies among communities in Bangui, Central African Republic

description: Significance for public health This is a serological survey conducted in the Central Africa Republic among the general urban population not vaccinated against COVID-19. Its reveals a high cumulative level of immunity, thus indicating a significant degree of spread of SARS-CoV-2 in the population. More comprehensive studies to explore the impact of new variants on this quasi-herd-immunity as well as its effect on the immunity in people vaccinated against COVID-19.
DOI: 10.5281/zenodo.5794866
title: Seroprevalence of anti-SARS-CoV-2 antibodies among communities in Bangui, Central African Republic

description: Significance for public health This is a serological survey conducted in the Central Africa Republic among the general urban population not vaccinated against COVID-19. Its reveals a high cumulative level of immunity, thus indicating a significant degree of spread of SARS-CoV-2 in the population. More comprehensive studies to explore the impact of new variants on this quasi-herd-immunity as well as its effect on the immunity in people vaccinated against COVID-19.
DOI: 10.6084/m9.figshare.11881332.v7
title: Development and Evaluation of a Low-Cost Data Acquisition System using Heterogeneous Sensors

description: Acquiring the human’s musculoskeletal movements is pivotal in conducting physiotherapeutic studies, rehabilitation exercises and, designing assistive devices to cater mobility impairments. Existing studies are generally more focused on measuring a specific phenomenon and/or lack the diversity needed to cover a range of human activities. In this context, the research presented herein is aimed to develop a low-cost wearable sensory system, having a diverse set of sensors, which can acquire data of healthy and rehabilitated subjects alike during diverse locomotor activities in both indoor and outdoor environments. The system consists of twenty wearable sensors, and two off-the-shelf NI’s myRIO microcontroller boards. The statistical analysis showed no significant difference between and among the subjects for various locomotor activities (P-values &lt; 0.05), hence, showing the system’s reliability and reproducibility. Gait-event identification (i.e., heel contact/toe off) has also been evaluated and showed promising results (overall time difference= ±50ms during level ground and ramp activities).