cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7104479
title: Women in Limnology: from a historical perspective to a present-day evaluation

description: Data presented in the manuscript "Women in Limnology: from a historical perspective to a present-day evaluation", authored by the Gender and Science Group of the Iberian Association of Limnology and published in the journal <em>WIREs Water</em> in 2022. There is two datasets: <strong>Annex1:</strong> Table containing the name, bibliography and relevant contributions of 73 female limnologists. The selected limnologists are from different countries, periods, and disciplines; and were chosen because of their relevance in the field. <strong>Annex3:</strong> Representation of female limnologists as (1) presidents of academic sociaties (year 2021), (2) plenary speakers in conferences, (3) senor and junior awardees and (4) members of journal editorial boards. bbreviations for societies and conferences are: AFL, Association Françoise de Limnologie; AIL, Asociación Ibérica de Limnología; ASLO, Association for the Sciences of Limnology and Oceanography; DGL, German Limnological Society; EFFS, European Federation for Freshwater Sciences; PTH, Polish Hydrobiological Society; SEFS, Symposium for European freshwater Sciences; SFS, Society for Freshwater Sciences; SGHL, Swiss Hydrological & Limnology Society; SIL, International Society of Limnology; SIL‐Austria, Austrian Limnological Association; and TLS, Turkish Limnological Society
DOI: 10.5281/zenodo.6624064
title: How Scrum Adds Value to Achieving Software Quality?

description: 39 interviews with Scrum practitioners on the topic of how Scrum as a method contribute to achieving software quality. Data analysis documentation also available with the dataset. Two validation focus groups transcript also available with the data.
DOI: 10.5281/zenodo.6624063
title: How Scrum Adds Value to Achieving Software Quality?

description: 39 interviews with Scrum practitioners on the topic of how Scrum as a method contribute to achieving software quality. Data analysis documentation also available with the dataset. Two validation focus groups transcript also available with the data.
DOI: 10.57760/sciencedb.02810
title: Test-yzy-0922
title: Test-yzy-0922

description: Information such as equipment and tools; • Please specify the time, spatial information, time, spatial resolution and other information involved in the data set (if applicable); • Table data, please describe the number of records in the table data, the meaning of row labels and column labels in the data table, and the unit of measurement involved (if applicable); • If the data is missing, please clearly point out the missing data and explain it (if applicable); • Whether there are errors in the data, the error range or the reason for introducing errors, if necessary, please explain appropriately (if applicable); • The content of each data file and its description (if applicable); • If the format of the data file you submit is relatively small, please explain the applicable software, the official download address of the software and other information (if applicable). The above suggestions are for reference only. If there are any omissions, please supplement and improve your dataset introduction. Your dataset introduction should be organized and submitted in the form of one or more natural sections. Please do not reply to the above questions one by one in the form of question and answer.
description: Information such as equipment and tools; • Please specify the time, spatial information, time, spatial resolution and other information involved in the data set (if applicable); • Table data, please describe the number of records in the table data, the meaning of row labels and column labels in the data table, and the unit of measurement involved (if applicable); • If the data is missing, please clearly point out the missing data and explain it (if applicable); • Whether there are errors in the data, the error range or the reason for introducing errors, if necessary, please explain appropriately (if applicable); • The content of each data file and its description (if applicable); • If the format of the data file you submit is relatively small, please explain the applicable software, the official download address of the software and other information (if applicable). The above suggestions are for reference only. If there are any omissions, please supplement and improve your dataset introduction. Your dataset introduction should be organized and submitted in the form of one or more natural sections. Please do not reply to the above questions one by one in the form of question and answer.
DOI: 10.5281/zenodo.4627219
title: Semantically tagged Europarl-cs.v7

description: <strong>Semantically tagged Europarl-cz.v7 </strong> 14.9+ M lines Lexical coverage of the tagging: 83.90% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Czech, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Example output</strong> následný A N4<br> postup N X4.2<br> na R Z5<br> základ N A2.2 T2+ X4.2<br> usnesení N X6+ X9.2+<br> parlament# Z99<br> : PUNCT<br> viz V X3.4 X2.1 S1.1.1 X2.5+ X2.3+ X3 A7+ Z4 S3.2<br> zápis N M7 Q1.2 S7.3 M1 T2+<br> předložení N A9- A2.2 Q2.2 S1.1.3+ Q4.3 O4.1 K4<br> dokument N Q1.2 X2.2+ Y2<br> : PUNCT<br> viz V X3.4 X2.1 S1.1.1 X2.5+ X2.3+ X3 A7+ Z4 S3.2<br> zápis N M7 Q1.2 S7.3 M1 T2+<br> písemný A Q1.2
DOI: 10.5281/zenodo.4627218
title: Semantically tagged Europarl-cs.v7

description: <strong>Semantically tagged Europarl-cz.v7 </strong> 14.9+ M lines Lexical coverage of the tagging: 83.90% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Czech, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Example output</strong> následný A N4<br> postup N X4.2<br> na R Z5<br> základ N A2.2 T2+ X4.2<br> usnesení N X6+ X9.2+<br> parlament# Z99<br> : PUNCT<br> viz V X3.4 X2.1 S1.1.1 X2.5+ X2.3+ X3 A7+ Z4 S3.2<br> zápis N M7 Q1.2 S7.3 M1 T2+<br> předložení N A9- A2.2 Q2.2 S1.1.3+ Q4.3 O4.1 K4<br> dokument N Q1.2 X2.2+ Y2<br> : PUNCT<br> viz V X3.4 X2.1 S1.1.1 X2.5+ X2.3+ X3 A7+ Z4 S3.2<br> zápis N M7 Q1.2 S7.3 M1 T2+<br> písemný A Q1.2
DOI: 10.5281/zenodo.4627154
title: Semantically tagged Europarl-pt.v7

description: <strong>Semantically tagged Europarl-pt.v7 </strong> 55.9+ M lines Lexical coverage of the tagging: 76.97% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Portuguese, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Example output</strong> reinício# Z99<br> de+a# Z99<br> sessão noun T1.3 T1.3/G1.1 T1.3/G2.1 T1.3/K1<br> declarar# Z99<br> reaberto# Z99<br> o pron Z8 Z8f Z8m Z8mf Z8mfn<br> sessão noun T1.3 T1.3/G1.1 T1.3/G2.1 T1.3/K1<br> de+o# Z99<br> parlamento noun G1.1<br> europeu noun Z2/S2mf Z3<br> , PUNCT<br> que conj A13.3 A6.1+ Z5 Z8<br> ter# Z99<br> ser verb A3+ Z5<br> interromper verb H4 T1.3+<br> em+a# Z99
DOI: 10.5281/zenodo.4627153
title: Semantically tagged Europarl-pt.v7

description: <strong>Semantically tagged Europarl-pt.v7 </strong> 55.9+ M lines Lexical coverage of the tagging: 76.97% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Portuguese, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Example output</strong> reinício# Z99<br> de+a# Z99<br> sessão noun T1.3 T1.3/G1.1 T1.3/G2.1 T1.3/K1<br> declarar# Z99<br> reaberto# Z99<br> o pron Z8 Z8f Z8m Z8mf Z8mfn<br> sessão noun T1.3 T1.3/G1.1 T1.3/G2.1 T1.3/K1<br> de+o# Z99<br> parlamento noun G1.1<br> europeu noun Z2/S2mf Z3<br> , PUNCT<br> que conj A13.3 A6.1+ Z5 Z8<br> ter# Z99<br> ser verb A3+ Z5<br> interromper verb H4 T1.3+<br> em+a# Z99
DOI: 10.5281/zenodo.4627102
title: Semantically tagged Europarl-es.v7

description: <strong>Semantically tagged Europarl-es.v7 </strong> 55.8+ M lines Lexical coverage of the tagging: 85.54% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Spanish, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Example output</strong> reanudación# Z99<br> del prep+art Z5<br> período noun T1.1 T1.3<br> de prep Z5<br> sesión# Z99<br> declarar# Z99<br> reanudar# Z99<br> el art Z5<br> período noun T1.1 T1.3<br> de prep Z5<br> sesión# Z99<br> del prep+art Z5<br> parlamento noun G1.2<br> europeo adj Z2 Z2/S2mfnc
DOI: 10.5281/zenodo.4627101
title: Semantically tagged Europarl-es.v7

description: <strong>Semantically tagged Europarl-es.v7 </strong> 55.8+ M lines Lexical coverage of the tagging: 85.54% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Spanish, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Format: base form POS Semtag</strong> Unknown words marked with tag Z99 <strong>Example output</strong> reanudación# Z99<br> del prep+art Z5<br> período noun T1.1 T1.3<br> de prep Z5<br> sesión# Z99<br> declarar# Z99<br> reanudar# Z99<br> el art Z5<br> período noun T1.1 T1.3<br> de prep Z5<br> sesión# Z99<br> del prep+art Z5<br> parlamento noun G1.2<br> europeo adj Z2 Z2/S2mfnc
DOI: 10.5281/zenodo.4620662
title: Semantically tagged Europarl-it.v7

description: <strong>Semantically tagged Europarl-it.v7 </strong> 54.2+ M lines Lexical coverage of the tagging: 94.06% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Italian, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Format:</strong> <em>base form POS Semtag</em> Unknown words marked with tag Z99 <strong>Example output</strong> ripresa noun I1.1+<br> del art Z5<br> sessione noun Q3 T1.3<br> dichiarare verb Q2.2<br> riprendere verb M2<br> IL noun Z5<br> sessione noun Q3 T1.3<br> del art Z5<br> parlamento noun G2.1<br> europeo adj Z2<br> PON PUNCT<br> interrompere verb T2-<br> Venerdì abr T1.3<br> 17 NUMB
DOI: 10.5281/zenodo.4620661
title: Semantically tagged Europarl-it.v7

description: <strong>Semantically tagged Europarl-it.v7 </strong> 54.2+ M lines Lexical coverage of the tagging: 94.06% No semantic ambiguity resolving, all the tags marked POS tagging for semantic tagging performed with Treetagger: https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/ Output in base form Documentation of the semantic tagger (for Finnish, but same principles hold for Italian, too): https://www.aclweb.org/anthology/W19-0306/ https://zenodo.org/record/3676372#.YFNwIa8zY2w <strong>Semantic tagging</strong> Tagging of the data was performed in Puhti computing environment of the CSC – IT CENTER FOR SCIENCE LTD. https://research.csc.fi/-/puhti <strong>Format:</strong> <em>base form POS Semtag</em> Unknown words marked with tag Z99 <strong>Example output</strong> ripresa noun I1.1+<br> del art Z5<br> sessione noun Q3 T1.3<br> dichiarare verb Q2.2<br> riprendere verb M2<br> IL noun Z5<br> sessione noun Q3 T1.3<br> del art Z5<br> parlamento noun G2.1<br> europeo adj Z2<br> PON PUNCT<br> interrompere verb T2-<br> Venerdì abr T1.3<br> 17 NUMB
DOI: 10.6084/m9.figshare.21111207
title: Raman spectroscopy as a novel technique for the identification of pathogens in a clinical microbiology laboratory

description: Raman spectroscopy is a vibrational spectroscopic technique with which the molecular fingerprint of a microorganism can be elucidated in 1 hr or less. In our study, the utility of this method as a rapid technique for the detection of different pathogens in a clinical microbiology laboratory was tested based on their unique spectra. The reference strains from American Type Culture Collection for frequently encountered pathogens such as <i>Staphylococcus aureus, Escherichia coli, Acinetobacter baumannii</i>, and <i>Pseudomonas aeruginosa</i> were used to capture the signature spectra of these pathogenic species using confocal Raman microscopy. Furthermore, a representative reference library was constructed using the spectra of these standard strains as well as the clinical isolates. The study revealed unique spectral outputs for each bacterial species studied. Importantly, the entire procedure could be performed in an hour. With the development of an extensive database, the spectra of unknown samples can be matched with those in the library, and thus, a new identification system can be developed.
DOI: 10.6084/m9.figshare.21111207.v1
title: Raman spectroscopy as a novel technique for the identification of pathogens in a clinical microbiology laboratory

description: Raman spectroscopy is a vibrational spectroscopic technique with which the molecular fingerprint of a microorganism can be elucidated in 1 hr or less. In our study, the utility of this method as a rapid technique for the detection of different pathogens in a clinical microbiology laboratory was tested based on their unique spectra. The reference strains from American Type Culture Collection for frequently encountered pathogens such as <i>Staphylococcus aureus, Escherichia coli, Acinetobacter baumannii</i>, and <i>Pseudomonas aeruginosa</i> were used to capture the signature spectra of these pathogenic species using confocal Raman microscopy. Furthermore, a representative reference library was constructed using the spectra of these standard strains as well as the clinical isolates. The study revealed unique spectral outputs for each bacterial species studied. Importantly, the entire procedure could be performed in an hour. With the development of an extensive database, the spectra of unknown samples can be matched with those in the library, and thus, a new identification system can be developed.
DOI: 10.25550/1w-wajj
title: Single-cell transcriptome profiling of the branching epithelium of mouse salivary glands

description: To explore the molecular signatures of subtypes among epithelial cells of mouse embryonic submandibular salivary glands undergoing branching morphogenesis, we performed single-cell RNA-sequencing and profiled near 7,000 single-cell transcriptomes. These cells formed 7 main clusters that corresponded well to cells at distinct locations, based on known expression patterns of selective marker genes. This study revealed many additional genes with differential expressions among clusters or subtypes of epithelial cells. Those can be good candidates underlying the regulation of their distinct biophysical properties.
DOI: 10.5281/zenodo.7104640
title: Datset related to article "AOPEP variants as a novel cause of recessive dystonia: Generalized dystonia and dystonia-parkinsonism"

description: Sanger sequences (.abi files) validating variants found with whole exome sequencing
DOI: 10.5281/zenodo.7104639
title: Datset related to article "AOPEP variants as a novel cause of recessive dystonia: Generalized dystonia and dystonia-parkinsonism"

description: Sanger sequences (.abi files) validating variants found with whole exome sequencing
DOI: 10.6084/m9.figshare.14865517
title: Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia

description: The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P) and REPTree (RF-REPTree), with three statistical models, i.e. weight of evidence, logistic regression and combination of WOE and LR. To predict the groundwater potential, nine groundwater potential conditioning parameters have been created. The Information Gain Ratio has been used to evaluate the impact of each parameter. The ROC curve has been used to validate the models. According to the findings, 15 to 30% of the study area has a very high or high groundwater potentiality. Furthermore, validation results revealed that RF based ensembles models outperformed other standalone models for groundwater potential modelling.
DOI: 10.6084/m9.figshare.14865517.v1
title: Integration of statistical models and ensemble machine learning algorithms (MLAs) for developing the novel hybrid groundwater potentiality models: a case study of semi-arid watershed in Saudi Arabia

description: The present study has proposed three novel hybrid models by integrating three traditional ensemble models, such as random forest, logitboost, and naive bayes, and six newly developed ensemble models of rotation forest (RF), such as decision tree (RF-DT), J48 (DF-J48), naive bayes tree (RF-NBT), neural network (RF-NN), M5P (RF-M5P) and REPTree (RF-REPTree), with three statistical models, i.e. weight of evidence, logistic regression and combination of WOE and LR. To predict the groundwater potential, nine groundwater potential conditioning parameters have been created. The Information Gain Ratio has been used to evaluate the impact of each parameter. The ROC curve has been used to validate the models. According to the findings, 15 to 30% of the study area has a very high or high groundwater potentiality. Furthermore, validation results revealed that RF based ensembles models outperformed other standalone models for groundwater potential modelling.
DOI: 10.7910/dvn/rzzwjy
title: Replication Data for: Storytelling Is a Way for Qamar Zaman, Born with a Disability, to Innovate!

description: Storytelling Is a Way for Qamar Zaman, Born with a Disability, to Innovate! Qamar Zaman has always been drawn to stories. As a child, he loved listening to his grandmother's tales from her homeland of Pakistan. When he was old enough, he began writing his own stories. Living through a birth injury, a vehicle accident and a Natural Disaster have given him an underdog mentality that has made him one of the most successful technology entrepreneurs. Now, Zaman is on a new mission: to help other people with disabilities through technology. Zaman has created a storytelling platform that helps with disabilities with their stories of struggle. Zaman announced that we will provide a $1,000 value scholarship for those who qualify our criteria to tell their stories using our ecosystem. This will help those who are less fortunate to get media attention and help raise funding and brand visibility. Zaman's company, Zaman Media, is a social enterprise that uses technology to help people with disabilities. Zaman Media has developed a web-based app called KISS PR Brand Story that helps people with disabilities get found. The app helps with SEO and branding for people with disabilities. KISS PR Brand Story is an online platform that helps people with disabilities get found by the media, investors and customers. The app provides tools to help with SEO and branding. About Qamar Zaman Zaman ventured from software development in the Guernsey Channel Islands to digital marketing in the Cayman Islands in 1995. ​ He initially worked with Cayman’s banking industry technology, the department of Cayman Islands Customs and Immigration, The Cayman Islands Department of Economics and Statistics, and various businesses. Zaman was also an adjunct Associate Professor for The International College of the Cayman Islands directly appointed by the Founder Dr. Elsa Cummings for his excellent achievements in the field of Economics, Risk, and IT. ​After several years of completing projects, Zaman decided to start his own business and build his own brand. ​After this first diffident step, things started speeding up, and Zaman’s digital disruption company was launched in Grand Cayman of the Cayman Islands in 2003. ​On September 12, 2004, Hurricane Ivan, a Category 5 deadly hurricane descended on the Cayman Islands and within hours, the good life of the Cayman Islands was a trampled dream of yesterday and Zaman eventually relocated to Dallas, Texas. ​ As a natural storyteller, he relived the harrowing tales and discovered his greatest gift - storytelling. Thus began KISS PR Brand Story, a revolutionary platform for storytelling. For over 18 years, Zaman focused on offering cost-effective storytelling services for social media and public relations. KISS PR has become the preferred choice of global small businesses. With over 60k+ stories already told.
DOI: 10.15139/s3/bft0xq
title: Vision Zero Action Plan: Safer Streets for El Monte (2022). El Monte, California

description: Vision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context.