cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25405/data.ncl.15081795.v1
title: Polarity supervised classification of Twitter data posted in English related to the 10th anniversary of the 2010 Maule earthquake in Chile

description: This database contains the supervised classification of the tweets originally posted in English related to the 10th anniversary of the Maule Earthquake in Chile on Febrary 27, 2010. Tweets were collected by a third-party vendor between December 11, 2019 to April 23, 2020 including the hashtags: #27F, #Maule, #PorUnaLeyDeCostasEnChile, #Chile and #Terremoto. A total of 44,189 tweets were collected, from this sample, only 237 (0.54%) tweets were originally posted or retweeted in English, which can be explained by the fact that Chile is a Spanish speaking country. After removing the not related tweets the sample was reduced to 121 (0.27%). Despite being a small sample, based on additional analysis, we state that this is a representative sample of the opinions related to this commemoration. The high number of not related tweets is given that the hashtag #27F is also included in tweets related to celebrating the Independence of the Dominican Republic and the International Polar Bear Day; the birthday of José Vasconcelos (Mexican writer, philosopher, and politician), Elizabeth Taylor (American actress) and José Antonio Meade Kuribreña (Mexican politician). Other tweets that include the hashtag #27F are those commemorating the 'Caracazo' (protests, riots and looting that started on February 27 1989 in Caracas, Venezuela); the dead of Mauricio Garcés (Mexican actor) and Eyker Daniel Rojas (Venezuelan citizen assassinated during the Caracazo).
DOI: 10.25405/data.ncl.14687163.v1
title: Supervised polarity and topic classification of LastQuake app user’s pictures with comments – Zagreb 2020 earthquake

description: This database contains the sentiment analysis (SA) and topic supervised classification of the comments posted with pictures by LastQuake app users related to the 22nd March 2020 Zagreb earthquake. LastQuake app is a crowdsource-based earthquake information app that allows eyewitnesses to share information about the earthquakes that they felt, combined with seismic data. This app was developed by the European-Mediterranean Seismological Centre (EMSC). Attributes and data contained in the database are: <br>- eq_evid : Number of the earthquake the comment is associated with- eq_mag : magnitude of the event- eq_t0 : Origin time (UTC)- intensity: felt report intensity (as before leaving a comment users must leave a felt report)- epidist : distance from the event of the comment, in km- dt : response time from the origin time of the associated event, in seconds- rate_pos : number of positive rates *- rate_neg : number of negative rates* - device : device from which the comment was left (desktop, mobile ou app)- comm_valid : 0 or 1 depending on if we validated the comment or not. We invalidate comments when we consider them inappropriate (violence, insults,...)- language: Original language on which the comment was written- polarity: Polarity on which the comment is classified, i.e. positive, negative, neutral- topic: Building damages and intensity- comment: comment posted by LastQuake app user translated to English<br>LastQuake app obtained 31,911intensity reports from its users with comments, considered as text data, from which it has been possible to translate 31,403 (98%). The citizens included in their comments 361 pictures. After data processing, 314 (87%) pictures were selected for damage assessment. However, this database contains the classification of only those intensity reports that include pictures and comments: 45. This clarification is because some intensity reports from LasQuake app users include only comments or only pictures and some of them include both, and these are the intensity reports contained in this database. The supervised or unsupervised classification of the total number of comments posted by the LastQuake app users' with respect to the 22nd March 2020 Zagreb earthquake will be displayed in another database in the future.<br>
DOI: 10.25405/data.ncl.14604654
title: Sentiment analysis (supervised and unsupervised polarity classification) of Twitter data about the Albania 2019 earthquake

description: This database contains the comparison of results between the supervised and unsupervised polarity classification of the tweets related to the 2019 Albania earthquake. The database was constructed with the aim to test the accuracy of the algorithm developed by MonkeyLearn for polarity classification. 675 Tweets with the hashtags: #Albania #AlbanianEarthquake #albanianearthquake from the 26th November 2019 to the 3rd February 2020 were collected by the third-party vendor: TweetBinder. The social media department of Newcastle University provided us with 1001 tweets with the hashtags: #Albania collected from the 31st January to the 2nd February 2020. After removing repeated tweets from the database, we obtained a dataset made up of 255 original tweets. This database only contains text data of original tweets (no retweets). Attributes and data contained: -N: Number of the tweet -Tweet: Text data -ML-Classification: Unsupervised polarity classification performed by the algorithm developed by MonkeyLearn. -Confidence: Percentage of trust that the predicted polarity is right -RB-Classification: Supervised polarity classification performed by experts. -Accuracy: Coincidence indicated by 1 in polarity predicted by the unsupervised classification and the supervised classification. No coincidence is indicated by 0. -TPt: true positive -FPtNg: misclassified as Positive then False positive, when it is negative -FPtNt: misclassified as Positive then False positive , when it is Neutral -TNg: true negative -FNgPt: missclasified as Negative then False negative when it is Postive. -FNgNt: missclasified as Negative then False negative when it is Neutral. -TNt: true neutral -FNt Pt: missclasified as Neutral, then False-Neutral when it is Positive -FNtNg: and missclasified as Neutral, then False-Neutral, when it is negative. <br>
DOI: 10.25405/data.ncl.14604654.v3
title: Sentiment analysis (supervised and unsupervised polarity classification) of Twitter data about the Albania 2019 earthquake

description: This database contains the comparison of results between the supervised and unsupervised polarity classification of the tweets related to the 2019 Albania earthquake. The database was constructed with the aim to test the accuracy of the algorithm developed by MonkeyLearn for polarity classification. 675 Tweets with the hashtags: #Albania #AlbanianEarthquake #albanianearthquake from the 26th November 2019 to the 3rd February 2020 were collected by the third-party vendor: TweetBinder. The social media department of Newcastle University provided us with 1001 tweets with the hashtags: #Albania collected from the 31st January to the 2nd February 2020. After removing repeated tweets from the database, we obtained a dataset made up of 255 original tweets. This database only contains text data of original tweets (no retweets). Attributes and data contained: -N: Number of the tweet -Tweet: Text data -ML-Classification: Unsupervised polarity classification performed by the algorithm developed by MonkeyLearn. -Confidence: Percentage of trust that the predicted polarity is right -RB-Classification: Supervised polarity classification performed by experts. -Accuracy: Coincidence indicated by 1 in polarity predicted by the unsupervised classification and the supervised classification. No coincidence is indicated by 0. -TPt: true positive -FPtNg: misclassified as Positive then False positive, when it is negative -FPtNt: misclassified as Positive then False positive , when it is Neutral -TNg: true negative -FNgPt: missclasified as Negative then False negative when it is Postive. -FNgNt: missclasified as Negative then False negative when it is Neutral. -TNt: true neutral -FNt Pt: missclasified as Neutral, then False-Neutral when it is Positive -FNtNg: and missclasified as Neutral, then False-Neutral, when it is negative. <br>
DOI: 10.25405/data.ncl.14604654.v2
title: Supervised & unsupervised polarity classification of Twitter data related to the Albania 2019 earthquake

description: This database contains the comparison of results between the supervised and unsupervised polarity classification of the tweets related to the 2019 Albania earthquake. The database was constructed with the aim to test the accuracy of the algorithm developed by MonkeyLearn for polarity classification. 675 Tweets with the hashtags: #Albania #AlbanianEarthquake #albanianearthquake from the 26th November 2019 to the 3rd February 2020 were collected by the third-party vendor: TweetBinder. The social media department of Newcastle University provided us with 1001 tweets with the hashtags: #Albania collected from the 31st January to the 2nd February 2020. After removing repeated tweets from the database, we obtained a dataset made up of 255 original tweets. This database only contains text data of original tweets (no retweets). Attributes and data contained: -N: Number of the tweet -Tweet: Text data -ML-Classification: Unsupervised polarity classification performed by the algorithm developed by MonkeyLearn. -Confidence: Percentage of trust that the predicted polarity is right -RB-Classification: Supervised polarity classification performed by experts. -Accuracy: Coincidence indicated by 1 in polarity predicted by the unsupervised classification and the supervised classification. No coincidence is indicated by 0. -TPt: true positive -FPtNg: misclassified as Positive then False positive, when it is negative -FPtNt: misclassified as Positive then False positive , when it is Neutral -TNg: true negative -FNgPt: missclasified as Negative then False negative when it is Postive. -FNgNt: missclasified as Negative then False negative when it is Neutral. -TNt: true neutral -FNt Pt: missclasified as Neutral, then False-Neutral when it is Positive -FNtNg: and missclasified as Neutral, then False-Neutral, when it is negative. <br>
DOI: 10.25405/data.ncl.14604354
title: Sentiment (supervised polarity classification) and topic analysis of LastQuake app user's comments - Aegean 2020 earthquake

description: This database contains the sentiment analysis (SA) and topic supervised classification of the comments posted by LastQuake app users related to the 30th October 2020 Aegean earthquake. LastQuake app is a crowdsource-based earthquake information app that allows eyewitnesses to share information about the earthquakes that they felt, combined with seismic data. This app was developed by the European-Mediterranean Seismological Centre (EMSC). Attributes and data contained in the database are: § eq_evid : Number of the earthquake the comment is associated with § eq_mag : magnitude of the event § eq_t0 : Origin time (UTC) § intensity: felt report intensity (as before leaving a comment users must leave a felt report) § epidist : distance from the event of the comment, in km § dt : response time from the origin time of the associated event, in seconds § rate_pos : number of positive rates * § rate_neg : number of negative rates* § device : device from which the comment was left (desktop, mobile ou app) § comm_valid : 0 or 1 depending on if we validated the comment or not. We invalidate comments when we consider them inappropriate (violence, insults,...) § language: Original language on which the comment was written § polarity: Polarity on which the comment is classified, i.e. positive, negative, neutral § topic: Main topic addressed in the comment, i.e. building damages, tsunami effects, geotechnical effects, lifelines affected, emergency response, solidarity messages, preparedness, seismic information, intensity and unrealted. § comment: comment posted by LastQuake app user translated to English<br>
DOI: 10.25405/data.ncl.14604354.v3
title: Polarity and topic supervised classification of LastQuake app user's comments - Aegean 2020 earthquake

description: This database contains the sentiment analysis (SA) and topic supervised classification of the comments posted by LastQuake app users related to the 30th October 2020 Aegean earthquake. LastQuake app is a crowdsource-based earthquake information app that allows eyewitnesses to share information about the earthquakes that they felt, combined with seismic data. This app was developed by the European-Mediterranean Seismological Centre (EMSC). Attributes and data contained in the database are: § eq_evid : Number of the earthquake the comment is associated with § eq_mag : magnitude of the event § eq_t0 : Origin time (UTC) § intensity: felt report intensity (as before leaving a comment users must leave a felt report) § epidist : distance from the event of the comment, in km § dt : response time from the origin time of the associated event, in seconds § rate_pos : number of positive rates * § rate_neg : number of negative rates* § device : device from which the comment was left (desktop, mobile ou app) § comm_valid : 0 or 1 depending on if we validated the comment or not. We invalidate comments when we consider them inappropriate (violence, insults,...) § language: Original language on which the comment was written § polarity: Polarity on which the comment is classified, i.e. positive, negative, neutral § topic: Main topic addressed in the comment, i.e. building damages, tsunami effects, geotechnical effects, lifelines affected, emergency response, solidarity messages, preparedness, seismic information, intensity and unrealted. § comment: comment posted by LastQuake app user translated to English<br>
DOI: 10.25405/data.ncl.14579196
title: Sentiment analysis (supervised polarity classification) of Twitter data about the 10th anniversary of the L'Aquila 2009 earthquake

description: This database contain the supervised classification of the tweets related to the 10th anniversary of the earthquake in L'Aquila according to their polarity for the assesment of the post-disaster recovery process. Tweets were collected by the third-party vendor: TweetBinder in the period from April 4th to 10th 2019, with the hashtag:#L'Aquila.
DOI: 10.25405/data.ncl.14579196.v2
title: Sentiment analysis (supervised polarity classification) of Twitter data about the 10th anniversary of the L'Aquila 2009 earthquake

description: This database contain the supervised classification of the tweets related to the 10th anniversary of the earthquake in L'Aquila according to their polarity for the assesment of the post-disaster recovery process. Tweets were collected by the third-party vendor: TweetBinder in the period from April 4th to 10th 2019, with the hashtag:#L'Aquila.
DOI: 10.25405/data.ncl.14579196.v1
title: Polarity supervised classification of Twitter data related to the 10th anniversary of the L'Aquila 2009 earthquake

description: This database contain the supervised classification of the tweets related to the 10th anniversary of the earthquake in L'Aquila according to their polarity for the assesment of the post-disaster recovery process. Tweets were collected by the third-party vendor: TweetBinder in the period from April 4th to 10th 2019, with the hashtag:#L'Aquila.
DOI: 10.6084/m9.figshare.19535599.v1
title: LegislaciónServiciosCDR-OViso.pdf

description: Se recogen aquí elementos legislativos de interés en torno a los servicios y progrmas del CDR O Viso
DOI: 10.6084/m9.figshare.19535599
title: LegislaciónServiciosCDR-OViso.pdf

description: Se recogen aquí elementos legislativos de interés en torno a los servicios y progrmas del CDR O Viso
DOI: 10.5281/zenodo.6411789
title: Corpus of Early English Correspondence Extension Sampler part 1 (CEECES 1)

description: The <em>Corpus of Early English Correspondence Extension Sampler</em> part 1 (CEECES 1) is the first public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400). The CEECES 1 forms one part of the full CEECES. The other parts, both released 11 April 2022, are the CEECES part 2 and the TCEECES. The CEECES 1 was originally released in April 2021; this <strong>new version</strong> (11 April 2022) contains the <strong>exact same data</strong> (corpus files and supporting metadata), only the new zip file has been cleaned of macOS metafiles (__MACOSX and .DS_Store files), the manual has been updated, the metadata is now provided as a tsv file and a MS Excel file, and a key to the metadata has been included in the bundle. See the accompanying manual for more information on the CEECES 1; see the manuals for the CEECES 2 and the TCEECES for more information on the CEECES. See https://varieng.helsinki.fi/CoRD/corpora/CEEC/ for more on the CEEC-400. Citation: CEECES 1 = <em>Corpus of Early English Correspondence Extension Sampler</em> part 1. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2021. <strong>Version history:</strong> 1.4.2021 Version 1 – First release of corpus texts, metadata, and manual. 11.4.2022 Version 2 – Second release, to coincide with the release of CEECES 2 and TCEECES. Zip file cleaned, manual updated.
DOI: 10.6084/m9.figshare.19518133.v2
title: Tonnages, durations of ore formation, Sr/Y values and depths of formation of porphyry Cu-Au deposits

description: This table provides a summary of Cu and Au endowments of ~120 porphyry Cu-Au deposits, of the duration of the ore-forming processes where data are available, of the average Sr/Y values of associated magmatic rocks, and of the depth of formation of the porphyry Cu-Au deposits<br>
DOI: 10.5281/zenodo.5887101
title: Corpus of Early English Correspondence Extension Sampler part 2 (CEECES 2)

description: The <em>Corpus of Early English Correspondence Extension Sampler</em> part 2 (CEECES 2) is the second public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400). The CEECES 2 forms one part of the full CEECES. The other parts are the CEECES part 1 (released 1 April 2021), and the TCEECES (released 11 April 2022). See the accompanying manual for more information on the CEECES 2; see the manuals for the CEECES 1 and the TCEECES for more information on the CEECES. See https://varieng.helsinki.fi/CoRD/corpora/CEEC/ for more on the CEEC-400. Citation: CEECES 2 = <em>Corpus of Early English Correspondence Extension Sampler</em> part 2. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2022.
DOI: 10.5281/zenodo.5887231
title: Tagged Corpus of Early English Correspondence Extension Sampler (TCEECES)

description: The <em>Tagged Corpus of Early English Correspondence Extension Sampler</em> (TCEECES) is the third public release from the 18th-century part of the <em>Corpora of Early English Correspondence</em> (CEEC-400). The TCEECES forms one part of the full CEECES. The other parts are the CEECES part 1 (released 1 April 2021), and the CEECES part 2 (released 11 April 2022). The TCEECES is an extract from the full <em>Tagged Corpus of Early English Correspondence Extension</em> (TCEECE), which remains unpublished. See the accompanying manual for more information on the TCEECES; see the manuals for the CEECES 1 and the CEECES 2 for more information on the CEECES. See https://varieng.helsinki.fi/CoRD/corpora/CEEC/ for more on the CEEC-400. Citation: TCEECES = <em>Tagged Corpus of Early English Correspondence Extension Sampler</em>. Compiled by Terttu Nevalainen, Helena Raumolin-Brunberg, Samuli Kaislaniemi, Mikko Laitinen, Minna Nevala, Arja Nurmi, Minna Palander-Collin, Tanja Säily and Anni Sairio at the Department of Languages, University of Helsinki. Spelling standardised by Mikko Hakala, Minna Palander-Collin, Minna Nevala, Emanuela Costea, Anne Kingma and Anna-Lina Wallraff. Annotated by Lassi Saario and Tanja Säily. XML conversion and encoding by Lassi Saario. Helsinki: VARIENG, 2022.
DOI: 10.25502/81by-4t56/d
title: Genotypic data for maize Ex-PVP inbred lines and tropical inbred testers for diversity assessment and heterotic grouping

description: Optimizing use of U.S. Ex-PVP inbred lines for enhancing agronomic performance of tropical Striga resistant maize inbred lines
DOI: 10.6084/m9.figshare.19535632
title: Supplementary Material for: Potential of Thymus and Activation-Regulated Chemokine (TARC) as a Prognostic Biomarker of Food Protein-Induced Enterocolitis Syndrome (FPIES) Caused by Egg Yolk

description: <b><i>Background:</i></b> Several recent studies have reported egg yolk-associated food protein-induced enterocolitis syndrome (FPIES) in Japan. We previously reported the usefulness of post-emetic thymus and activation-regulated chemokine (TARC) levels for the diagnosis and evaluation of symptom severity in FPIES caused by solid foods including egg yolk. However, there are no studies on the usefulness of TARC as a prognostic biomarker. <b><i>Objective:</i></b> The aim of the study was to evaluate the post-emetic TARC levels, clinical symptoms, and post-index event results of the egg yolk oral food challenge test (OFC), and retrospectively investigate predictive factors of the subsequent OFC result. <b><i>Method:</i></b> This retrospective study included 12 patients with egg yolk FPIES. The following long-term management protocol for egg yolk FPIES was mandatory for study inclusion: Patients visited the emergency department, met the diagnostic criteria of FPIES, and underwent an egg yolk OFC 6–12 months after complete elimination of egg yolk. If the result of the OFC was positive, the patient underwent the OFC every year until it was negative. We analyzed a total of 20 episodes (12 department visits and eight positive OFCs). The blood test data, including post-emetic TARC level and symptom severity, were compared between the next-OFC-positive group and the next-OFC-negative group. In addition, tolerance development over follow-up was analyzed. <b><i>Results:</i></b> The median (range) ages of the next-OFC-positive and negative groups were 11 (6–33) and 10 (7–21) months, respectively. The median (range) serum TARC (pg/mL) level was 5,208 (2,009–8,147) in the next-OFC-positive group, which was significantly higher (<i>p</i> = 0.004) than that in the next-OFC-negative group, which was 1,803 (905–3,754). There were no significant differences in other hematological results. The next-OFC-positive group had greater severity compared to the next-OFC-negative group (<i>p</i> = 0.026). The remission rate was approximately 30% at 24 months and 80% at 36 months. <b><i>Conclusion:</i></b> Post-emetic TARC levels may predict the short-term prognosis of egg yolk FPIES after approximately 1 year and could be useful for the management of egg yolk FPIES.
DOI: 10.5281/zenodo.6421086
title: Catalonian routes tracking ranking from Wikiloc

description: This dataset contains the list of the most valued routes in Catalonia from wikiloc.
DOI: 10.5281/zenodo.6421085
title: Catalonian routes tracking ranking from Wikiloc

description: This dataset contains the list of the most valued routes in Catalonia from wikiloc.
DOI: 10.22003/xfel.eu-data-700000-00
title: Example Data