cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.23257256.v1
title: Supplementary information files for Attending sporting mega events during COVID-19: mitigation and messaging at UK EURO 2020 matches

description: Supplementary files for article Attending sporting mega events during COVID-19: mitigation and messaging at UK EURO 2020 matches The UEFA EURO 2020 football tournament was one of the largest Sporting Mega Events (SMEs) to take place during the COVID-19 pandemic. Mitigating the risk of virus transmission requires a multi-layered approach for any large event, more so in this case due to staging the tournament across eleven host countries. Yet, little is known about COVID-19 risks and mitigation from attending an event of this scale and nature. We examined the implementation of mitigation and messaging at EURO 2020 matches hosted at venues in the UK. The tournament was postponed from the summer of 2020 and played in June and July of 2021. Structured observations were conducted by 11 trained fieldwork-supporters at 10 matches played at Wembley Stadium, London, or Hampden Park, Glasgow. Fieldwork-supporters observed one-way systems and signage, and hand sanitizing stations inside the stadia, but reported significant variation in the implementation of staggered timeslots, testing upon entry, and procedures for exit. Adherence to planned measures by ticket holders and implementation by stewards waned as the tournament progressed culminating in an absence of enforced measures at the final. The non-compliance with COVID-19 mitigation measures was likely to have led to a significantly increased risk of transmission. Future events should consider how COVID-19 mitigation measures could become 'new norms' of fan behaviour, learning from what is already known about football fandom. Tournament organizers of SMEs can use these findings to promote clearer messaging on pandemic-driven changes in fan behaviour and best practices in mitigating risk at future sporting and cultural events. <br>
DOI: 10.5517/ccdc.csd.cc1kn209
title: CCDC 1449250: Experimental Crystal Structure Determination

description: Related Article: Runhai Chen, Guorui Qin, Shihui Li, Alison J. Edwards, Ross O. Piltz, Iker Del Rosal, Laurent Maron, Dongmei Cui, Jianhua Cheng|2020|Angew.Chem.,Int.Ed.|59|11250|doi:10.1002/anie.202002303
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22788422.v2
title: Supplementary Files

description: Supplementary information files for the flux stations and meteorological stations.
DOI: 10.6084/m9.figshare.23257478
title: [Supplementary_Files]_ironic_effects_of_motor_task_performance_A_Systematic_Review.pdf

description: Taylor and Francis pulishing organization published the content of the supplementary files on 11 April 2023 online as part of the article titled "A systematic review of ironic effects of motor task performance under pressure: The past 25 years". The article is open access ➡ https://doi.org/10.1080/1750984X.2023.2193966
DOI: 10.6084/m9.figshare.23257478.v1
title: [Supplementary_Files]_ironic_effects_of_motor_task_performance_A_Systematic_Review.pdf

description: Taylor and Francis pulishing organization published the content of the supplementary files on 11 April 2023 online as part of the article titled "A systematic review of ironic effects of motor task performance under pressure: The past 25 years". The article is open access ➡ https://doi.org/10.1080/1750984X.2023.2193966
DOI: 10.5517/ccdc.csd.cc28jv2r
title: CCDC 2101554: Experimental Crystal Structure Determination

description: Related Article: Anni Li, Shungang Jiao, Huiming Huang, Panlong Chen, Ruifei Zhang, Guozhu Su, Jixuan Xu, Changxin Liu, Zhongdong Hu, Suyile Chen, Pengfei Tu, Xingyun Chai, Luqi Huang|2022|Bioorg.Chem.|125|105879|doi:10.1016/j.bioorg.2022.105879
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cz99z
title: CCDC 2203861: Experimental Crystal Structure Determination

description: Related Article: Wen-Juan Wei, Hong-Qiang Gao, Ming Fang, Yun-Zhi Tang, Yen Wei|2023|CrystEngComm|25|1333|doi:10.1039/D2CE01419J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25345/c5nv99m83
title: MassIVE MSV000092048 - AAV LC-IMS-MS for Empty and Filled AAV2, AAV6, and AAV8
title: LC-IMS-MS analysis of empty and filled AAV2, AAV6, and AAV8.
DOI: 10.5281/zenodo.7817484
title: Umfrage zu prekären Arbeitsbedingungen im Wissenschaftssystem in Deutschland

description: Umfrage von RespectScience e.V. zu prekären Arbeitsbedingungen im deutschen Wissenschaftssystem. Die Umfrage wurde im April 2023 durchgeführt und in den Monaten danach schon teilweise ausgewertet. Die Ergebnisse der Auswertung können auf Nachfrage bei RespectScience e.V. (respect@science-future.de) eingesehen werden. Der Datensatz ist anonymisiert. Nachträglich haben wir uns dazu entschieden Textfeldantworten nicht hier zu veröffentlichen um Datenschutz und Anonymität zu garantieren; die entsprechenden Variablen sind gekennzeichnet (Siehe variables_respectscience.csv; "Comments"). Für die Auswertung des Datensatzes (Themenbezogene-Fragen und Auswertung der Kohorte), werden folgende Antworten nicht betrachtet:<br> 1. Keine Antworten (N = 7) des Pretests (filter(MODE != “pretest”))<br> 2. Keine Antworten (N = 5) von Teilnehmenden, die bei der Frage nach dem Alter 99, 12 oder 10 angegeben haben (filter(F010_01 &gt; 18 & F010_01 &lt; 90))<br> 3. Keine Antworten von Teilnehmenden (N = 48), bei denen die Beschäftigung im Akademischen System länger als 5 Jahre zurück liegt und die gleichzeitig (laut eigenen Angaben) in der Industrie oder bei “Sonstige” beschäftigt sind (filter(F003 != 1 |( F008 != 4 & F008 != 5))<br> 4. Keine Antworten (N = 78) von Teilnehmenden, die nicht in Deutschland-Beschäftigt sind (filter(F012 != 17))<br> 5. Keine Antworten (N = 2) von Schüler:innen (filter(F002 != 1)) <br> Insgesamt bleiben N = 2874 Interviews, die für die Auswertung des Datensatzes genutzt werden.<br> Insgesamt wurden 140 Interviews gefiltert.<br> Interviews bereinigt: N Auswertung = 2874<br> Interviews insgesamt angefangen: N Alle = 3969 (Rücklaufstatisktik soSci)<br> Interviews komplett abgeschlossen: N abgeschlossen = 3008 (Rücklaufstatistik soSci)<br> Drop Out Rate: 24.21 %
DOI: 10.5281/zenodo.7817483
title: Umfrage zu prekären Arbeitsbedingungen im Wissenschaftssystem in Deutschland

description: Umfrage von RespectScience e.V. zu prekären Arbeitsbedingungen im deutschen Wissenschaftssystem. Die Umfrage wurde im April 2023 durchgeführt und in den Monaten danach schon teilweise ausgewertet. Die Ergebnisse der Auswertung können auf Nachfrage bei RespectScience e.V. (respect@science-future.de) eingesehen werden. Der Datensatz ist anonymisiert. Nachträglich haben wir uns dazu entschieden Textfeldantworten nicht hier zu veröffentlichen um Datenschutz und Anonymität zu garantieren; die entsprechenden Variablen sind gekennzeichnet (Siehe variables_respectscience.csv; "Comments"). Für die Auswertung des Datensatzes (Themenbezogene-Fragen und Auswertung der Kohorte), werden folgende Antworten nicht betrachtet:<br> 1. Keine Antworten (N = 7) des Pretests (filter(MODE != “pretest”))<br> 2. Keine Antworten (N = 5) von Teilnehmenden, die bei der Frage nach dem Alter 99, 12 oder 10 angegeben haben (filter(F010_01 &gt; 18 & F010_01 &lt; 90))<br> 3. Keine Antworten von Teilnehmenden (N = 48), bei denen die Beschäftigung im Akademischen System länger als 5 Jahre zurück liegt und die gleichzeitig (laut eigenen Angaben) in der Industrie oder bei “Sonstige” beschäftigt sind (filter(F003 != 1 |( F008 != 4 & F008 != 5))<br> 4. Keine Antworten (N = 78) von Teilnehmenden, die nicht in Deutschland-Beschäftigt sind (filter(F012 != 17))<br> 5. Keine Antworten (N = 2) von Schüler:innen (filter(F002 != 1)) <br> Insgesamt bleiben N = 2874 Interviews, die für die Auswertung des Datensatzes genutzt werden.<br> Insgesamt wurden 140 Interviews gefiltert.<br> Interviews bereinigt: N Auswertung = 2874<br> Interviews insgesamt angefangen: N Alle = 3969 (Rücklaufstatisktik soSci)<br> Interviews komplett abgeschlossen: N abgeschlossen = 3008 (Rücklaufstatistik soSci)<br> Drop Out Rate: 24.21 %
DOI: 10.5281/zenodo.7982164
title: desmognathus_amphileucus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982163
title: desmognathus_amphileucus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982187
title: desmognathus_aureatus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982186
title: desmognathus_aureatus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982197
title: desmognathus_balsameus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982196
title: desmognathus_balsameus_spades_01.fasta.gz

description: .
DOI: 10.3886/e192022
title: Evaluation of Stamford Hospital’s Geriatric Emergency Medicine Initiative

description: The older adult population in the United States is projected to rise by at least 10% by 2030, creating a unique challenge for emergency departments (ED) nationwide. Older adults are more likely to be admitted to the general medical floors and the Intensive Care Unit and experience a higher in-hospital and ED mortality compared to younger populations. To accommodate the complex healthcare needs of the rising geriatric population, Stamford Hospital’s (SH) ED began a Geriatric Emergency Medicine Initiative (GEMI) in 2018 to improve our ED environment, practices, and focused assessments for our community-dwelling older adults. Novel processes, geriatric assessments and program metrics are described, as well as descriptive statistics on diagnoses and assessment data. Comparisons between GEMI and non-GEMI cohorts on readmission rates, time-based outcomes, throughput metrics, and other outcomes of interest were compared using chi-square tests and group t-tests. A p-value of 0.05 (p &lt;0.05) defined statistical significance. GEMI-assessed patients had significantly lower readmission rates and length of stay in the hospital and ED compared to the non-GEMI assessed cohort. GEMI patients were more likely to be admitted as observation patients but less likely to return within 72 hours and subsequently admitted to the hospital. GEMI patients commonly presented to the ED for falls, gastrointestinal related issues, and urinary tract infections. The top medications prescribed at discharge included Lipitor, Tylenol, and laxatives. Prior to 2018, SH’s ED did not have a solidified care pathway for geriatric patients and care teams acknowledged a need for change. These results are promising as one of the main goals of the program is to decrease hospital admissions of older adults and reduce the length of stay in the ED to prevent common adverse effects from hospitalization, such as delirium and infections. Our results are dependent on the successful implementation of a unit wide geriatric intervention as well as availability of trained geriatric nurses, optimizing system changes and innovating hospital resources for older adults.<b></b> <b> </b> Keywords: geriatric emergency department, quality improvement, emergency department innovations
description: Convenience Sampling
DOI: 10.3886/e192022v1
title: Evaluation of Stamford Hospital’s Geriatric Emergency Medicine Initiative

description: The older adult population in the United States is projected to rise by at least 10% by 2030, creating a unique challenge for emergency departments (ED) nationwide. Older adults are more likely to be admitted to the general medical floors and the Intensive Care Unit and experience a higher in-hospital and ED mortality compared to younger populations. To accommodate the complex healthcare needs of the rising geriatric population, Stamford Hospital’s (SH) ED began a Geriatric Emergency Medicine Initiative (GEMI) in 2018 to improve our ED environment, practices, and focused assessments for our community-dwelling older adults. Novel processes, geriatric assessments and program metrics are described, as well as descriptive statistics on diagnoses and assessment data. Comparisons between GEMI and non-GEMI cohorts on readmission rates, time-based outcomes, throughput metrics, and other outcomes of interest were compared using chi-square tests and group t-tests. A p-value of 0.05 (p &lt;0.05) defined statistical significance. GEMI-assessed patients had significantly lower readmission rates and length of stay in the hospital and ED compared to the non-GEMI assessed cohort. GEMI patients were more likely to be admitted as observation patients but less likely to return within 72 hours and subsequently admitted to the hospital. GEMI patients commonly presented to the ED for falls, gastrointestinal related issues, and urinary tract infections. The top medications prescribed at discharge included Lipitor, Tylenol, and laxatives. Prior to 2018, SH’s ED did not have a solidified care pathway for geriatric patients and care teams acknowledged a need for change. These results are promising as one of the main goals of the program is to decrease hospital admissions of older adults and reduce the length of stay in the ED to prevent common adverse effects from hospitalization, such as delirium and infections. Our results are dependent on the successful implementation of a unit wide geriatric intervention as well as availability of trained geriatric nurses, optimizing system changes and innovating hospital resources for older adults.<b></b> <b> </b> Keywords: geriatric emergency department, quality improvement, emergency department innovations
description: Convenience Sampling
DOI: 10.6084/m9.figshare.23256887
title: Extent of the acid soil in North East India (million hectares)

description: This table summarizes the area under soil acidity in different states of Northeast India
DOI: 10.6084/m9.figshare.23256887.v1
title: Extent of the acid soil in North East India (million hectares)

description: This table summarizes the area under soil acidity in different states of Northeast India
DOI: 10.5281/zenodo.7982205
title: desmognathus_cheaha_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982204
title: desmognathus_cheaha_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982211
title: desmognathus_gvnigeusgwotli_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982210
title: desmognathus_gvnigeusgwotli_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982225
title: desmognathus_intermedius_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982224
title: desmognathus_intermedius_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982239
title: desmognathus_kanawha_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982238
title: desmognathus_kanawha_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.6826567
title: Onco-mNGS Facilitates Rapid and Precise Identification of The Etiology of Fever of Unknown Origin: A Single-centre Prospective Study in North China

description: Supplemental Files and Data. Raw data for the copy numbers of chromosome were shown in fd.txt format. The picture of genomic of each sample was shown in png format.
DOI: 10.5281/zenodo.6826566
title: Onco-mNGS Facilitates Rapid and Precise Identification of The Etiology of Fever of Unknown Origin: A Single-centre Prospective Study in North China

description: Supplemental Files and Data. Raw data for the copy numbers of chromosome were shown in fd.txt format. The picture of genomic of each sample was shown in png format.
DOI: 10.5281/zenodo.7982217
title: Onco-mNGS Facilitates Rapid and Precise Identification of The Etiology of Fever of Unknown Origin: A Single-centre Prospective Study in North China

description: Supplemental Files and Data. Raw data for the copy numbers of chromosome were shown in fd.txt format. The picture of genomic of each sample was shown in png format.
DOI: 10.5281/zenodo.7966204
title: CyTOF test data for BinaryClust2

description: PBMC samples from 11 Myeloproliferative neoplasms(MPN) patients were subject to a 36-marker panel and aquired by mass cytometry for immune surveillance, which represent a test dataset for BinaryClust2 pipeline(https://github.com/JingAnyaSun/BinaryClust2). Enclosed are raw data including fcs files after clean-up, sample metadata('metadata2.xlsx') and panel metadata('panel_metadata2'), and the defined R objects which comprises panel.RData, md.RData and MPN_sce.RData.
DOI: 10.5281/zenodo.7966205
title: CyTOF test data for BinaryClust2

description: PBMC samples from 11 MPN patients were subject to a 36-marker panel and aquired by mass cytometry for immune surveillance, which represent a test dataset for BinaryClust2 pipeline(https://github.com/JingAnyaSun/BinaryClust2).
DOI: 10.5281/zenodo.7982165
title: CyTOF test data for BinaryClust2

description: PBMC samples from 11 Myeloproliferative neoplasms(MPN) patients were subject to a 36-marker panel and aquired by mass cytometry for immune surveillance, which represent a test dataset for BinaryClust2 pipeline(https://github.com/JingAnyaSun/BinaryClust2). Enclosed are raw data including fcs files after clean-up, sample metadata('metadata2.xlsx') and panel metadata('panel_metadata2'), and the defined R objects which comprises panel.RData, md.RData and MPN_sce.RData.
DOI: 10.5281/zenodo.7982245
title: desmognathus_mavrokoilius_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982244
title: desmognathus_mavrokoilius_spades_01.fasta.gz

description: .
DOI: 10.21227/23ww-y263
title: MFP (Mobile FingerPrint)

description: A short time ago, the study of contactless fingerprint authentication gained appeal among biometric researchers.Contactless fingerprint systems offer various advantages, such as ease of capture and affordability, over conventional fingerprint identification systems, which demand that the user's finger make direct contact with the sensor.The Mobile FingerPrint dataset (MFP dataset) contains thumb and index fingerprint images.The finger is placed in front of the mobile camera without any contact, and the camera should be placed in front of the finger at approximately 14 cm to capture the entire fingerprint modality. Images are taken without light conditions and in a free position.
DOI: 10.5281/zenodo.7962714
title: The image processing of Milani: challenges after DART impact

description: This dataset contain data for the paper The image processing of Milani challenges after DART impact, presented at ESA-GNC Sopot, Poland, June 2023 The .zip folder Dataset is made of 4 subfolders <br> -CoefficientsPCE With the PCEFull9.mat file with all the coefficients of the aPC basis and PCE listed in the paper<br> <br> -TestResults With the ResultsAll.mat file that contains the predicted and true values of phase angle over the test set illustrated in the paper and the MakePlot.m script in Matlab. You can use this data to compare your method directly with the results we have obtained in this work. The script generate a simple histogram plot and compute the mean, std, Q67, and Q95 values. -D1_with_sz_1p00s0ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by ESA (old values before impact). -D1_with_sz_0p78s0 ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by JHUAPL (updated values after impact). This directory also contains the pre-processed input-output pairs for the WCOB, NN, and PCE (X_features, Y) and CELM, and CNN (X_images, Y) methods. You can either decide to use the same pre-processed data we have used for training, validation, and testing or you can work with the raw data (the one from the ImagesDataset folder) to generate your own dataset. Feel free to drop a line vie email in case you need clarification at mattia.pugliatti@polimi.it or pugliatti.mattia@gmail.com
DOI: 10.5281/zenodo.7962713
title: The image processing of Milani: challenges after DART impact

description: This dataset contain data for the paper The image processing of Milani challenges after DART impact, presented at ESA-GNC Sopot, Poland, June 2023 The .zip folder Dataset is made of 4 subfolders <br> -CoefficientsPCE With the PCEFull9.mat file with all the coefficients of the aPC basis and PCE listed in the paper<br> <br> -TestResults With the ResultsAll.mat file that contains the predicted and true values of phase angle over the test set illustrated in the paper and the MakePlot.m script in Matlab. You can use this data to compare your method directly with the results we have obtained in this work. The script generate a simple histogram plot and compute the mean, std, Q67, and Q95 values. -D1_with_sz_1p00s0ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by ESA (old values before impact). -D1_with_sz_0p78s0 ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by JHUAPL (updated values after impact). This directory also contains the pre-processed input-output pairs for the WCOB, NN, and PCE (X_features, Y) and CELM, and CNN (X_images, Y) methods. You can either decide to use the same pre-processed data we have used for training, validation, and testing or you can work with the raw data (the one from the ImagesDataset folder) to generate your own dataset. Feel free to drop a line vie email in case you need clarification at mattia.pugliatti@polimi.it or pugliatti.mattia@gmail.com
DOI: 10.5281/zenodo.7982253
title: desmognathus_perlapsus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982252
title: desmognathus_perlapsus_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7982213
title: Test dataset(CyTOF) for BinaryClust2

description: Raw dataset are from Trussart et al. (Marie Trussart, Charis E Teh, Tania Tan, Lawrence Leong, Daniel HD Gray, Terence P Speed (2020) Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets eLife 9:e59630 https://doi.org/10.7554/eLife.59630), which was adopt here for testing the batch normalisation function of BinaryClust2.
DOI: 10.5281/zenodo.7982212
title: Test dataset(CyTOF) for BinaryClust2

description: Raw dataset are from Trussart et al. (Marie Trussart, Charis E Teh, Tania Tan, Lawrence Leong, Daniel HD Gray, Terence P Speed (2020) Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets eLife 9:e59630 https://doi.org/10.7554/eLife.59630), which was adopt here for testing the batch normalisation function of BinaryClust2.