Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Privacy Policies Paragraph containing Personal Data

The data consists in crawled privacy policies from European privacy policies. They were split into paragraphs and annotated as containing or not personal data. The question that was asked to annotators was "Does this paragraph contain the explicit mention of specific personal data (e.g. name, phone number, social security, …) being collected?". A full description of the dataset can be found in D3.4 of the SMOOTH project

opencc-by-4.0May 2020View details →

LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots

In forest inventory, trees are usually measured by handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory is nowadays redesigned, since modern laser scanner technology became available. Laser scanner generate massive data in the form of 3D point clouds. Novel methodology is currently developed to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK), and (ii) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contains digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.

opencc-by-4.0Jun 2024View details →

Data and analysis script for "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel"

Dataset and analysis script for the article "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel", under review at Geoscience Communication (https://doi.org/10.5194/egusphere-2024-543)

opencc-by-4.0Mar 2024View details →

Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study

This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented. For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables.

opencc-by-4.0Jul 2021View details →

Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment

Dataset name: asppl-dataset.csv Version: 1.0 Dataset period: 06/07/2018- 05/25/2021 Dataset Characteristics: Multivalued Number of Instances: 4861 Number of Attributes: 33 Missing Values: Yes Area(s): Health and education  Sources:  <ul> <li> Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1]; </li> <li> Secondary:  <ol> <li> Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2]; </li> <li> National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and  </li> <li> Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4]. </li> </ol> </li> </ul> Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

opencc-by-4.0Mar 2023View details →

DATA: Linking Personality and Trust in Intelligent Virtual Assistants

This dataset (n=367) investigates links between people's personality, their trust in intelligent virtual agents (e.g., Amazon's Alexa, Apple's Siri, etc.) and their affinity for technology interaction.

opencc-by-4.0Oct 2020View details →

Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"

Data associated with: Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE. Description of work associated with this data: <blockquote> As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities. </blockquote>

opencc-by-4.0May 2024View details →

RADAR – Guideline on Personal Data

The HTML publication is available at <a href="https://nfdi4culture.de/go/E5380" target="_blank" rel="noopener">https://nfdi4culture.de/go/E5380</a>. This guideline is intended to help you understand what information falls under the term “personenbezogene Daten” and which of these can be published on RADAR4Culture.

opencc-by-4.0May 2024View details →

Survey with game development companies on personal data protection

Dataset linked to the article: Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study

opengpl-3.0-or-laterSep 2024View details →

Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)

This repository concerns the supplementary material data of the research article entitled "Personalised in silico model for radiation-induced pulmonary fibrosis" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.

opencc-by-4.0Dec 2022View details →

Witchcraft trials in the Czech lands: Data on persons, places, charges, punishments, and material substances

This is the most comprehensive digital dataset of witchcraft trials in the Czech lands since the earliest case in 1491 until as late as 1785. The dataset covers 257 cases. It records the year, the suspects' names, their sex, places of residence (including geographic coordinates), charges, results of the trial, places of interrogation (including geographic coordinates), and the material substances they were charged of using to perform magic. The dataset allows researchers to conduct systematic and quantitative research into early modern withcraft trials, crime and punishment, as well as the imagery of magic and evil-doing. It also allows to include the Czech lands in broader quantitative studies of European witchcraft trials on a large temporal and geographic scale. Five B.A. theses have been written at Masaryk University, Brno, Czech Republic on the basis of this dataset. The dataset does not cover all known trials. Based on data availability, we did our best to cover what was published and reasonably accessible, but we did not perform original archival research. Our very rough estimate is that up to 100 further specific witchcraft trials in the Czech lands could be identified in published material, and archival work could reveal further ca. 250-800 cases.

opencc-by-4.0Mar 2023View details →

Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data

Abstract: The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations. Content: <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a>  <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a>  <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a>  <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a>  <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a>  <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> Publications refering to this dataset: Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes." Funding: This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6.  License: CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a> Version history: Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file. <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>

opencc-by-4.0Jul 2023View details →

Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> .  The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data. `processed_covariate_data.csv`: ``` Rows: 298 Columns: 7 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ age_cat: indicates age category at the time of a PEM session $ sq_sex:  indicates the sex of the participant (male, female) as filled in during the screening questionaire $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions.  $ bmi_cat: indicates BMI category at the time of a PEM session ``` `processed_lcms_data data.csv` contains the processed LCMS data: ``` Rows: 298 Columns: 4297 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime. Non-detects (below limit of detection (LOD) are coded as 1 for the compounds. .... ``` In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables. The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.

opencc-by-4.0Oct 2020View details →

Data for Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men

These are the data and metadata for the article  Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men by  Gerhard Gmel, Simon Marmet, Joseph Studer, and Matthias Wicki to be published in Frontiers of Psychiatry

opencc-by-4.0Jan 2021View details →

Refined personal name data from the census book of Vodskaja pjatina

The data contains approximately 36,000 personal names derived from medieval Russian documentation. More preciously, names are collected from an edited version of the census book of Vodskaja pjatina, which was one of the five administrative areas in the late 15th century Novgorod. Editions were compiled in parts and the first two, which cover the northernmost region, are called Переписная окладная книга по новугороду вотской пятины (1851, 1852)(POKV I‒II). The third part of the book series Новгородские пистсовые книги (1868)(NPK III) covers the southern and western parts of the study area. The process of obtaining the personal from the inscription has been following: First, editions of the census book were obtained as scanned PDF files. These were transformed as editable copies by using OCR (=Optical Character Recognition) software Abbyy. The program read the original mid-19th century Russian text adequately with its old Russian alphabet package. After the initial corrections, a Python script was written to harvest the personal names. This was based on exploiting the systematic formalities in how most of the names were presented in the census book. The script looked for abbreviations “дв.” and “д.” and extracted all following capitalized words until section end markers “.”, “;” or “:”. As an output, a name to pogost matrix was produced, which held the raw frequencies of each word in each pogost. The process of cleaning the name data, in turn, has been done mostly by data wrangling program OpenRefine in following manner: For starters, all name forms shorter than four characters were removed as there were no personal names consisting of three or less letters. Furthermore, nouns that were not names were removed. This meant discarding expressions that described person’s special feature or profession, like such as being a widow (“вдова”) or working as a deacon (“діакъ”). For some reason, editors followed inconsistent conventions in capitalizing these non-name nouns. In addition, some orthographical and morphological harmonization was done on the data. The letter ы was cut from the end of bynames, where it denotes plurality. Similarity of so called soft and hard signs, ь and ъ caused some problems. As the latter one is not used in contemporary Russian and was not used in the original documents either (Неволин 1853 : 4 (in Appendix 1)) it was removed. The soft sign ь was also removed because it was absent in the original documents and it had been used inconsistently by the editors. The letter ѣ (yat) is rarely used in personal names but nevertheless, it was changed to е (like as it is in contemporary Russian) as since it was often confused with soft and hard signs (ь and ъ). Furthermore, the letter ѳ (fita) was often erroneously recognized as о or е. As it is only found in NPK III and only in the beginning of certain names, which all are also written with “Ф” (e.g. “Ѳедко” vs. “Федко”), it was replaced with Ф. In the second phase most of the erroneous orthographies were corrected. We do not detail herescribe all the OCR-errors here that were found, but in the following a short description is given of the most significant corrections. There were, for example, many letters whose similarity caused problems for the OCR-program (e.g.  и / й and б / в). In these cases, the correct orthography was sought in the census book editions and accordingly, Openrefine was used to change erroneous forms to right correct ones. After the corrections were made, the number of name types (= name variants) was reduced from 4942 to 2748. The Overall overall number of name tokens was dropped as well: from 36,405 to 35,726. Of the name types, more than half (1484) have only one occurrence. The refined and harmonized data is published as pogost-by-name frequency tabulations (pogost, equivalent of English parish). The file is in tab-delimited file (.tsv) format. References: Неволин, К. А. 1853, О пятинах и погостах новгородских в XVI веке, с приложением карты,  Санкт-Петербург (Из Записок Императорского русского географического общества, Кн. VIII). NPK III = Новгородские писцовые книги, Т. 3 : Переписная оброчная книга Вотской пятины, 1500 года, 1868, 1868, Санкт Петербург. POKV I, II = Переписная окладная книга по Новугороду Вотьской пятины, 1851, 1852, Имп. Моск. о-во истории и древностей рос., Москва.

opencc-by-4.0Dec 2023View details →

Handling of Personal Data by Smart Home Equipment: an Exploratory Analysis in the Context of LGPD

This dataset provides data about an exploratory research that analyzed the Privacy and Security Policies and the Instruction Manuals of 59 home automation equipment for Smart Home in order to verify which personal data was handled and how these documents were providing information about processes performed in personal data. The analysis was conducted with a quantitative approach followed by a qualitative analysis, using content analysis.

opencc-by-4.0May 2022View details →

Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? — Supporting Data

Supporting data for Martens et al. Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model? arXiv:2111.13404.

opencc-by-4.0Dec 2021View details →

Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis

This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.

dryad40/100

Source code and data from: Foraging personalities modify effects of habitat fragmentation on biodiversity

Habitat loss undeniably poses a substantial threat to biodiversity, but whether fragmentation per se drives the loss of species is still widely debated. While negative consequences from fragmentation are often anticipated, many empirical studies report positive effects. However, the intrinsic mechanisms governing species' persistence in fragmented landscapes are not yet understood. In this study, we investigated consistent personality-dependent differences in foraging behavior among individuals as a possible mechanism underlying the discrepancy of reported fragmentation effects. We devised a mechanistic individual-based model simulating the home range behavior of a competitive small mammal community based on the availability of a shared resource. Thereby, an individual's risk-taking behavior dictates its foraging decisions at risky habitat edges, an inherent property of fragmentation per se. Our simulations show that differences in risk-taking while foraging are potentially a further mechanism contributing to reconciling the fragmentation debate. The first scenario considering risk-seeking communities showed a neutral response towards fragmentation, while the second scenario featuring risk-avoiding communities confirmed the negative effects of fragmentation. Notably, the third scenario, simulating behaviorally diverse communities including risk-avoiding and risk-seeking individuals, demonstrated a positive influence of fragmentation on biodiversity. Intraspecific differences in behavior could also enhance the temporal species coexistence (coviability) of communities threatened by an ongoing habitat loss. Our study highlights the importance of recognizing the behavioral composition of populations and communities for estimating fragmentation effects, because differences in risk-taking can influence the coping abilities of animal communities in light of fragmentation.

opencc-zeroSep 2022View details →