cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12011940
title: Targeting the Coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors Lopinavir, Ritonavir, and Nelfinavir.

description: This dataset consists of three videos in<b> .mp4 </b>format. The videos were generated using the computational technique of Supervised Molecular Dynamics (SuMD) to provide molecular insight on the whole interaction pathway of lopinavir, ritonavir, and nelfinavir, three potential C30 Endopeptidase inhibitors, with the last one taken into consideration due to the promising in-vitro activity shown against the structurally related SARS-CoV protease.These videos are supporting materials for the related publication.<br><br>Details of each video are as follows:- <b>Video_1: Lopinavir binding pathway against SARS-CoV-2 main protease.</b><br>The video is composed of four synchronized and animated panels that summarized a putative molecular recognition mechanism of HIV protease inhibitor <b>Lopinavir </b>against SARS-CoV-2 main protease. In the first panel (upper-left), the SuMD binding trajectory is reported. The backbone of SARS-CoV-2 main protease is represented using ribbon style (pink colour) and the protein residues within 4 Å of <b>Lopinavir </b>(rendered by green carbon atoms) are dynamically shown. In the second panel (upper-right), the distance between the inhibitor center of mass (CM) and the protein catalytic binding site during the entire trajectory is reported. The time evolution is reported in a ns scale. In the third panel (lower-left), the MMGBSA energy profile describing the binding event is reported. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 protease residues most contacted by <b>Lopinavir </b>during the whole binding simulation.- <b>Video_2: Ritonavir binding pathway against SARS-CoV-2 main protease.</b><br>As above, but for the protease inhibitor <b>Ritonavir </b>against SARS-CoV-2 main protease. The protein residues within 4 Å of <b>Ritonavir </b>are rendered by orange carbon atoms.- <b>Video_3: Nelfinavir binding pathway against SARS-CoV-2 main protease.</b><br>As above, but for the protease inhibitor <b>Nelfinavir </b>against SARS-CoV-2 main protease. The protein residues within 4 Å of <b>Nelfinavir </b>are rendered by cyan carbon atoms.<br><b>Software</b>All the SuMD trajectories have been carried out using AceMD engine and analyzed by an in-house tool written in tcl and python programming languages. MMGBSA protocol as implemented in AMBER 2014 was exploited to estimate protein-ligand interaction energy. NAMD engine was used for post-processing computation of electrostatic interactions, using AMBER ff14SB forcefield.<br><br>
DOI: 10.6084/m9.figshare.12011940.v1
title: Targeting the Coronavirus SARS-CoV-2: computational insights into the mechanism of action of the protease inhibitors Lopinavir, Ritonavir, and Nelfinavir.

description: This dataset consists of three videos in<b> .mp4 </b>format. The videos were generated using the computational technique of Supervised Molecular Dynamics (SuMD) to provide molecular insight on the whole interaction pathway of lopinavir, ritonavir, and nelfinavir, three potential C30 Endopeptidase inhibitors, with the last one taken into consideration due to the promising in-vitro activity shown against the structurally related SARS-CoV protease.These videos are supporting materials for the related publication.<br><br>Details of each video are as follows:- <b>Video_1: Lopinavir binding pathway against SARS-CoV-2 main protease.</b><br>The video is composed of four synchronized and animated panels that summarized a putative molecular recognition mechanism of HIV protease inhibitor <b>Lopinavir </b>against SARS-CoV-2 main protease. In the first panel (upper-left), the SuMD binding trajectory is reported. The backbone of SARS-CoV-2 main protease is represented using ribbon style (pink colour) and the protein residues within 4 Å of <b>Lopinavir </b>(rendered by green carbon atoms) are dynamically shown. In the second panel (upper-right), the distance between the inhibitor center of mass (CM) and the protein catalytic binding site during the entire trajectory is reported. The time evolution is reported in a ns scale. In the third panel (lower-left), the MMGBSA energy profile describing the binding event is reported. In the fourth panel (lower-right) cumulative electrostatic interactions are reported for the 15 protease residues most contacted by <b>Lopinavir </b>during the whole binding simulation.- <b>Video_2: Ritonavir binding pathway against SARS-CoV-2 main protease.</b><br>As above, but for the protease inhibitor <b>Ritonavir </b>against SARS-CoV-2 main protease. The protein residues within 4 Å of <b>Ritonavir </b>are rendered by orange carbon atoms.- <b>Video_3: Nelfinavir binding pathway against SARS-CoV-2 main protease.</b><br>As above, but for the protease inhibitor <b>Nelfinavir </b>against SARS-CoV-2 main protease. The protein residues within 4 Å of <b>Nelfinavir </b>are rendered by cyan carbon atoms.<br><b>Software</b>All the SuMD trajectories have been carried out using AceMD engine and analyzed by an in-house tool written in tcl and python programming languages. MMGBSA protocol as implemented in AMBER 2014 was exploited to estimate protein-ligand interaction energy. NAMD engine was used for post-processing computation of electrostatic interactions, using AMBER ff14SB forcefield.<br><br>
DOI: 10.5281/zenodo.4382073
title: Data of normal spectral emissivity measurements for Ta, Mo, W and Nb

description: Data aquired during my master thesis about the normal spectral emissivty of Ta, Mo, W and Nb measured with an ohmic pulse heating apparatus and a us-DOAP
DOI: 10.5281/zenodo.4382072
title: Data of normal spectral emissivity measurements for Ta, Mo, W and Nb

description: Data aquired during my master thesis about the normal spectral emissivty of Ta, Mo, W and Nb measured with an ohmic pulse heating apparatus and a us-DOAP
DOI: 10.25646/7768
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.13270094
title: Supplementary information to "Plastic in global rivers: are floods making it worse?"

description: Here we provide the data that was produced for the paper "Plastic in global rivers: are floods making it worse?" (Environmental Research Letters). The scripts that produced these results can be found through the following link: https://github.com/jasperroebroek/Plastic_flood).
DOI: 10.17632/4d7dk9czxw.1
title: Data for: Identification of living and dead microalgae cells with digital holography and verified in the East China Sea
DOI: 10.7910/dvn/vxy7lw
title: Meloidogyne images

description: Meloidogyne images
DOI: 10.25403/upresearchdata.13310060
title: Factors leading to successful organisational change outcomes: an in-depth study of organisational change at a large South African manufacturer

description: This is the data which was used for a qualitative study. Accompanying that are transcript of interviews, signed non disclosure & consent forms as well as Atlas.ti coding report.
DOI: 10.25403/upresearchdata.13310060.v1
title: Factors leading to successful organisational change outcomes: an in-depth study of organisational change at a large South African manufacturer

description: This is the data which was used for a qualitative study. Accompanying that are transcript of interviews, signed non disclosure & consent forms as well as Atlas.ti coding report.
DOI: 10.15468/3rrn2d
title: Towards completion of the species inventory of small-sized leaf-tailed geckos: two new species of Uroplatus from northern Madagascar

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ratsoavina, Fanomezana M., Glaw, Frank, Raselimanana, Achille P., Rakotoarison, Andolalao, Vieites, David R., Hawlitschek, Oliver, Vences, Miguel, Scherz, Mark D. (2020): Towards completion of the species inventory of small-sized leaf-tailed geckos: two new species of Uroplatus from northern Madagascar. Zootaxa 4895 (2): 251-271, DOI: https://doi.org/10.11646/zootaxa.4895.2.5
DOI: 10.15468/3a2v4y
title: A Baltic amber species of the diving beetle genus Coptotomus Say, 1830 (Coleoptera Dytiscidae: Coptotominae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hendrich, Lars, Balke, Michael (2020): A Baltic amber species of the diving beetle genus Coptotomus Say, 1830 (Coleoptera Dytiscidae: Coptotominae). Zootaxa 4895 (2): 285-290, DOI: https://doi.org/10.11646/zootaxa.4895.2.7
DOI: 10.25403/upresearchdata.13341689
title: The influence of institutional voids on the success of waste treatment entrepreneurial ecosystems (EEs) in the Western Cape and Gauteng Provinces

description: Interview recordings, consent forms and transcripts used to research the impact of institutional voids in the success of entrepreneurial ecosystems. It is a qualitative study hence interview methods was adopted.<br><br>
DOI: 10.25403/upresearchdata.13341689.v1
title: The influence of institutional voids on the success of waste treatment entrepreneurial ecosystems (EEs) in the Western Cape and Gauteng Provinces

description: Interview recordings, consent forms and transcripts used to research the impact of institutional voids in the success of entrepreneurial ecosystems. It is a qualitative study hence interview methods was adopted.<br><br>
DOI: 10.5519/qd.ah78a8zw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4215989 records)
DOI: 10.6084/m9.figshare.13315259
title: User Perceived Disinformation on Reddit: Manual Classification

description: Manual annotation of Reddit comments as "flags" or "non-flags".<br><br>Annotations used in <b>training set</b> for ML model and validation set for POS matcher:(1.200 comments in total)<br>classified_regex_labeled_train.csv<br><br><br>Annotations used in <b>test set</b> for ML model and POS matcher:<br>(300 comments in total)<br>classified_regex_labeled_test.csv<br><br><br><b>Codebook </b>(for both files)<br><b>id</b>ID number starting from 1<b>indx</b>Index from larger file containing all matches<b>word</b>Which keyword does it match (disinformation, fake news, misleading, unreliable, propaganda, bullshit)<b>subm_title</b>Title of Reddit post / submission.<b>domain</b>Web domain of link shared in Reddit post.<b>comm_body</b>Full text of comment.<b>disinformation</b>Matched by POS matcher as "disinformation".<b>fakenews</b>Matched by POS matcher as "fake news"<b><br></b><b>bs</b>Matched by POS matcher as "bullshit"<b><br></b><b>misleading</b>Matched by POS matcher as "misleading/clickbait"<b><br></b><b>unreliable</b>Matched by POS matcher as "unreliable"<b><br></b><b>propaganda</b>Matched by POS matcher as "propaganda"<b><br></b><b>sample</b>Sample from keyword matching ("all") or sample from POS matches ("pos")<b>matches_POS</b>Matches at least one POS pattern with the POS matcher.<b>consensus</b>Manual annotation (consensus of both coders):- f = comment was coded as "informal flag for false information"- n = comment was coded as "NOT informal flag for false information"- u = uncertain- na/r = removed for being automated message<br><br><br>
DOI: 10.6084/m9.figshare.13315259.v1
title: User Perceived Disinformation on Reddit: Manual Classification

description: Manual annotation of Reddit comments as "flags" or "non-flags".<br><br>Annotations used in <b>training set</b> for ML model and validation set for POS matcher:(1.200 comments in total)<br>classified_regex_labeled_train.csv<br><br><br>Annotations used in <b>test set</b> for ML model and POS matcher:<br>(300 comments in total)<br>classified_regex_labeled_test.csv<br><br><br><b>Codebook </b>(for both files)<br><b>id</b>ID number starting from 1<b>indx</b>Index from larger file containing all matches<b>word</b>Which keyword does it match (disinformation, fake news, misleading, unreliable, propaganda, bullshit)<b>subm_title</b>Title of Reddit post / submission.<b>domain</b>Web domain of link shared in Reddit post.<b>comm_body</b>Full text of comment.<b>disinformation</b>Matched by POS matcher as "disinformation".<b>fakenews</b>Matched by POS matcher as "fake news"<b><br></b><b>bs</b>Matched by POS matcher as "bullshit"<b><br></b><b>misleading</b>Matched by POS matcher as "misleading/clickbait"<b><br></b><b>unreliable</b>Matched by POS matcher as "unreliable"<b><br></b><b>propaganda</b>Matched by POS matcher as "propaganda"<b><br></b><b>sample</b>Sample from keyword matching ("all") or sample from POS matches ("pos")<b>matches_POS</b>Matches at least one POS pattern with the POS matcher.<b>consensus</b>Manual annotation (consensus of both coders):- f = comment was coded as "informal flag for false information"- n = comment was coded as "NOT informal flag for false information"- u = uncertain- na/r = removed for being automated message<br><br><br>
DOI: 10.6084/m9.figshare.13174145
title: Descriptives of User Perceived Disinformation on Reddit

description: Jupyter notebook showing how the results presented in the paper "Raising the Flag: Monitoring User Perceived Disinformation on Reddit" were obtained.<br>- Section 4.2 POS matching on sentences- Section 4.4.3. Machine Learning- Section 5. Results - general descriptives<br>- Section 5.1. Trends and peaks (including topics for peaks)<br>- Section 5.5 Comparison with a fact checking website<br><br>Also, datasets needed to run some of the analyses.
DOI: 10.6084/m9.figshare.13174145.v1
title: Descriptives of User Perceived Disinformation on Reddit

description: Jupyter notebook showing how the results presented in the paper "Raising the Flag: Monitoring User Perceived Disinformation on Reddit" were obtained.<br>- Section 4.2 POS matching on sentences- Section 4.4.3. Machine Learning- Section 5. Results - general descriptives<br>- Section 5.1. Trends and peaks (including topics for peaks)<br>- Section 5.5 Comparison with a fact checking website<br><br>Also, datasets needed to run some of the analyses.
DOI: 10.5281/zenodo.4349220
title: Data: Autonomous Drones for Search and Rescue in Forests

description: Supplementary Dataset for the article Autonomous Drones for Search and Rescue in Forests. <strong>Abstract:</strong> Drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a first prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found 38 out of 42 hidden persons; average precision was 86% for predefined flight paths, while adaptive path planning (where potential findings are double-checked) increased confidence by 15%. Image processing, classification, and dynamic flight-path adaptation are computed on-board in real time and while flying. Our finding that deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures allows flights to be shortened and reduces recording requirements to one tenth of the number of images needed for sampling using two-dimensional synthetic apertures. The goal of our adaptive path planning is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, as it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.
description: Funding: LIT – Linz Institute of Technology (grant: LIT-2019-8-SEE-114)
DOI: 10.5281/zenodo.4349219
title: Data: Autonomous Drones for Search and Rescue in Forests

description: Supplementary Dataset for the article Autonomous Drones for Search and Rescue in Forests. <strong>Abstract:</strong> Drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a first prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found 38 out of 42 hidden persons; average precision was 86% for predefined flight paths, while adaptive path planning (where potential findings are double-checked) increased confidence by 15%. Image processing, classification, and dynamic flight-path adaptation are computed on-board in real time and while flying. Our finding that deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures allows flights to be shortened and reduces recording requirements to one tenth of the number of images needed for sampling using two-dimensional synthetic apertures. The goal of our adaptive path planning is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, as it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.
description: Funding: LIT – Linz Institute of Technology (grant: LIT-2019-8-SEE-114)
DOI: 10.5517/ccdc.csd.cc2487h5
title: CCDC 1974126: Experimental Crystal Structure Determination

description: Related Article: Bo Qi, Sai An, Jiancheng Luo, Tianbo Liu, Yu-Fei Song|2020|Chem.-Eur.J.|26|16802|doi:10.1002/chem.202003083
description: Related Article: Bo Qi|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24lsvd
title: CCDC 1984274: Experimental Crystal Structure Determination

description: Related Article: Bo Qi, Sai An, Jiancheng Luo, Tianbo Liu, Yu-Fei Song|2020|Chem.-Eur.J.|26|16802|doi:10.1002/chem.202003083
DOI: 10.5517/ccdc.csd.cc23dyyf
title: CCDC 1948875: Experimental Crystal Structure Determination

description: Related Article: Bo Qi|2019|CSD Communication|||
description: Related Article: Bo Qi, Sai An, Jiancheng Luo, Tianbo Liu, Yu-Fei Song|2020|Chem.-Eur.J.|26|16802|doi:10.1002/chem.202003083
DOI: 10.5517/ccdc.csd.cc25kcgl
title: CCDC 2012689: Experimental Crystal Structure Determination

description: Related Article: Rai-Shung Liu,  Amit Vijay Sasane,  Antony Sekar Kulandai Raj,  Tzu-Hsuan Chao,  Mu-Jeng Chen|2020|Chem.-Eur.J.|26|16932|doi:10.1002/chem.202003840
DOI: 10.5517/ccdc.csd.cc25l41z
title: CCDC 2013420: Experimental Crystal Structure Determination

description: Related Article: Rai-Shung Liu,  Amit Vijay Sasane,  Antony Sekar Kulandai Raj,  Tzu-Hsuan Chao,  Mu-Jeng Chen|2020|Chem.-Eur.J.|26|16932|doi:10.1002/chem.202003840
DOI: 10.5517/ccdc.csd.cc25lk2f
title: CCDC 2013824: Experimental Crystal Structure Determination

description: Related Article: Rai-Shung Liu,  Amit Vijay Sasane,  Antony Sekar Kulandai Raj,  Tzu-Hsuan Chao,  Mu-Jeng Chen|2020|Chem.-Eur.J.|26|16932|doi:10.1002/chem.202003840
DOI: 10.5517/ccdc.csd.cc25x35d
title: CCDC 2023003: Experimental Crystal Structure Determination

description: Related Article: Rai-Shung Liu,  Amit Vijay Sasane,  Antony Sekar Kulandai Raj,  Tzu-Hsuan Chao,  Mu-Jeng Chen|2020|Chem.-Eur.J.|26|16932|doi:10.1002/chem.202003840
DOI: 10.25505/fiz.icsd.cc25yyqt
title: CSD 2024787: Experimental Crystal Structure Determination

description: Related Article: Bertram Peters, Chloé Krampe, Julian Klärner, Stefanie Dehnen|2020|Chem.-Eur.J.|26|16683|doi:10.1002/chem.202003887
DOI: 10.25505/fiz.icsd.cc26vxzz
title: CSD 2051672: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2020|ICSD Communication|||
DOI: 10.6084/m9.figshare.13469157
title: GEOMOD-MCA

description: We employed a hybrid GEOMOD-Markov Chain model
DOI: 10.6084/m9.figshare.13469157.v1
title: GEOMOD-MCA

description: We employed a hybrid GEOMOD-Markov Chain model
DOI: 10.6084/m9.figshare.12656165.v2
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.5281/zenodo.1332024
title: Earliest records of Oxalis corniculata by country

description: <em>Oxalis corniculata</em> L. is a weed with a global distribution. Here I have gathered first observations of this species in as many countries as possible. These data come from human observations, historic literature, herbarium specimens and paleobotanical studies. Many of these records come from the Global Biodiversity Information Facility at the references below, but also the monograph of Lourteig (1979). Custom data export (3rd Sept 2015). https://doi.org/10.15468/dl.nuaaek Available from https://www.gbif.org/occurrence/download/0001780-150831095807960 [4th August 2018] Custom data export (1st March 2016). https://doi.org/10.15468/dl.bdlcpo Available from https://www.gbif.org/occurrence/download/0003355-180730143533302 [4th August 2018] Lourteig, A. (1979). Oxalidaceae extra-austroamericanae: 2. Oxalis L. Sectio Corniculatae DC. <em>Phytologia</em>, 42, 57−198. https://www.biodiversitylibrary.org/part/38832