cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14554137.v1
title: UnitelmaSapienza_1_0.zip

description: <b>Dataset UnitelmaSapienza - Version 1.0 (May 2021)</b><br><br><b>Description: </b>this is the dataset from the article "Hidden Space Deep Sequential Risk Prediction on Student Trajectories" (under review)<br><b>Disclaimer:</b>Due to privacy protection regulations, all data are aggregated and fully anonymized.<b><br></b><b>Dataset Description:</b><br>The zip contains 13 folders, each folder contains the timeseries (timeseries.csv) of a specific learning course from the UnitelmaSapienza University of Rome.<br>Each row, of a timeseries.csv file, corresponds to a student. Each column is a two dimensional numpy array, which corresponds to the history of 365 days of interactions of the student with the platform.The last column corresponds to the probability of drop-out (see the paper for more information on how this probability is thresholded to obtain a binary ground truth).<br>This dataset can be directly used as input for the code we released at http://iim.di.uniroma1.it/projects/hsdsrpst/<br><br><b>Authors:</b><br>Bardh Prenkaj - Sapienza University of Rome, ItalyDamiano Distante - University of Rome Unitelma Sapienza, ItalyStefano Faralli - University of Rome Unitelma Sapienza, ItalyPaola Velardi - Sapienza University of Rome, Italy<br><br><b>License:</b> Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/deed.en<br><b>Contacts</b>:For any question regarding the dataset and the source code please contact:Bardh Prenkaj prenkaj@di.uniroma1.it<br>
DOI: 10.6084/m9.figshare.14554341
title: COVID-19 testing and reporting template

description: The data describes how the information from the COVID-19 samples are entered into the Excel sheets and has formulas that generate plate maps and generates either positive or negative depending on the cut off Ct value.<br>
DOI: 10.5281/zenodo.4742139
title: Dataset - Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model

description: This is the dataset we used in our paper entitled "Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model". The manually validated dataset contains various several static source code metrics along with vulnerability fixing hashes for numerous vulnerabilities. For more details, you can read the paper here. Security has become a central and unavoidable aspect of today’s software development. Practitioners and researchers have proposed many code analysis tools and techniques to mitigate security risks. These tools apply static and dynamic analysis or, more recently, machine learning. Machine learning models can achieve impressive results in finding and forecasting possible security issues in programs. However, there are at least two areas where most of the current approaches fall short of developer demands: explainability and granularity of predictions. In this paper, we propose a novel and simple yet, promising approach to identify potentially vulnerable source code in JavaScript programs. The model improves the state-of-the-art in terms of explainability and prediction granularity as it gives results at the level of individual source code lines, which is fine-grained enough for developers to take immediate actions. Additionally, the model explains each predicted line (i.e., provides the most similar vulnerable line from the training set) using a prototype-based approach. In a study of 186 real-world and confirmed JavaScript vulnerability fixes of 91 projects, the approach could flag 60% of the known vulnerable lines on average by marking only 10% of the code-base, but in certain cases the model identified 100% of the vulnerable code lines while flagging only 8.72% of the code-base. If you wish to use our dataset, please cite this dataset, or the corresponding paper: <pre><code>@inproceedings{mosolygo2021towards, title={Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model}, author={Mosolyg{\'o}, Bal{\'a}zs and V{\'a}ndor, Norbert and Antal, G{\'a}bor and Heged{\H{u}}s, P{\'e}ter and Ferenc, Rudolf}, booktitle={2021 International Conference on Code Quality (ICCQ)}, pages={15--25}, year={2021}, organization={IEEE} }</code></pre>
DOI: 10.6084/m9.figshare.14554137.v2
title: UnitelmaSapienza_1_0.zip

description: <b>Dataset UnitelmaSapienza - Version 1.0 (May 2021)</b><br><b><br></b><b>Description: </b>This is the dataset from the article "Hidden Space Deep Sequential Risk Prediction on Student Trajectories" by Bardh Prenkaj, Damiano Distante, Stefano Faralli and Paola Velardi (under review)<b><br></b><b><br></b><b>Disclaimer: </b>Due to privacy protection regulations, all data are aggregated and fully anonymized.<b><br></b><b><br></b><b>Dataset Description:</b><b><br></b>The zip contains 13 folders, each folder contains the timeseries (timeseries.csv) of a specific learning course from the University of Rome Unitelma Sapienza.<br>Each row of a timeseries.csv file corresponds to a student. Each column is a two dimensional numpy array, which corresponds to 365 days of interactions of the student with the e-learning platform.The last column corresponds to the probability of drop-out (see the above indicated paper for more information on how this probability is thresholded to obtain a binary ground truth).<br>This dataset can be directly used as input for the code we released at http://iim.di.uniroma1.it/projects/hsdsrpst/<br>When using this dataset in your work please cite the paper it is associated to.<br><br><b>Authors:</b>Bardh Prenkaj - Sapienza University of Rome, ItalyDamiano Distante - University of Rome Unitelma Sapienza, ItalyStefano Faralli - University of Rome Unitelma Sapienza, ItalyPaola Velardi - Sapienza University of Rome, Italy<br><br><b>License:</b>Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/deed.en<br>For any question regarding the dataset and the source code please contact:Bardh Prenkaj (prenkaj@di.uniroma1.it)<br>
DOI: 10.25345/c50z4p
title: MassIVE MSV000087380 - GNPS_Srigolactone_Hemp_exsudat-10-12_method_MS02_traite3
title: Research in hemp exudate of cannabis sativa strigolactone by MS/MS with various Strigolactone control and a blank in acetonitrile.
DOI: 10.14457/cu.res.2012.73
title: โครงสร้างประชากรและนิเวศวิทยาของค้างคาวคุณกิตติในพื้นที่โครงการอนุรักษ์พันธุกรรมพืชอันเนื่องมาจากพระราชดำริ
DOI: 10.14457/sdu.res.2012.30
title: การศึกษาสาเหตุและผลลัพธ์จากการปรับเปลี่ยนระบบการผลิตข้าวเคมีไปเป็นข้าวอินทรีย์ของเกษตรกรตำบลวัดพริก อำเภอเมือง จังหวัดพิษณุโลก
DOI: 10.14457/tu.res.2012.4
title: วิเคราะห์การจ่ายเงิน สินบนและเงินรางวัลแก่เจ้าหน้าที่ของรัฐในการปฏิบัติหน้าที่
DOI: 10.14457/psu.res.2012.14
title: โครงการวิจัยเรื่องชุมชนท้องถิ่นกับกระบวนการมีส่วนร่วมในการจัดการ พื้นที่ลุ่มน้ำในเขตความมั่นคงจังหวัดสงขลา
DOI: 10.14457/mu.res.2012.17
title: การวิเคราะห์หาปริมาณ cotinine ในปัสสาวะเพื่อบ่งชี้ทางชีวภาพ ในการสัมผัสกับควันบุหรี่จากการสูบบุหรี่โดยตรงและควันบุหรี่มือสอง และตัวบ่งชี้ของความเสี่ยงในการเกิดโรคหัวใจ
DOI: 10.14457/cu.the.2012.157
title: การพัฒนารูปแบบการออกแบบบทเรียนสถานการณ์จำลองบนเว็บเพื่อพัฒนาความสามารถในการตัดสินใจของนักเรียนเตรียมทหาร
DOI: 10.14457/kmutt.the.2012.84
title: การสังเคราะห์อนุภาคนาโนเงินด้วยวิธีที่เป็นมิตรกับสิ่งแวดล้อมโดยใช้น้ำสกัดจากเปลือกทุเรียนและสารละลาย agno3 ภายใต้สภาวะที่มีแสง
DOI: 10.14457/cu.the.2012.177
title: การรับรู้ข่าวสาร และความพึงพอใจต่อข่าวสารประชาสัมพันธ์ทางการตลาดของตราสินค้าเครื่องดื่มเพื่อสุขภาพ และความตั้งใจซื้อของผู้บริโภค
DOI: 10.5281/zenodo.4742161
title: Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model

description: This is the dataset we used in our paper entitled "Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model". The manually validated dataset contains various several static source code metrics along with vulnerability fixing hashes for numerous vulnerabilities. For more details, you can read the paper here. Security has become a central and unavoidable aspect of today’s software development. Practitioners and researchers have proposed many code analysis tools and techniques to mitigate security risks. These tools apply static and dynamic analysis or, more recently, machine learning. Machine learning models can achieve impressive results in finding and forecasting possible security issues in programs. However, there are at least two areas where most of the current approaches fall short of developer demands: explainability and granularity of predictions. In this paper, we propose a novel and simple yet, promising approach to identify potentially vulnerable source code in JavaScript programs. The model improves the state-of-the-art in terms of explainability and prediction granularity as it gives results at the level of individual source code lines, which is fine-grained enough for developers to take immediate actions. Additionally, the model explains each predicted line (i.e., provides the most similar vulnerable line from the training set) using a prototype-based approach. In a study of 186 real-world and confirmed JavaScript vulnerability fixes of 91 projects, the approach could flag 60% of the known vulnerable lines on average by marking only 10% of the code-base, but in certain cases the model identified 100% of the vulnerable code lines while flagging only 8.72% of the code-base. If you wish to use our dataset, please cite this dataset, or the corresponding paper: <pre><code>@inproceedings{mosolygo2021towards, title={Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model}, author={Mosolyg{\'o}, Bal{\'a}zs and V{\'a}ndor, Norbert and Antal, G{\'a}bor and Heged{\H{u}}s, P{\'e}ter and Ferenc, Rudolf}, booktitle={2021 International Conference on Code Quality (ICCQ)}, pages={15--25}, year={2021}, organization={IEEE} }</code></pre>
DOI: 10.5281/zenodo.4742138
title: Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model

description: This is the dataset we used in our paper entitled "Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model". The manually validated dataset contains various several static source code metrics along with vulnerability fixing hashes for numerous vulnerabilities. For more details, you can read the paper here. Security has become a central and unavoidable aspect of today’s software development. Practitioners and researchers have proposed many code analysis tools and techniques to mitigate security risks. These tools apply static and dynamic analysis or, more recently, machine learning. Machine learning models can achieve impressive results in finding and forecasting possible security issues in programs. However, there are at least two areas where most of the current approaches fall short of developer demands: explainability and granularity of predictions. In this paper, we propose a novel and simple yet, promising approach to identify potentially vulnerable source code in JavaScript programs. The model improves the state-of-the-art in terms of explainability and prediction granularity as it gives results at the level of individual source code lines, which is fine-grained enough for developers to take immediate actions. Additionally, the model explains each predicted line (i.e., provides the most similar vulnerable line from the training set) using a prototype-based approach. In a study of 186 real-world and confirmed JavaScript vulnerability fixes of 91 projects, the approach could flag 60% of the known vulnerable lines on average by marking only 10% of the code-base, but in certain cases the model identified 100% of the vulnerable code lines while flagging only 8.72% of the code-base. If you wish to use our dataset, please cite this dataset, or the corresponding paper: <pre><code>@inproceedings{mosolygo2021towards, title={Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model}, author={Mosolyg{\'o}, Bal{\'a}zs and V{\'a}ndor, Norbert and Antal, G{\'a}bor and Heged{\H{u}}s, P{\'e}ter and Ferenc, Rudolf}, booktitle={2021 International Conference on Code Quality (ICCQ)}, pages={15--25}, year={2021}, organization={IEEE} }</code></pre>
DOI: 10.6084/m9.figshare.14554137
title: SDP@US1.0 - Dataset for Student Dropout Prediction

description: <b>Dataset UnitelmaSapienza - Version 1.0 (May 2021)</b><br><b><br></b><b>Description: </b>This is the dataset from the article "Hidden Space Deep Sequential Risk Prediction on Student Trajectories" by Bardh Prenkaj, Damiano Distante, Stefano Faralli and Paola Velardi (under review)<b><br></b><b><br></b><b>Disclaimer: </b>Due to privacy protection regulations, all data are aggregated and fully anonymized.<b><br></b><b><br></b><b>Dataset Description:</b><b><br></b>The zip contains 13 folders, each folder contains the timeseries (timeseries.csv) of a specific learning course from the University of Rome Unitelma Sapienza.<br>Each row of a timeseries.csv file corresponds to a student. Each column is a two dimensional numpy array, which corresponds to 365 days of interactions of the student with the e-learning platform.The last column corresponds to the probability of drop-out (see the above indicated paper for more information on how this probability is thresholded to obtain a binary ground truth).<br>This dataset can be directly used as input for the code we released at http://iim.di.uniroma1.it/projects/hsdsrpst/<br>When using this dataset in your work please cite the paper it is associated to.<br><br><b>Authors:</b>Bardh Prenkaj - Sapienza University of Rome, ItalyDamiano Distante - University of Rome Unitelma Sapienza, ItalyStefano Faralli - University of Rome Unitelma Sapienza, ItalyPaola Velardi - Sapienza University of Rome, Italy<br><br><b>License:</b>Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/deed.en<br>For any question regarding the dataset and the source code please contact:Bardh Prenkaj (prenkaj@di.uniroma1.it)<br>
DOI: 10.6084/m9.figshare.14554137.v3
title: UnitelmaSapienza 1.0 May 2021 - Hidden Space Deep Sequential Risk Prediction on Student Trajectories

description: <b>Dataset UnitelmaSapienza - Version 1.0 (May 2021)</b><br><b><br></b><b>Description: </b>This is the dataset from the article "Hidden Space Deep Sequential Risk Prediction on Student Trajectories" by Bardh Prenkaj, Damiano Distante, Stefano Faralli and Paola Velardi (under review)<b><br></b><b><br></b><b>Disclaimer: </b>Due to privacy protection regulations, all data are aggregated and fully anonymized.<b><br></b><b><br></b><b>Dataset Description:</b><b><br></b>The zip contains 13 folders, each folder contains the timeseries (timeseries.csv) of a specific learning course from the University of Rome Unitelma Sapienza.<br>Each row of a timeseries.csv file corresponds to a student. Each column is a two dimensional numpy array, which corresponds to 365 days of interactions of the student with the e-learning platform.The last column corresponds to the probability of drop-out (see the above indicated paper for more information on how this probability is thresholded to obtain a binary ground truth).<br>This dataset can be directly used as input for the code we released at http://iim.di.uniroma1.it/projects/hsdsrpst/<br>When using this dataset in your work please cite the paper it is associated to.<br><br><b>Authors:</b>Bardh Prenkaj - Sapienza University of Rome, ItalyDamiano Distante - University of Rome Unitelma Sapienza, ItalyStefano Faralli - University of Rome Unitelma Sapienza, ItalyPaola Velardi - Sapienza University of Rome, Italy<br><br><b>License:</b>Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/deed.en<br>For any question regarding the dataset and the source code please contact:Bardh Prenkaj (prenkaj@di.uniroma1.it)<br>
DOI: 10.6084/m9.figshare.14526798.v1
title: Tectonic-geomorphology and prehistoric earthquakes of the West Helanshan fault, West Ordos, and its implications for regional tectonics and seismic hazard

description: This data resposity includes two high-resolution digital elevation models (DEMs) derived from drone images using structure from motion (SfM) technique, a KMZ file showing detailed fault traces, and a 14C dating report created by Beta Analystic Radiocarbon Dating Laboratory.
DOI: 10.5281/zenodo.4537834
title: SherlockBox-RuNeg

description: SherlockBox-RuNeg is a data set in Russian that features annotations of negation cues and negation scopes. It consists of 120 sentences from a story by Sir Arthur Conan Doyle, The Adventure of the <em>Cardboard Box</em> ("Картонная коробка"). The story itself and this Russian translation of the story are in the public domain. The Russian text was obtained from http://www.lib.ru/AKONANDOJL/sh_cbox.txt. Only sentences that contain negation were included in SherlockBox-RuNeg. Annotations were performed by one native Russian speaker with the Prodigy annotation tool. The data is presented in two files. One is a json file that stores each sentence as 3 vectors: list of tokens, list of cue labels (1 = cue, 3 = not a cue), list of scope labels (1 = in scope, 0 = not in scope). Another is a text file that gives a more visual representation of each sentence in 5 lines: tokens, cue labels, scope labels, cue tokens, scope tokens.
description: <br>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DOI: 10.5281/zenodo.4537833
title: SherlockBox-RuNeg

description: SherlockBox-RuNeg is a data set in Russian that features annotations of negation cues and negation scopes. It consists of 120 sentences from a story by Sir Arthur Conan Doyle, The Adventure of the <em>Cardboard Box</em> ("Картонная коробка"). The story itself and this Russian translation of the story are in the public domain. The Russian text was obtained from http://www.lib.ru/AKONANDOJL/sh_cbox.txt. Only sentences that contain negation were included in SherlockBox-RuNeg. Annotations were performed by one native Russian speaker with the Prodigy annotation tool. The data is presented in two files. One is a json file that stores each sentence as 3 vectors: list of tokens, list of cue labels (1 = cue, 3 = not a cue), list of scope labels (1 = in scope, 0 = not in scope). Another is a text file that gives a more visual representation of each sentence in 5 lines: tokens, cue labels, scope labels, cue tokens, scope tokens.
description: <br>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DOI: 10.5281/zenodo.4575297
title: Dataset of species richness and mean phylogenetic distance of native and exotic plant species in Japan

description: This data includes species richness (SR) and standardized effect size of mean phylogenetic distance (MPDses) of native and exotic seed plant species in Japan at 10 km grid-cell level. For exotic species, SR and MPDses are also given at sub-group level: 1) exotics from the same biogeographical realms with Japan (Palearctic and Indo-Malay); 2) exotics from the adjacent realms (Nearctic and Oceanic); 3) exotics from remote realms (Afrotropic, Australasian, and Neotropical); 4) exotics whose origins are unknown. The environmental conditions of the grid-cells are also provided (minimum temperature in the coldest month, annual precipitation, soil pH, elevation, and human influence index). This data is used in the manuscript (Kusumoto et al. 2021 DOI: 10.1007/s10530-021-02550-3).
DOI: 10.5281/zenodo.4575296
title: Dataset of species richness and mean phylogenetic distance of native and exotic plant species in Japan

description: This data includes species richness (SR) and standardized effect size of mean phylogenetic distance (MPDses) of native and exotic seed plant species in Japan at 10 km grid-cell level. For exotic species, SR and MPDses are also given at sub-group level: 1) exotics from the same biogeographical realms with Japan (Palearctic and Indo-Malay); 2) exotics from the adjacent realms (Nearctic and Oceanic); 3) exotics from remote realms (Afrotropic, Australasian, and Neotropical); 4) exotics whose origins are unknown. The environmental conditions of the grid-cells are also provided (minimum temperature in the coldest month, annual precipitation, soil pH, elevation, and human influence index). This data is used in the manuscript (Kusumoto et al. 2021 DOI: 10.1007/s10530-021-02550-3).
DOI: 10.5281/zenodo.4740452
title: Dataset archeologisch syntheseonderzoek loopgraven Eerste Wereldoorlog in Vlaanderen (UGent)

description: Shapefiles en gegevenstabel van de onderzochte loopgraven en opgegraven sites in Vlaanderen in het kader van het project "Syntheseonderzoek Archeologie: Loopgraven uit de Eerste Wereldoorlog" - Vakgroep Archeologie - Universiteit Gent (subsidie agentschap Onroerend Erfgoed) De Eerste Wereldoorlog was het eerste wereldwijde conflict dat op een industriële schaal werd uitgevochten. Nooit eerder werd een landschap op zo’n grote schaal vernield dan tijdens die vier jaar aanslepende stellingenoorlog. Na de oorlog keerde de bevolking mondjesmaat terug. Duizenden kilometers loopgraven en ontelbare bomtrechters werden gedempt, en de militaire infrastructuur werd grotendeels ontmanteld. De materiële resten van de oorlog gingen deel uitmaken van het archeologische bodemarchief. Eén van de meest voorkomende en meest iconische structuren uit de archeologie van de Eerste Wereldoorlog zijn de loopgraven. Bij benadering 0,61% van het potentieel aantal loopgraven is archeologisch onderzocht. Dit onderzoek maakt een synthese van alle informatie over loopgraven uit de Eerste Wereldoorlog vanuit 201 archeologische onderzoeksprojecten die sporen uit WO1 onderzochten. Op 114 sites werden loopgraven aangetroffen, die over een totale lengte van 14,9 kilometer archeologisch werden onderzocht (558 fragmenten). Het syntheseonderzoek levert een werkbare typologie op van alle gekende types loopgraven, geeft een inzicht in de verspreiding van de verschillende types loopgraven en een levert een bijdrage tot een verfijning van de chronologie. Bijkomend werd ook een methodische richtlijn over het opgraven van loopgraven opgesteld, en een registratieformulier voorgesteld.
description: {"references": ["Gheyle W., Stichelbaut B. & Verdegem S. 2021: Loopgraven uit de Eerste Wereldoorlog, SYNTAR 2. ISSN 2736-6057 ISBN 9789075230567"]}
DOI: 10.5281/zenodo.4740451
title: Dataset archeologisch syntheseonderzoek loopgraven Eerste Wereldoorlog in Vlaanderen (UGent)

description: Shapefiles en gegevenstabel van de onderzochte loopgraven en opgegraven sites in Vlaanderen in het kader van het project "Syntheseonderzoek Archeologie: Loopgraven uit de Eerste Wereldoorlog" - Vakgroep Archeologie - Universiteit Gent (subsidie agentschap Onroerend Erfgoed) De Eerste Wereldoorlog was het eerste wereldwijde conflict dat op een industriële schaal werd uitgevochten. Nooit eerder werd een landschap op zo’n grote schaal vernield dan tijdens die vier jaar aanslepende stellingenoorlog. Na de oorlog keerde de bevolking mondjesmaat terug. Duizenden kilometers loopgraven en ontelbare bomtrechters werden gedempt, en de militaire infrastructuur werd grotendeels ontmanteld. De materiële resten van de oorlog gingen deel uitmaken van het archeologische bodemarchief. Eén van de meest voorkomende en meest iconische structuren uit de archeologie van de Eerste Wereldoorlog zijn de loopgraven. Bij benadering 0,61% van het potentieel aantal loopgraven is archeologisch onderzocht. Dit onderzoek maakt een synthese van alle informatie over loopgraven uit de Eerste Wereldoorlog vanuit 201 archeologische onderzoeksprojecten die sporen uit WO1 onderzochten. Op 114 sites werden loopgraven aangetroffen, die over een totale lengte van 14,9 kilometer archeologisch werden onderzocht (558 fragmenten). Het syntheseonderzoek levert een werkbare typologie op van alle gekende types loopgraven, geeft een inzicht in de verspreiding van de verschillende types loopgraven en een levert een bijdrage tot een verfijning van de chronologie. Bijkomend werd ook een methodische richtlijn over het opgraven van loopgraven opgesteld, en een registratieformulier voorgesteld.
description: {"references": ["Gheyle W., Stichelbaut B. & Verdegem S. 2021: Loopgraven uit de Eerste Wereldoorlog, SYNTAR 2. ISSN 2736-6057 ISBN 9789075230567"]}