cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4943481
title: Supplementary material to "Food and habitats requirements of the Scops Owl (Otus scops) in Switzerland revealed by very high-resolution multi-scale models"

description: <strong>Abstract</strong> In Europe, agricultural practices have progressively evolved towards high productivity leading either to the intensification of productive and accessible areas or to the abandonment of less profitable sites. Both processes have led to the degradation of semi-natural habitats like extensive grasslands, threatening species such as the Eurasian Scops Owl <em>Otus scops</em> that rely on extensively managed agricultural landscapes. In this work, we aimed to assess the habitat preferences of the Scops Owl using habitat suitability models combined with a multi-scale approach. We generated a set of multi-scale predictors, considering both biotic and abiotic variables, built on two newly developed vegetation management and orthopteran abundance models. To select the variables to incorporate in a ‘best multi-scale model’, we chose the best spatial scale for each variable using univariate models and by calculating their relative importance through multi-model inference. Next, we built ensembles of small models (ESMs) at 10 different scales from 50 to 1000 m, and an additional model with each variable at its best scale (‘best multi-scale model’). The latter performed better than most of the other ESMs and allowed the creation of a high-resolution habitat suitability map for the species. Scops Owls showed a preference for dry sites with extensive and well-structured habitats with 30–40% bush cover, and relied strongly on semi-extensive grasslands covering at least 30% of the surface within 300 m of the territory centre and with high orthopteran availability near the centre (50-m radius), revealing a need for good foraging grounds near the nest. At a larger spatial scale within a radius of 1000 m, the habitat suitability of Scops Owls was negatively related to forest cover. The resulting ESM predictions provide valuable tools for conservation planning, highlighting sites in need of particular conservation efforts together with offering estimates of the percentage of habitat types and necessary prey abundance that could be used as targets in future management plans to ensure the persistence of the population.
DOI: 10.15468/6vsd72
title: The first Nocticola Bolivar 1892 (Blattodea: Nocticolidae) from New Guinea

description: This dataset contains the digitized treatments in Plazi based on the original journal article Lucañas, Cristian C., Bláha, Martin, Rahmadi, Cahyo, Patoka, Jiří (2021): The first Nocticola Bolivar 1892 (Blattodea: Nocticolidae) from New Guinea. Zootaxa 5082 (3): 294-300, DOI: https://doi.org/10.11646/zootaxa.5082.3.7
DOI: 10.5281/zenodo.5788222
title: Preventing technical debt with the TAP framework for Technical Debt Aware Management

description: <br> This zip file contains twelve files and one folder to replicate and verify the study evaluating the TAP Framework (for Technical debt Aware Project management). * TDframework_ticket_rawdata_revised_anonymised.xlsx: <br> revised and anonymized raw data for tickets statistic containing two sheets for TD ticket and maintenance ticket * TDframework_survey_questions_and_SPSSvariable_definition.xlsx: <br> excel sheet with all survey questions in German and English language, SPSS variable names, and short names<br> <br> <br> * TDframework_survey_results.sav:<br> survey results already in SPSS format, variables with suffix "D" are dichotomized versions of the variables as explained in the paper. * TDframework_survey_results.xslx:<br> Excel-Export of TDframework_survey_results.sav<br> <br> <br> * MannWhitneyUTest.spv:<br> results of the Mann-Whitney U-Test, which show the significances of the hypothesis mentioned in the paper (Section 5.2.2)<br> <br> * MannWhitneyUTest.xslx:<br> Excel-Export of MannWhitneyUTest.sav<br> <br> * Correlations.spv<br> results of the Chi-square test for correlations and the significances mentioned in the paper (Section 5.2.3)<br> <br> * Correlations.xslx:<br> Excel-Export of Correlations.sav<br> <br> <br> * Management-Survey_2021_en.pdf:<br> - original survey with englisch translation of the questions<br> <br> * Management-Survey_2021_XXX.pdf:<br> results of the management survey from <br> - team manager of the observed unit (TMO), participant A<br> - team manager of the comparison unit (TMC), participant B<br> - unit manager of the observed unit (UMO), participant C<br> - unit manager of the comparison unit (UMC), participant C<br> UMC =&gt; no survey, but email conversation between author and UMC to get the answers for the comparison unit<br> <br> * Fig6_Cause-Effect-Diagram_arrow_details.xslx<br> details about sources for the arrows in this figure<br> <br> * folder "Case Study Protocol"<br> contains the case study protocol according to Runeson's guidelines.<br>
DOI: 10.5281/zenodo.5502649
title: Preventing technical debt with the TAP framework for Technical Debt Aware Management

description: <br> This zip file contains twelve files and one folder to replicate and verify the study evaluating the TAP Framework (for Technical debt Aware Project management). * TDframework_ticket_rawdata_revised_anonymised.xlsx: <br> revised and anonymized raw data for tickets statistic containing two sheets for TD ticket and maintenance ticket * TDframework_survey_questions_and_SPSSvariable_definition.xlsx: <br> excel sheet with all survey questions in German and English language, SPSS variable names, and short names<br> <br> <br> * TDframework_survey_results.sav:<br> survey results already in SPSS format, variables with suffix "D" are dichotomized versions of the variables as explained in the paper. * TDframework_survey_results.xslx:<br> Excel-Export of TDframework_survey_results.sav<br> <br> <br> * MannWhitneyUTest.spv:<br> results of the Mann-Whitney U-Test, which show the significances of the hypothesis mentioned in the paper (Section 5.2.2)<br> <br> * MannWhitneyUTest.xslx:<br> Excel-Export of MannWhitneyUTest.sav<br> <br> * Correlations.spv<br> results of the Chi-square test for correlations and the significances mentioned in the paper (Section 5.2.3)<br> <br> * Correlations.xslx:<br> Excel-Export of Correlations.sav<br> <br> <br> * Management-Survey_2021_en.pdf:<br> - original survey with englisch translation of the questions<br> <br> * Management-Survey_2021_XXX.pdf:<br> results of the management survey from <br> - team manager of the observed unit (TMO), participant A<br> - team manager of the comparison unit (TMC), participant B<br> - unit manager of the observed unit (UMO), participant C<br> - unit manager of the comparison unit (UMC), participant C<br> UMC =&gt; no survey, but email conversation between author and UMC to get the answers for the comparison unit<br> <br> * Fig6_Cause-Effect-Diagram_arrow_details.xslx<br> details about sources for the arrows in this figure<br> <br> * folder "Case Study Protocol"<br> contains the case study protocol according to Runeson's guidelines.<br>
DOI: 10.15151/esrf-es-642557235
title: XAS on Ce hexaboride

description: XAS on Ce hexaboride
DOI: 10.5281/zenodo.5788353
title: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis during the COVID-19 pandemic (01.2020-06.2020)

description: <strong>Sources: </strong> Euractiv The Conversation Politico Europe IEEE Spectrum Techforge Fastcompany The Guardian (Tech) Arstechnica Reuters Gizmodo ZDNet The Register The Verge TechCrunch <strong>Methodology</strong> The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
DOI: 10.5281/zenodo.5788352
title: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis during the COVID-19 pandemic (01.2020-06.2020)

description: <strong>Sources: </strong> Euractiv The Conversation Politico Europe IEEE Spectrum Techforge Fastcompany The Guardian (Tech) Arstechnica Reuters Gizmodo ZDNet The Register The Verge TechCrunch <strong>Methodology</strong> The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
DOI: 10.5281/zenodo.3243961
title: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2019)

description: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis <strong>Sources with weights:</strong> Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% <strong>Methodology</strong> The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
DOI: 10.25934/pr00007462
title: Associations of Amyloid, Tau, and Neurodegeneration Biomarker Profiles with Clinical Profiles in the Progression of Alzheimer’s Disease

description: • The project background：
   With the arrival of the global aging society, there is a new case of dementia every 3 seconds in the world. Alzheimer's disease (AD) is the most common form of dementia, accounting for 60-70%. Currently, we are facing the huge social and economic pressure brought by AD. Unfortunately, there are no drugs known to effectively treat Alzheimer's disease.  

• Necessity of the research;
   Recent two years, two new drugs probed by United States Food and Drug Administration and China National Medical Products Administration, as well as some ongoing clinical trials suggest that these drugs maybe delay the progression of AD when used in the early stage of AD. However, the early stage of AD not easy to identified. In addition, some previous studies showed that different subgroups in the early stage of AD maybe exist different cognition decline pattern.

• How the research will add to medical science or patient care; 
  This research will help to optimize the design of clinical trials in the future.
DOI: 10.5281/zenodo.3243960
title: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2019)

description: Sentiment analysis of tech media articles using VADER package and co-occurrence analysis <strong>Sources with weights:</strong> Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% <strong>Methodology</strong> The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
DOI: 10.25934/pr00005794.7
title: Available IPD datapackage for study 'A Phase III, Open-Label, Multicenter, Randomized Study to Investigate the Efficacy and Safety of Atezolizumab (Anti-PD-L1 Antibody) Compared With Docetaxel in Patients With Non-Small Cell Lung Cancer After Failure With Platinum Containing Chemotherapy'

description: This global, multicenter, open-label, randomized, controlled study evaluated the efficacy and safety of atezolizumab (an anti-programmed death−ligand 1 [anti-PD-L1] antibody)compared with docetaxel in participants with locally advanced or metastatic non-small cell lung cancer (NSCLC) after failure with platinum-containing chemotherapy. Participants were randomized 1:1 to receive either docetaxel or atezolizumab. Treatment may continue as long as participants experienced clinical benefit as assessed by the investigator, i.e., in the absence of unacceptable toxicity or symptomatic deterioration attributed to disease progression.
DOI: 10.5281/zenodo.3942104
title: Co-occurrences of trending keywords in popular tech media (01.2016-12.2019)

description: Sources with weights Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues and technologies have been selected (e.g. 'gdpr', '5G') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.3942103
title: Co-occurrences of trending keywords in popular tech media (01.2016-12.2019)

description: Sources with weights Euractiv 5% The Conversation 5% Politico Europe 5 % IEEE Spectrum 5 % Techforge 5% Fastcompany 5% The Guardian (Tech) 12% Arstechnica 5% Reuters 5% Gizmodo 9% ZDNet 9% The Register 12% The Verge 9% TechCrunch 9% Methodology Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues and technologies have been selected (e.g. 'gdpr', '5G') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.5237472
title: X-ray diffraction images of juvenile hormone diol kinase from the silk worm Bombyx mori

description: X-ray diffraction images of juvenile hormone diol kinase (JHDK) from the silk worm Bombyx mori
DOI: 10.5281/zenodo.5237471
title: X-ray diffraction images of juvenile hormone diol kinase from the silk worm Bombyx mori

description: X-ray diffraction images of juvenile hormone diol kinase (JHDK) from the silk worm Bombyx mori