Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Dataset for ´´A New Detailed Global Map of Lunar Light Plains´´ research article

The shapefiles (.shp) provided in this repository are the datasets for the paper ´A new detailed global map of lunar light plains´ published in PSJ journal Special Issue.  These shapefiles can be directly imported in ArcMap/ArcPRO. The third dataset is a .tif or image of the global map for a fast and easy overview. Two geomorphologic maps of lunar light plains are provided as described in the article: one with an FeO wt% cut off of about 12 wt% (Area_lightplains), and the other around 8 wt% (Area_LPFeOLow).

opencc-zeroJul 2024View details →

Supplemental material to the journal article "The complete mitogenome of an unidentified Oikopleura species"

<ul> <li>Wibisana2024)_rev2.tar.gz: aligned sequence files, command-line notes and figures related to the phylogenetic tree in the journal article “The complete mitogenome of an unidentified Oikopleura species”, revision 2.</li> <li>Wibisana2024_rDNA_PacBio_read.fa: a sequence read from the same run used to assemble the mitogenome, that contains a copy of the rDNA locus of the nuclear genome.</li> <li>Wibisana2024_supplementary_rev2.pdf: supplementary figures and tables for the article, revision 2.</li> </ul>

opencc-by-4.0Jan 2021View details →

Data for the Article: Cross-validation of a semantic segmentation network for natural history collection specimens

This deposit contains six datasets which were used for testing and validating a semantic segmentation network. The purpose was to evaluate the suitability of the segmentation network for use in the processing of images from Natural History Collections.

opencc-by-4.0Oct 2021View details →

Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"

Data for the research article: "Simulations of Energetic Neutral Atom sputtering from Ganymede in preparation for the JUICE mission"

Selected articles from the scoping review of biomarker discovery studies for the EU project on "Personalised Medicine Trials" (PERMIT)

This dataset provides the data extracted for the scoping review of the literature on biomarker discovery studies for patient stratification using machine learning analysis of omics data, as part of the EU project on “Personalised Medicine Trials” (PERMIT). It covers the references for all articles selected as part of the scoping review, as well as information on the study type and methodology, the outcome measures, the validation type, and representative sentences extracted from each article on the main results and key findings of the corresponding biomarker study.

Dataset accompanying the journal article: Bouldering psychotherapy is not inferior to cognitive behavioural therapy in the group treatment of depression: A randomized controlled trial

SPSS-Dataset containing the (not-imputed) raw data for the non-inferiority trial on BPT vs. CBT. All personal data removed. Only data of participants of the CBT or BPT group included.

opencc-by-4.0Dec 2020View details →

Datasets and Simulator of the kinSoftChallenge & related article.

Data described in the article:  Inferring kinetic rate constants from single-molecule FRET trajectories – a blind benchmark of kinetic analysis tools. - MATLAB simulator scripts & configuration files incl. parameters used in the kinSoftChallenge. - simulated challenge datasets used in the kinSoftChallenge. - experimental challenge datasets (Fig. 2) were kindly provided by Ben Schuler, University of Zurich, CH. - experimental challenge datasets (Fig. 5) were kindly provided by Michael Schlierf, TU Dresden, D.

opengpl-2.0-or-laterNov 2021View details →

Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.

This package contains data and codes related to the article: B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra.  A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research. R scripts The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh. The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps. <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in dataset_XXX/runs.</li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in dataset_XV and dataset_XV_1971-1990.</li> </ul> Data Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders dataset_XXX/data. <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul>

opencc-by-4.0Dec 2020View details →

Dataset from "The Land-to-Ocean Loops of the Global Carbon Cycle" article

Dataset of Tables S1, S2 and S3 from the paper entitled "The Land-to-Ocean Loops of the Global Carbon Cycle".

Article dataset: 'How stable are visions for protected area management? Stakeholder perspectives before and during a pandemic'

This dataset includes the raw data of a survey of 38 stakeholders in the region of the Sierra de Guadarrama National Park, Spain (2019). The "Dataset" sheet presents respondents' Likert-scale answers (ranging from 1 to 5) of agreement regarding values, perceived changes and perceived drivers of change of the national park landscapes. The complete methodology is described in: Lo, V.B., López-Rodríguez, M.D., Metzger, M., Oteros-Rozas, E., Cebrián-Piqueras, M. A., Ruiz-Mallén, I., March, H., Raymond, C.M. (in press) ‘How stable are visions for protected area management? Stakeholder perspectives before and during a pandemic.’ People and Nature. Accompanying supplementary data (interview script, tiles and canvasses, coding, follow-up survey questions) are available as supplementary information to this article available for open access on the People and Nature website.

Dataset related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma"

This record contains data related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma". Myxoid liposarcoma (MLPS) is a rare soft-tissue sarcoma characterised by the expression of FUS-DDIT3 chimera. Trabectedin has shown significant clinical anti-tumour activity against MLPS. To characterise the molecular mechanism of trabectedin sensitivity and of resistance against it, we integrated genomic and transcriptomic data from treated mice bearing ML017 or ML017/ET, two patient-derived MLPS xenograft models, sensitive to and resistant against trabectedin, respectively. Longitudinal RNA-Seq analysis of ML017 showed that trabectedin acts mainly as a transcriptional regulator: 15 days after the third dose trabectedin modulates the transcription of 4883 genes involved in processes that sustain adipocyte differentiation. No such differences were observed in ML017/ET. Genomic analysis showed that prolonged treatment causes losses in 4p15.2, 4p16.3 and 17q21.3 cytobands leading to acquired-resistance against the drug. The results dissect the complex mechanism of action of trabectedin and provide the basis for novel combinatorial approaches for the treatment of MLPS that could overcome drug-resistance.

Topic Model for English Wikipedia's Biographies with list of all 1.8M articles linked to Wikidata

A Genism LDA Topic Model of English Wikipedia biographical articles with list of all 1.8M articles, and some associated Wikidata information The model has 150 Topics. This model was developed in the process of isolating a set of visual arts biographical articles, as described in "Clowns in the Visual Artists: Topic Modeling Wikipedia and Wikidata" in the Spring 2022 issue of Art Documentation - <a href="https://doi.org/10.1086/719999">https://doi.org/10.1086/719999</a> Because names, nationalities, and birthdays are so prominent in biographies, the stopwords list removed 170,000 names, surnames, city names, place names, countries, days, months and other time related words (https://github.com/mandiberg/Names-Surnames-and-Countries-for-Stopwords).  We also directly removed each article subject’s given and surname, which were almost always the most frequently occurring words in any given article. Otherwise, the model just produced topics based on nationality, and common names and surnames. Files: all_enwiki_bios_from_wikidata.csv The list of all Wikidata items for humans with an enwiki page (e.g biographical article) was extracted from Wikidata JSON dump; list includes gender, occupation, and nationality. This was joined with the converted plaintext from an English Wikipedia dump. This data was downloaded in March 2021. Wikipedia Biographies LDA Topic Model human readable summary.csv A human readable file with the 150 topics ranked by count of articles per topic from the 1.8M corpus. The most popular topics have categorical descriptions of the occupations of each cluster. Some are marked as not an occupation cluster.  BoW_corpus.mm* model_lda_full_Sep2_150Tv2* These six files comprise the topic model. The code to load them is present in the python files.  dict_full_Aug-28-2021 processed_docs_full_Aug-28-2021.txt processed_docs_1000_Aug-18-2021.txt These are the dictionary and processed corpuses required to build and implement the model using this code. The corpus with the first 1000 items is meant to be used for testing, as the full one is quite large and takes a long time to complete.  topic-model-wikipedia-sept2021.zip The code and settings used for creating and implementing this model are included in this zip and are also available here: https://github.com/mandiberg/topic-model-wikipedia All-Wikipedia-Biographies-with-topic1.csv All-Wikipedia-Biographies-with-topic1and2.csv These are the list of 1.8M biographies matched to topics. The "topic1" file just includes the first topic, this is a slightly larger list. The "topic1and2" file is slightly smaller because about 2% articles do not match to a second topic. Analysis-for-Clowns-Visual-Arts.zip These are the raw data and final data produced for the "Clowns in the Visual Artists." Please see the article for context.

opencc-by-4.0Mar 2021View details →

Data for the article "Strained crystalline nanomechanical resonators with ultralow dissipation"

GDS fabrication masks, ringdown traces and compilations of fit results, exemplary Raman scans, results of relevant FEM simulations and some of the scripts used for generating the plots in the manuscript.

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis (01.2016-04.2021)

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources with weights: <ul> <li>Euractiv 5%</li> <li>The Conversation 5%</li> <li>Politico Europe 5 %</li> <li>IEEE Spectrum 5 %</li> <li>Techforge 5%</li> <li>Fastcompany 5%</li> <li>The Guardian (Tech) 12%</li> <li>Arstechnica 5%</li> <li>Reuters 5%</li> <li>Gizmodo 9%</li> <li>ZDNet 9%</li> <li>The Register 12%</li> <li>The Verge 9%</li> <li>TechCrunch 9%</li> </ul> Methodology 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: <ul> <li>The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)</li> <li>The articles containing the given social issue and co-occurring term are identified</li> <li>The identified articles are divided into paragraphs</li> <li>Social issue and co-occurring words are removed from the paragraph</li> <li>The VADER sentiment analysis is carried out for every identified and modified paragraph</li> <li>The average for the given word pair is calculated for the final result</li> </ul> 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.   *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.

Dataset related to article "How Academics and the Public Experienced Immersive Virtual Reality for Geo-education"

The dataset is associated with the paper entitled: How Academics and the Public Experienced Immersive Virtual Reality for Geo-education. It contains feedback regarding users’ experience with Immersive Virtual Reality for geological exploration, through a tailored approach developed by Tibaldi et al. (2020) where the Virtual Landscape is based on 3D photogrammetry-based high-resolution models. Such feedback has been acquired through anonymous questionnaires during nine dissemination events held in 2018 and 2019 in various locations (Vienna in Austria, Milan and Catania in Italy and Santorini in Greece), in the framework of the following projects: i) the MIUR project ACPR15T4_00098–Argo3D (http://argo3d.unimib.it/); ii) 3DTeLC Erasmus+Project 2017-1-UK01-KA203-036719 (<a href="http://www.3dtelc.com">http://www.3dtelc.com</a>); iii) EGU 2018 Public Engagement Grant (https://www.egu.eu/outreach/peg/) . In the dataset, feedback has been grouped into categories, based on users age and background: i) Middle and High School Students (Schools students, results in Sheet 1); ii) MSc Students in Earth Sciences (MSc, results in Sheet 2); iii) Academics/Researchers in Earth Sciences, that include PhD students and postdocs (Academics, results in Sheet 3); iv) Lay Public (i.e. participants that do not belong to the other groups, results in Sheet 4). It lists a total of 459 records; further details are available in the manuscript. If you use this dataset, please do cite the following papers: Bonali et al., How Academics and the Public Experienced Immersive Virtual Reality for Geo-education. Geosciences. Tibaldi, A.; Bonali, F.L.; Vitello, F.; Delage, E.; Nomikou, P.; Antoniou, V.; Becciani, U.; Van Wyk de Vries, B.; Krokos, M.; Whitworth, M. Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. Bull. Volcanol. 2020, 82, 1–12.

opencc-by-3.0Dec 2021View details →

Recent Revision History of Featured Articles on Wikipedia

The dataset is composed of recent revisions (from July 21st 2020 to Dec 10th 2021) of 6,039 featured articles on Wikipedia. Each page is stored in a .json file with all associated revisions contained within, sorted from most recent to oldest. Each revision contains metadata as well as the full wikitext content. Extra fields were added to revisions: <ul> <li>content-plaintext: Parsed wikitext content (using python's mwparserfromhell package)</li> <li>comment-plaintext: Parsed plaintext of comment</li> <li>content-diff: Sentence-level diffs of current revision's content and the previous revision's content, produced by the difflib python package (unified diff)</li> <li>content-plaintext-diff: Similar to content-diff, but with content-plaintext as input</li> <li>reverted: Indicates whether the revision was reverted by a later revision</li> </ul> Total size after decompression: ~ 59.2 GB

Annotation dataset for the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind"

This is the resulting dataset from the text annotation exercise in the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind" that will appear in the journal Open Archeology in a special issue titled Archaeological Practice on Shifting Grounds (edited by Åsa Berggren and Antonia Davidovic-Walther). The article is accepted for publication and the annotations are final. CIDOC CRM is used for text annotations.

opencc-by-4.0Jul 2021View details →

rmidura/emdigit: Early Modern Digital Itineraries: Itinerating Europe Article Appendix & Data

This project combines history of the book with digital approaches to explore how a database of sixteenth- through eighteenth-century itineraries may reshape our understanding of historical travel and communication. Printed itinerary books provided early modern travelers with lists of cities along routes. Digital spatial approaches often rely upon modern mapmaking and its built-in assumptions of up-to-date accuracy, decentered viewpoint, stable place identifiers, and direct distances. The data provided has been hand-entered from over 80 multilingual, international itineraries published from the sixteenth through eighteenth centuries and formatted for relative ease of use with network and spatial analysis tools. The data provided here represents the stage of work at the time of publication of Rachel Midura, Itinerating Europe: Early Modern Spatial Networks in Printed Itineraries, 1545–1700, Journal of Social History, Volume 54, Issue 4, Summer 2021, Pages 1023–1063, <a href="https://doi.org/10.1093/jsh/shab011">https://doi.org/10.1093/jsh/shab011</a>. Drawing from the physical and digitized collections of European state libraries, the database includes more than three-quarters of identified itinerary authors (twenty-five of thirty-two) and nearly a third of identified surviving editions (84 of 299, see Appendix). The resulting database consists of 3,655 unique routes, connecting 1,587 cities, published and re-published over the course of two centuries. All titles featured in the appendix bibliography follow the format of a header ("Rome to Milan") followed by a list of intermediary cities. Narrative guidebooks are only included if they feature such route tables as a significant component. I distinguish between a "route," meaning an abstracted connection between an origin, a destination, and possible intermediaries ("Rome to Paris via Milan,") and an "edge," meaning a one-to-one relationship between locations ("Rome to Paris," "Rome to Milan," and "Milan to Paris."). Routes and edges both preserve the original directionality, distinguishing between "Rome to Paris," and "Paris to Rome," for example. This dataset consists solely of the route headers found across the included itineraries and not the intermediary stops within at the present time. Further detail can be found in the included data schema.

Machine Assisted Translation of Wikipedia Articles into Low Resource Languages

Wikipedia is the largest encyclopedia ever assembled with the vision of enabling every human being to freely share in the sum of all knowledge. Wikipedia currently has a total of more than six million articles and over 17 billion words in its English edition. Unfortunately, millions of people cannot access this resource because it’s not available in their language. For instance, at the moment there are only 218 Tigrinya Wikipedia and 15,018 Amharic Wikipedia articles. In this project, we investigate the problem of translating Wikipedia articles from a high resource language into low resource languages using human-in-the-loop MT systems. In particular, we investigate different approaches to translate a sample of English Wikipedia articles into Tigrinya and Amharic. Currently, this repository contains 100k English Wikipeida abstracts translated using Lesan (https://lesan.ai) into Amharic and Tigrinya.   Structure of data directory: data ├── human └── mt     ├── google     ├── lesan     │   ├── am.txt     │   ├── en.txt     │   └── ti.txt     └── microsoft