Find research datasets worth reusing

Reference percentiles for carotid measures of subclinical atherosclerosis in children and adolescents – Data from the KiGGS study

The dataset presents reference centile data for carotid intima-media thickness as well as four carotid stiffness parameters. The reference percentiles are based on data from the German Health Interview and Examination Survey for Children and Adolescents 2003-2006 (KiGGS). KiGGS started as a cross-sectional study conducted between 2003 and 2006 which was based on a nationally representative sample and aimed at obtaining comprehensive data on the health of children and adolescents aged 0 to 17 years living in Germany. Detailed information on study design and conduct has been published in peer reviewed journals as well as detailed papers on individual parameters. The documentation provides relevant references. 11 years later, KiGGS Wave 2 (2014-2017) was conducted as an interview and examination survey. Carotid sonography was attempted at follow-up (KiGGS2) in all 4,798 participants of the KiGGS cohort aged 14 to 28 years. Carotid intima-media thickness (CIMT) was successfully measured in 4,709 participants. Reference centiles for the distensibility coefficient, stiffness index ß, Young's elastic modulus and Peterson's elastic modulus were computed using data on 4,305 adolescents and young adults aged 14 to 28.

opencc-by-4.0Feb 2022View details →

Zero Modes and Classification of Combinatorial Metamaterials

This dataset contains the simulation data of the combinatorial metamaterial as used for the paper 'Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials', as published in Physical Review Letters. In this paper, the data is used to classify each \(k \times k\) unit cell design into one of two classes (C or I) based on the scaling (linear or constant) of the number of zero modes \(M_k(n)\) for metamaterials consisting of an \(n\times n\) tiling of the corresponding unit cell. Additionally, a random walk through the design space starting from class C unit cells was performed to characterize the boundary between class C and I in design space. A more detailed description of the contents of the dataset follows below. Modescaling_raw_data.zip This file contains uniformly sampled unit cell designs for metamaterial M2 and \(M_k(n)\) for \(1\leq n\leq 4\), which was used to classify the unit cell designs for the data set. There is a small subset of designs for \(k=\{3, 4, 5\}\) that do not neatly fall into the class C and I classification, and instead require additional simulation for \(4 \leq n \leq 6\) before either saturating to a constant number of zero modes (class I) or linearly increasing (class C). This file contains the simulation data of size \(3 \leq k \leq 8\) unit cells. The data is organized as follows. Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4.npy", and contain a [Nsim, 1+k*k+4] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4)\}\).</li> </ul> Note: the unit cell design uses the numbers \(\{0, 1, 2, 3\}\) to refer to each building block orientation. The building block orientations can be characterized through the orientation of the missing diagonal bar (see Fig. 2 in the paper), which can be Left Up (LU), Left Down (LD), Right Up (RU), or Right Down (RD). The numbers correspond to the building block orientation \(\{0, 1, 2, 3\} = \{\mathrm{LU, RU, RD, LD}\}\). Simulation data for \(3 \leq k \leq 5\) and \(1 \leq n \leq 6\) for unit cells that cannot be classified as class C or I for \(1 \leq n \leq 4\) is stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. These files are named "data_new_rrQR_i_n_M_kxk_fixn4_classX_extend.npy", and contain a [Nsim, 1+k*k+6] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+5: number of zero modes \(M_k(n)\) in ascending order of \(n\), so: \(\{M_k(1), M_k(2), M_k(3), M_k(4), M_k(5), M_k(6)\}\).</li> </ul> Simulation data for \(6 \leq k \leq 8\)  unit cells are stored in numpy array format (.npy) and can be readily loaded in Python with the Numpy package using the numpy.load command. Note that the number of modes is now calculated for \(n_x \times n_y\) metamaterials, where we calculate \((n_x, n_y) = \{(1,1), (2, 2), (3, 2), (4,2), (2, 3), (2, 4)\}\) rather than \(n_x=n_y=n\) to save computation time. These files are named "data_new_rrQR_i_n_Mx_My_n4_kxk(_extended).npy", and contain a [Nsim, 1+k*k+8] sized array, where Nsim is the number of simulated unit cells. Each row corresponds to a unit cell. The columns are organized as follows: <ul> <li>col 0: label number to keep track</li> <li>col 1 - k*k+1: flattened unit cell design, numpy.reshape should bring it back to its original \(k \times k\) form. </li> <li>col k*k+1 - k*k+9: number of zero modes \(M_k(n_x, n_y)\) in order: \(\{M_k(1, 1), M_k(2, 2), M_k(3, 2), M_k(4, 2), M_k(1, 1), M_k(2, 2), M_k(2, 3), M_k(2, 4)\}\).</li> </ul> Simulation data of metamaterial M1 for \(k_x \times k_y\) metamaterials are stored in compressed numpy array format (.npz) and can be loaded in Python with the Numpy package using the numpy.load command. These files are named "smiley_cube_x_y_\(k_x\)x\(k_y\).npz", which contain all possible metamaterial designs, and "smiley_cube_uniform_sample_x_y_\(k_x\)x\(k_y\).npz", which contain uniformly sampled metamaterial designs. The configurations are accessed with the keyword argument 'configs'. The classification is accessed with the keyword argument 'compatible'. The configurations array is of shape [Nsim, \(k_x\), \(k_y\)], the classification array is of shape [Nsim]. The building blocks in the configuration are denoted by 0 or 1, which correspond to the red/green and white/dashed building blocks respectively. Classification is 0 or 1, which corresponds to I and C respectively. Modescaling_classification_results.zip This file contains the classification, slope, and offset of the scaling of the number of zero modes \(M_k(n)\) for the unit cells of metamaterial M2 in Modescaling_raw_data.zip. The data is organized as follows. The results for \(3 \leq k \leq 5\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 4\)) col 2: slope from \(n \geq 2\) onward (undefined for class X) col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\) col 4: \(M_k(1)\) The results for \(3 \leq k \leq 5\) based on the extended \(1 \leq n \leq 6\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scen_slope_offset_M1k_kxk_fixn4_classC_extend.txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class, where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n \leq 6\)) col 2: slope from \(n \geq 2\) onward (undefined for class X) col 3: the offset is defined as \(M_k(2) - 2 \cdot \mathrm{slope}\) col 4: \(M_k(1)\) The results for \(6 \leq k \leq 8\) based on the \(1 \leq n \leq 4\) mode scaling data is stored in "results_analysis_new_rrQR_i_Scenx_Sceny_slopex_slopey_offsetx_offsety_M1k_kxk(_extended).txt". The data can be loaded using ',' as delimiter. Every row corresponds to a unit cell design (see the label number to compare to the earlier data). The columns are organized as follows: col 0: label number to keep track col 1: the class_x based on \(M_k(n_x, 2)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_x \leq 4\)) col 2: the class_y based on \(M_k(2, n_y)\), where 0 corresponds to class I, 1 to class C and 2 to class X (neither class I or C for \(1 \leq n_y \leq 4\)) col 3: slope_x from \(n_x \geq 2\) onward (undefined for class X) col 4: slope_y from \(n_y \geq 2\) onward (undefined for class X) col 5: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_x}\) col 6: the offset_x is defined as \(M_k(2, 2) - 2 \cdot \mathrm{slope_y}\) col 7: \(M_k(1, 1)\) Additionally, results including classification for M2.ii can be found in the "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scen_slope_M_M1k_kxk.txt and "results_analysis_unimodal_vs_oligomodal_vs_plurimodal_i_Scenx_Sceny_slopex_slopey_Mx_My_M1k_kxk.txt" files. Random Walks Data This file contains the random walks for \(3 \leq k \leq 8\) unit cells of metamaterial M2. The random walk starts from a class C unit cell design (classification M2.ii), for each step \(s\) a randomly picked unit cell is changed to a random new orientation for a total of \(s=k^2\) steps. The data is organized as follows. The configurations for each step are stored in the files named "configlist_test_i.npy", where i is a number and corresponds to a different starting unit cell. The stored array has the shape [k*k+1, 2*k+2, 2*k+2]. The first dimension denotes the step \(s\), where \(s=0\) is the initial configuration. The second and third dimension denote the unit cell configuration in the pixel representation (see paper) padded with a single pixel wide layer using periodic boundary conditions.  The class for each configuration are stored in "lmlist_test_i.npy", where i corresponds to the same number as for the configurations in the "configlist_test_i.npy" file. The stored array has the shape [k*k+1], where the index corresponds to the step \(s\) and displays the class for the accompanying unit cell. The stored number corresponds to the class as \(\{0, 1\} = \{\mathrm{I}, \mathrm{C}\}\).

opencc-by-4.0Nov 2019View details →

Updating Möller – Teil 1 (supplementary material)

Supplementary material for blog post <a href="https://aku.hypotheses.org/64">Updating Möller – Teil 1</a>. It contains a CSV file with a concordance of sigla used in Möller, Georg. 1927. Hieratische Paläographie. Die aegyptische Buchschrift in ihrer Entwicklung von der fünften Dynastie bis zur römischen Kaiserzeit I. Bis zum Beginn der Achtzehnten Dynastie. 2. Aufl. Leipzig: Hinrichs. The column names of the header are “Spalte”, “Sigle” and “Identifizierung“. Version 1.2.0: Updated sigla in column “Sinuhe”.

opencc-by-4.0Mar 2021View details →

QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles

This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>. The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.

Optimizing laboratory cultures of Gammarus fossarum (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology

Supplemental code and data for Alther, Krähenbühl, Bucher & Altermatt (2022) 'Optimizing laboratory cultures of Gammarus fossarum (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology' (DOI: 10.1016/j.scitotenv.2022.158730). The repository folder contains three text files and a corresponding R script. Rerunning the analysis and producing figures requires two raw data files: LabdataAK_v6_210616_Daylength_input.txt and Nutrition_Exp_KaplanMeier_v1_input.txt. In order to reproduce the analysis and figures, run 'AmphipodHusbandry_20220919.R'. Make sure that your working directory is the folder containing all data files, easily achieved by (re)starting R (or R Studio) by double-clicking the R script file in the folder. The analysis script will produce all the figures from the paper, organized in a folder 'Results' and a subfolder 'Supplement'. Figures are prepared as pixel graphics (PNG). The R script was tested in R ver. 4.1.1 (Windows 10, version 21H1), 4.1.3 (macOS 11.6), and 4.2.0 (Ubuntu 22.04. Required packages are survival (version 3.2-13 worked), survminer (version 0.4.9 worked), and vioplot (version 0.3.7 worked).

Viva con Agua de St. Pauli e.V. User Stories created by socio-trechnical walkthroughs

In March 2016 six socio-technical walktroughs (STWT, Hermann et al. 2004) have been conducted to identify requirements for the technical tool Pool used by Viva con Agua de St. Pauli e.V. (VCA) as a socio-technical organization (Kunau 2006). Volunteers of the NGO, as well as employees and partners were invited to participate one weekend for one STWT workshop. Every workshop has covered two days (a weekend) and focused one specific business process of VCA. The participants were independently selected for each working procedure that became a topic for a workshop. During the workshops, the existing business processes have been identified in a first step and during a second one improved. Afterwards, the moderator of the workshops has derived user stories for new technical support functions needed to establish the reworked business processes. These user stories are collected in the user stories catalog that is published in this data publication. <table align="left"> <tbody> <tr> <td>(Herrmann et al. 2004)</td> <td>Herrmann, T., Kunau, G., Loser, K.-U., and Menold, N. Socio-technical walkthrough: Designing technology along work processes. Proceedings of the eighth conference on Participatory design Artful integration: interweaving media, materials and practices - PDC 04, ACM Press (2004), 132.</td> </tr> <tr> <td>(Kunau 2006)</td> <td>Kunau, G. Facilitating computer supported cooperative work with socio-technical self-descriptions. Technische Universität Dortmund, (2006).</td> </tr> </tbody> </table>

MUHAI Benchmark : Task 1 (Short story generation with Knowledge Graphs)

Meaning and Understanding in Human-Centric AI (MUHAI) Benchmark Task 1 (Short story generation with Knowledge Graphs and Language Models)  The dataset can be used to test understandability of text generated through the combination of knowledge graphs and language models without using knowledge graph embeddings. The task here is to generate 5-sentence stories from a set of subject-predicate-object triples that are extracted from a knowledge graph. Two steps need to be performed: 1. Language model fine-tuning (SVO triple extraction + model fine-tuning) 2. Story generation (knowledge enrichment + text generation)  The submission includes the following data: <ol> <li>Original ROC stories corpus (100 stories)</li> <li>ROC stories encoded with relevant triples (extracted through SpaCy, 2 versions, with and without coreference resolution)</li> <li>Stories generated by the pre-trained model (GPT2-simple)</li> <li>Stories generated by the fine-tuned model (DICE + ConceptNet + DBpedia )</li> <li>Stories generated by the fine-tuned model (DICE + ConceptNet + DBpedia + WordNet )</li> <li>Stories generated by the GPT-2-keyword-generation (an open-source software that uses GPT-2 to generate text pertaining to the specified keywords)</li> <li>Model results</li> <li>Evaluation metrics description</li> <li>User-evaluation questionnaire </li> </ol> Code : https://github.com/kmitd/muhai-dice_story

opencc-by-4.0May 2022View details →

Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode

This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past. This dataset contains: <ul> <li>Solar constant experiments: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul>      <ul> <li>Transient experiments: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)

Corona-Datenspende - Teildatensatz Erleben und Verhalten in der Pandemie

Die Daten von Fitnessarmbändern und Smartwatches, sogenannten Wearables, können Hinweise auf Symptome einer Infektion mit COVID-19 liefern. Mit Hilfe der <a href="https://corona-datenspende.de/science/">Corona-Datenspende-App</a> (CDA) können Bürger:innen dem Robert Koch-Institut diese Daten zur wissenschaftlichen Auswertung zur Verfügung stellen. Zusammen mit Informationen aus anderen Quellen, z.B. offiziellen Meldedaten zu Fallzahlen, helfen diese Daten den Wissenschaftler:innen, die Ausbreitung des Coronavirus besser zu erfassen und zu verstehen.   In ihrer ersten, seit April 2020 verfügbaren, Version erhob die CDA nur sogenannte Vitaldaten der Nutzer:innen, insbesondere den Ruhepuls, körperliche Aktivität und Schlafverhalten. In einem Update im Oktober 2021 wurden der App Umfragemodule zu verschiedenen, für die Pandemieforschung relevanten Fragestellungen, hinzugefügt. Die hier bereitgestellten Daten entstammen der Teilstudie "Erleben und Verhalten in der Pandemie" die momentan als eines von drei Befragungsmodulen innerhalb der CDA durchgeführt wird. Sie basiert auf dem <a href="https://projekte.uni-erfurt.de/cosmo2020/web/">COVID-19 Snapshot Monitoring (COSMO)</a>, einem sich wiederholenden querschnittlichen Monitoring von Wissen, Risikowahrnehmung, Schutzverhalten und Vertrauen während des aktuellen COVID-19 Ausbruchsgeschehens (Betsch et al., 2022). In dieser Studie wollen das Robert Koch-Institut sowie führende Verhaltensforscher:innen erfahren, wie sich die Bevölkerung unter pandemischen Bedingungen verhält. Durch die Beantwortung regelmäßiger Fragebögen können Teilnehmer:innen dabei helfen, Strategien zur Bekämpfung des Coronavirus zu optimieren. Die Forscher:innen wollen hierzu mehr über den Arbeitsalltag, die persönliche Belastung und die Risikowahrnehmung lernen. Die hier bereitgestellten Daten dienen der Reproduktion aller Ergebnisse in der Studie "From Delta to Omicron: The role of individual factors and social context in compliance with pandemic regulations and recommendations" (Sprengholz et al., 2022, in Begutachtung). Es werden hier daher jene Datenpunkte aus der Studie "Erleben und Verhalten in der Pandemie" bereitgestellt, die zu diesem Zweck benötigt werden. Das heißt auch, dass nicht alle im Rahmen dieser Studie erhobenen Datenpunkte, beispielsweise Informationen zu COVID-19-Testergebnissen, sowie alle Zeitpunkte der Erhebung im vorliegenden Datensatz enthalten sind. Ein Link zur oben genannten Publikation folgt nach der Veröffentlichung in einer Fachzeitschrift.

opencc-by-4.0Jun 2022View details →

Double-directional Multipath Data at 140 GHz

This data set contains 140 GHz double-directional path data in an indoor hall environment. Details of the environment and data format are available in .txt and .ppt files in the same package as data. The data were derived from channel sounding along with a measurement-based ray-launcher, which is elaborated in the following paper. M. F. de Guzman, P. Koivumäki and K. Haneda, "Double-directional multipath data at 140 GHz derived from measurement-based ray-tracer," in Proc. 2022 Vehicular Technology Conference, Helsinki, Finland, June 2022. @INPROCEEDINGS{deGuzman22_VTCS,    author={de Guzman, Mar Francis and Koivum\"{a}ki, Pasi and Haneda, Katsuyuki},    booktitle={2022 95th Veh. Tech. Conf. (VTC2022-Spring)},    title={Double-directional multipath data at 140 {GHz} derived from measurement-based ray-launcher},    year={2022},    address={Helsinki, Finland},    month={June},    pages={1-6}, }

Actitudes sociales y políticas en Andalucía: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua.

Descripción en español (See English description below) Título base de datos: Actitudes sociales y políticas en Andalucía: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua.   Proyecto: PRY025/19 “Democracia y actitudes populistas en Andalucía: un análisis comparado multidisciplinar”, radicado en la Universidad de Málaga y dirigido por Manuel Arias Maldonado. Autores de la base de datos: José Javier Olivas Osuna (UNED y LSE), Manuel Arias Maldonado (Universidad de Málaga), Encarnación Hidalgo Tenorio (Universidad de Granada), Manuel Moyano (Universidad de Córdoba), Elena Llorca (Universidad de Alicante), Astrid Barrio (Universidad de Valencia) y Enrique Clari (Universidad de Valencia y UNED). Autor de correspondencia (para aclarar dudas técnicas): José Javier Olivas Osuna, jjolivas@poli.uned.es , Facultad de Ciencias Políticas y Sociología, Universidad Nacional de Educación a Distancia, UNED, Obispo Trejo S/N, 28004, Madrid Como citar base de datos: Olivas Osuna, J.J., Arias, M., Hidalgo-Tenorio, E.; Moyano, M., Llorca, E., Barrio, A. y Clari, E. (2022) Actitudes sociales y políticas en Andalucía: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua. Centro de Estudios Andaluces: Sevilla.  Fecha de creación: 8 junio 2022 Cobertura geográfica: Andalucía, España Datos recolectados: octubre 2021 Version: 1 Licencia: esta base de datos es publicada con acceso abierto (Open Access) bajo la licencia Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND): https://creativecommons.org/licenses/by-nc-nd/4.0/ Es decir, los datos están a disposición de otros investigadores para ser explotados pero estos deberán realizar la pertinente atribución a los autores de esta base de datos y no deberá ser usada para fines comerciales sino de investigación académica. Descripción del contenido y metodología: Esta nueva base de datos (ficheros “BBDD_POPULISMO_ANDALUCIA.xls” y “and.csv”) ha sido elaborada con los resultados de una encuesta telefónica (método CATI, Computer Assisted Telephone Interviewing) administrada por el Área de Estudios Estadísticos de COTESA, entre el 5 y 25 de octubre de 2021. Se entrevistaron a 1.500 personas en Andalucía con un muestreo probabilístico estratificado en virtud del sexo y la edad (7 grupos). En julio de 2021 COTESA realizó un pretest que no detectó problemas. La unidad informante fueron las personas mayores de edad y residentes en cada una de las 8 provincias de esta Comunidad Autónoma (ver fichero adjunto “Diseño Muestral UMA_Andalucia.xls”). Los datos fueron registrados por los entrevistadores de COTESA siguiendo un cuestionario (ver fichero “Cuestionario Actitudes Populistas en Andalucía final.docx”). Los valores perdidos (“missing values”) han sido recodificados como “99”. El fichero de la base de datos contiende además una pestaña con la descripción de las variables para facilitar el trabajo de los investigadores).  Esta base de datos se creo en paralelo a otra base de datos equivalente en Cataluña: " Actitudes sociales y políticas en Cataluña: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua." Los datos son compatibles y se pueden realizar comparaciones entre ambas (los ficheros "esp" fusiona las dos bases de datos) Esta base de datos contiene más de 100 items/variables reflejan que reflejan las respuestas de los entrevistados a un cuestionario que incluye muchas preguntas originales diseñadas ad-hoc por los autores, a través de sucesivas reuniones e intercambios durante 2021, así como una serie de preguntas correspondientes a escalas desarrolladas por otros autores previamente. Entre estas caben destacar escalas de populismo (Akkerman et al. 2014; Olivas Osuna et al. en elaboración), narcisismo colectivo (Golec de Zavala et al. 2009), creencia en teorías conspirativas (Brotherton et al. 2013), justificación de la violencia política (Belanger et al. 2019) y proximidad a una red radicalizada (Moyano 2011). Reconocimientos y fuentes de financiación: La encuesta fue financiada por el proyecto “Democracia y actitudes populistas en Andalucía: un análisis comparado multidisciplinar” (PRY025/19). Sin embargo la participación de algunos miembros del equipo en el diseño de la encuesta y depuración de la base de datos ha sido posible también gracias al “Proyecto Comparado Interdisciplinar de Populismo y Secesionismo” (2018-T1/COC-10152), financiado por el Programa de Atracción de Talento Investigador de la Comunidad de Madrid, y al proyecto “Populismo y Fronteras: un Estudio Comparado de Discursos y Actitudes en la Oferta y Demanda Populista” (PID2020-113182RA-I00), financiado por la Agencia Estatal de Investigación del Ministerio de Ciencia e Innovación de España. Cabe reconocer también el apoyo teórico y metodológico en el proceso de diseño del cuestionario a Andrés Santana y José Rama (ambos profesores en la Universidad Autónoma de Madrid). También se agradece el trabajo de Manuel Martín y Juan Ramón Barrada, que junto con Manuel Moyano y José Javier Olivas trabajaron desde noviembre de 2019 en el diseño y testeo de muchos de los ítems sobre populismo incluidos en el cuestionario (dentro del marco del proyecto 2018-T1/COC-10152). Ficheros adjuntos:  * Readme Actitudes sociales y políticas en Andalucía.txt * BBDD_POPULISMO_ANDALUCIA.xls (base de datos con pestaña descripción variables) * and.csv (base de datos en formato csv) * Cuestionario Actitudes Populistas en Andalucia final.docx (cuestionario adminstrado) * Diseño muestral UMA_Andalucía.xls (explicación de los criterios de muestreo)     English description: Database title: Social and political attitudes in Andalusia: telephone interviews with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language. Project: PRY025/19 "Democracy and populist attitudes in Andalusia: a multidisciplinary comparative analysis", based at the University of Malaga and directed by Manuel Arias Maldonado. Database authors: José Javier Olivas Osuna (UNED and LSE), Manuel Arias Maldonado (University of Málaga), Encarnación Hidalgo Tenorio (University of Granada), Manuel Moyano (University of Córdoba), Elena Llorca (University of Alicante), Astrid Barrio (University of Valencia) and Enrique Clari (University of Valencia and UNED). Correspondence author (for technical questions): José Javier Olivas Osuna, jjolivas@poli.uned.es , Facultad de Ciencias Políticas y Sociología, Universidad Nacional de Educación a Distancia, UNED, Obispo Trejo S/N, 28004, Madrid. How to cite database: Olivas Osuna, J.J., Arias, M., Hidalgo-Tenorio, E.; Moyano, M., Llorca, E., Barrio, A. and Clari, E. (2022) Social and political attitudes in Andalusia: telephone interview with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language. Andalusian Studies Centre: Seville.  Date of creation: 8 June 2022 Geographical coverage: Andalusia, Spain Data collected: October 2021 Version: 1 Licence: this database is published with Open Access under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ That is, the data is available for other researchers to exploit but they must make the appropriate attribution to the authors of this database and it must not be used for commercial purposes but for academic research. Description of content and methodology: This new database (files "BBDD_POPULISMO_ANDALUCIA.xls" and "and.csv") has been elaborated with the results of a telephone survey (CATI method, Computer Assisted Telephone Interviewing) administered by the Statistical Studies Area of COTESA, between 5 and 25 October 2021. A total of 1,500 people were interviewed in Andalusia with a probability sample stratified by sex and age (7 groups). A pre-test was conducted by COTESA in July 2021 and no problems were detected. The respondent unit was persons of legal age and resident in each of the 8 provinces of this Autonomous Community (see attached file "Diseño Muestral UMA_Andalucia.xls"). The data were recorded by COTESA interviewers following a questionnaire (see file "Cuestionario Actitudes Populistas en Andalucía final.docx"). Missing values have been re-coded as "99". The database file also contains a tab with the description of the variables to facilitate the work of the researchers).  This database was created in parallel to another equivalent database in Catalonia: "Social and political attitudes in Catalonia: telephone interview with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language". The data of the Andalusian and Spanish databases are compatible and comparable (the "esp" files merge the two databases). This database contains more than 100 items/variables reflecting respondents' answers to a questionnaire that includes many original questions designed ad-hoc by the authors, through successive meetings and exchanges during 2021, as well as a number of questions corresponding to scales previously developed by other authors. These include scales on populism (Akkerman et al. 2014; Olivas Osuna et al. in elaboration), collective narcissism (Golec de Zavala et al. 2009), belief in conspiracy theories (Brotherton et al. 2013), justification of political violence (Belanger et al. 2019) and proximity to a radicalised network (Moyano 2011). Acknowledgements and funding sources: The survey was funded by the project "Democracy and populist attitudes in Andalusia: a multidisciplinary comparative analysis" (PRY025/19). However, the participation of some members of the team in the design of the survey and the cleaning of the database has also been possible thanks to the "Comparative Interdisciplinary Project on Populism and Secessionism" (2018-T1/COC-10152), funded by the Research Talent Attraction Programme of the Community of Madrid, and to the project "Populism and Borders: A Comparative Study of Discourses and Attitudes in Populist Supply and Demand" (PID2020-113182RA-I00), funded by the State Research Agency of the Spanish Ministry of Science and Innovation. The theoretical and methodological support in the questionnaire design process is also acknowledged to Andrés Santana and José Rama (both professors at the Universidad Autónoma de Madrid). Thanks are also due to Manuel Martín and Juan Ramón Barrada, who together with Manuel Moyano and José Javier Olivas worked since November 2019 on the design and testing of many of the items on populism included in the questionnaire (within the framework of project 2018-T1/COC-10152). Attached files: * Readme Actitudes sociales y políticas en Andalucía.txt * BBDD_POPULISMO_ANDALUCIA.xls (database with a tab with the description of variables) * and.csv (database in csv format) * Cuestionario Actitudes Populistas en Andalucia final.docx (questionnaire) * Diseño muestral UMA_Andalucía.xls (explanation of the sampling method)

Actitudes sociales y políticas en Cataluña: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua.

Descripción en español (See English description below) Título base de datos: Actitudes sociales y políticas en Cataluña: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua. Proyecto: PRY025/19 “Democracia y actitudes populistas en Andalucía: un análisis comparado multidisciplinar”, radicado en la Universidad de Málaga y dirigido por Manuel Arias Maldonado. Autores de la base de datos: José Javier Olivas Osuna (UNED y LSE), Manuel Arias Maldonado (Universidad de Málaga), Encarnación Hidalgo Tenorio (Universidad de Granada), Manuel Moyano (Universidad de Córdoba), Elena Llorca (Universidad de Alicante), Astrid Barrio (Universidad de Valencia) y Enrique Clari (Universidad de Valencia y UNED). Autor de correspondencia (para aclarar dudas técnicas): José Javier Olivas Osuna, jjolivas@poli.uned.es , Facultad de Ciencias Políticas y Sociología, Universidad Nacional de Educación a Distancia, UNED, Obispo Trejo S/N, 28004, Madrid. Como citar base de datos: Olivas Osuna, J.J., Arias, M., Hidalgo-Tenorio, E.; Moyano, M., Llorca, E., Barrio, A. y Clari, E. (2022) Actitudes sociales y políticas en Cataluña: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua. Centro de Estudios Andaluces: Sevilla.  Fecha de creación: 8 junio 2022 Cobertura geográfica: Cataluña, España Datos recolectados: octubre 2021 Version: 1 Licencia: esta base de datos es publicada con acceso abierto (Open Access) bajo la licencia Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND): https://creativecommons.org/licenses/by-nc-nd/4.0/ Es decir, los datos están a disposición de otros investigadores para ser explotados pero estos deberán realizar la pertinente atribución a los autores de esta base de datos y no deberá ser usada para fines comerciales sino de investigación académica. Descripción del contenido y metodología: Esta nueva base de datos (ficheros “BBDD_POPULISMO_CATALUÑA.xls” y “cat.csv”) ha sido elaborada con los resultados de una encuesta telefónica (método CATI, Computer Assisted Telephone Interviewing) administrada por el Área de Estudios Estadísticos de COTESA, entre el 5 y 25 de octubre de 2021. Se entrevistaron a 1.500 personas en Cataluña con un muestreo probabilístico estratificado en virtud del sexo y la edad (7 grupos). La unidad informante fueron las personas mayores de edad y residentes en cada una de las 4 provincias de esta Comunidad Autónoma (ver fichero adjunto “Diseño Muestral UMA_Cataluña.xls”). En julio de 2021 COTESA realizó un pretest que no detectó problemas. Los datos fueron registrados por los entrevistadores de COTESA siguiendo un cuestionario (ver ficheros “Cuestionario Actitudes Populistas en Cataluña final cat.docx” y “Cuestionario Actitudes Populistas en Cataluña final esp.docx). Los entrevistadores daban la opción de elegir el idioma en el que se desarrollaba la entrevista. Los valores perdidos (“missing values”) han sido recodificados como “99”. El fichero de la base de datos contiende además una pestaña con la descripción de las variables para facilitar el trabajo de los investigadores).  Esta base de datos se creo en paralelo a otra base de datos equivalente en Andalucía: "Actitudes sociales y políticas en Andalucía: entrevista telefónica a 1500 personas sobre populismo, inmigración, modelo territorial, instituciones españolas, pensamiento conspirativo, activismo, narcisismo colectivo y lengua". Los datos son compatibles y se pueden realizar comparaciones (los ficheros "esp" fusionan las dos bases de datos). Esta base de datos contiene más de 100 items/variables reflejan que reflejan las respuestas de los entrevistados a un cuestionario que incluye muchas preguntas originales diseñadas ad-hoc por los autores, a través de sucesivas reuniones e intercambios durante 2021, así como una serie de preguntas correspondientes a escalas desarrolladas por otros autores previamente. Entre estas caben destacar escalas de populismo (Akkerman et al. 2014; Olivas Osuna et al. en elaboración), narcisismo colectivo (Golec de Zavala et al. 2009), creencia en teorías conspirativas (Brotherton et al. 2013), justificación de la violencia política (Belanger et al. 2019) y proximidad a una red radicalizada (Moyano 2011). Reconocimientos y fuentes de financiación: La encuesta fue financiada por el proyecto “Democracia y actitudes populistas en Andalucía: un análisis comparado multidisciplinar” (PRY025/19). Sin embargo la participación de algunos miembros del equipo en el diseño de la encuesta y depuración de la base de datos ha sido posible también gracias al “Proyecto Comparado Interdisciplinar de Populismo y Secesionismo” (2018-T1/COC-10152), financiado por el Programa de Atracción de Talento Investigador de la Comunidad de Madrid, y al proyecto “Populismo y Fronteras: un Estudio Comparado de Discursos y Actitudes en la Oferta y Demanda Populista” (PID2020-113182RA-I00), financiado por la Agencia Estatal de Investigación del Ministerio de Ciencia e Innovación de España. Cabe reconocer también el apoyo teórico y metodológico en el proceso de diseño del cuestionario a Andrés Santana y José Rama (ambos profesores en la Universidad Autónoma de Madrid). También se agradece el trabajo de Manuel Martín y Juan Ramón Barrada, que junto con Manuel Moyano y José Javier Olivas trabajaron desde noviembre de 2019 en el diseño y testeo de muchos de los ítems sobre populismo incluidos en el cuestionario (dentro del marco del proyecto 2018-T1/COC-10152). Ficheros adjuntos:  * Readme Actitudes sociales y políticas en Cataluña.txt * BBDD_POPULISMO_CATALUÑA.xls (base de datos con pestaña descripción variables) * cat.csv (base de datos en formato csv) * Cuestionario Actitudes Populistas en Cataluña final esp.docx (cuestionario en catalán) * Cuestionario Actitudes Populistas en Cataluña final cat.docx (cuestionario en español) * Diseño muestral UMA_Cataluña.xls (explicación de los criterios de muestreo) *esp.xls and esp.csv (bases de datos fusionadas: Andalucía y Cataluña)   English description: Database title: Social and political attitudes in Catalonia: telephone interviews with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language. Project: PRY025/19 "Democracy and populist attitudes in Andalusia: a multidisciplinary comparative analysis", based at the University of Malaga and directed by Manuel Arias Maldonado. Database authors: José Javier Olivas Osuna (UNED and LSE), Manuel Arias Maldonado (University of Málaga), Encarnación Hidalgo Tenorio (University of Granada), Manuel Moyano (University of Córdoba), Elena Llorca (University of Alicante), Astrid Barrio (University of Valencia) and Enrique Clari (University of Valencia and UNED). Corresponding author: José Javier Olivas Osuna, jjolivas@poli.uned.es , Facultad de Ciencias Políticas y Sociología, Universidad Nacional de Educación a Distancia, UNED, Obispo Trejo S/N, 28004, Madrid. How to cite database: Olivas Osuna, J.J., Arias, M., Hidalgo-Tenorio, E.; Moyano, M., Llorca, E., Barrio, A. and Clari, E. (2022) Social and political attitudes in Catalonia: telephone interview with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language. Andalusian Studies Centre: Seville.  Date of creation: 8 June 2022 Geographical coverage: Catalonia, Spain Data collected: October 2021 Version: 1 License: this database is published with Open Access under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license: https://creativecommons.org/licenses/by-nc-nd/4.0/ That is, the data is available for other researchers to exploit but they must make the appropriate attribution to the authors of this database and it should not be used for commercial purposes but for academic research. Description of content and methodology: This new database (files "BBDD_POPULISMO_CATALUÑA.xls" and "cat.csv") has been elaborated with the results of a telephone survey (CATI method, Computer Assisted Telephone Interviewing) administered by the Statistical Studies Area of COTESA, between 5 and 25 October 2021. A total of 1,500 people were interviewed in Catalonia with a probability sample stratified by sex and age (7 groups). The respondent unit was persons of legal age and resident in each of the 4 provinces of this Autonomous Community (see attached file "Sample Design UMA_Catalonia.xls"). In July 2021 COTESA carried out a pre-test that did not detect any problems. Data were recorded by COTESA interviewers following a questionnaire (see files "Cuestionario Actitudes Populistas en Cataluña final cat.docx" and "Cuestionario Actitudes Populistas en Cataluña final esp.docx). Interviewers were given the option of choosing the language in which the interview was conducted. Missing values have been re-coded as "99". The database file also contains a tab with the description of the variables to facilitate the work of the researchers).  This database was created in parallel to another equivalent database in Andalusia: "Social and political attitudes in Andalusia: telephone interviews with 1500 people on populism, immigration, territorial model, Spanish institutions, conspiratorial thinking, activism, collective narcissism and language". The data of the Andalusian and Spanish databases are compatible and comparable (the "esp" files merge the two databases). This database contains more than 100 items/variables reflecting respondents' answers to a questionnaire that includes many original questions designed ad-hoc by the authors, through successive meetings and exchanges during 2021, as well as a number of questions corresponding to scales previously developed by other authors. These include scales on populism (Akkerman et al. 2014; Olivas Osuna et al. forthcoming), collective narcissism (Golec de Zavala et al. 2009), belief in conspiracy theories (Brotherton et al. 2013), justification of political violence (Belanger et al. 2019) and proximity to a radicalised network (Moyano 2011). Acknowledgements and funding sources: The survey was funded by the project "Democracy and populist attitudes in Andalusia: a multidisciplinary comparative analysis" (PRY025/19). However, the participation of some members of the team in the design of the survey and the cleaning of the database has also been possible thanks to the "Comparative Interdisciplinary Project on Populism and Secessionism" (2018-T1/COC-10152), funded by the Research Talent Attraction Programme of the Community of Madrid, and to the project "Populism and Borders: A Comparative Study of Discourses and Attitudes in Populist Supply and Demand" (PID2020-113182RA-I00), funded by the State Research Agency of the Spanish Ministry of Science and Innovation. The theoretical and methodological support in the questionnaire design process is also acknowledged to Andrés Santana and José Rama (both professors at the Universidad Autónoma de Madrid). Thanks are also due to Manuel Martín and Juan Ramón Barrada, who together with Manuel Moyano and José Javier Olivas worked since November 2019 on the design and testing of many of the items on populism included in the questionnaire (within the framework of project 2018-T1/COC-10152). Attached files:  * Readme Actitudes sociales y políticas en Cataluña.txt * BBDD_POPULISMO_CATALUÑA.xls (base de datos con pestaña descripción variables) * cat.csv (base de datos en formato csv) * Cuestionario Actitudes Populistas en Cataluña final esp.docx (cuestionario en catalán) * Cuestionario Actitudes Populistas en Cataluña final cat.docx (cuestionario en español) * Diseño muestral UMA_Cataluña.xls (explicación de los criterios de muestreo) *esp.xls and esp.csv (merged databases: Andalusia + Catalonia)

Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon

The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020). All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment). Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). There are three csv files: ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA). ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was: `magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg` The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right. ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv. There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020). A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( `mogrify -resize 2000x2000`). For full size images, please download the jpegs directly from NOAA.

opencc-by-sa-4.0Dec 2017View details →

DU00-W-212 airfoil polars, sinusoidal inflow

Averaged lift and drag coeffficients of a DU00-W-212 profile in sinusoidally varying inflow (turbulence level approx. 5%) generated with an active grid at Reynolds numbers 500,000 and 900,000. Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord. Standard wind tunnel corrections according to Allen & Vincenti are applied. Data sets 182, 183, 186, 188: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side Data sets 183, 188, 242, 245: measured starting at positive angles of attack (AOA) to negative AOAs Data sets 182, 186, 243, 244: measured starting at negative angles of attack (AOA) to positive AOAs The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).

opencc-by-sa-4.0Dec 2017View details →

DU00-W-212 airfoil polars, laminar inflow

Averaged lift and drag coeffficients of a DU00-W-212 profile in laminar flow (background turbulence level approx. 0.3%) at Reynolds numbers 500,000 and 900,000. Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord. Standard wind tunnel corrections according to Allen & Vincenti are applied. Data sets 119, 120, 121, 122: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side Data sets 104, 107, 119, 122: measured starting at negative angles of attack (AOA) to positive AOAs Data sets 105, 106, 120, 121: measured starting at positive angles of attack (AOA) to negative AOAs The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).

opencc-by-sa-4.0Dec 2017View details →

DU00-W-212 airfoil polars, mimicked DanAero inflow

Averaged lift and drag coeffficients of a DU00-W-212 profile in turbulent inflow generated with an active grid at Reynolds numbers 500,000 and 900,000. The inflow pattern was mimicked from measurements a the blade with a 5-hole pressure probe performed in teh DanAero project. Data is obtained with a three-component load cell and via integration of 48 scanned pressure tabs along the chord. Standard wind tunnel corrections according to Allen & Vincenti are applied. Data sets 203, 204, 210, 211: flow tripped on the surface at 1.5% chord on upper airfoil side, 10% chord on lower airfoil side Data sets 203, 211, 225, 230: measured starting at positive angles of attack (AOA) to negative AOAs Data sets 204, 210, 224, 239: measured starting at negative angles of attack (AOA) to positive AOAs The experiment was performed within in the EU-funded project AVATAR (www.eera-avatar.eu).

opencc-by-4.0Apr 2024View details →

City features collection

<div> <div># City features collection</div> <div>A collection of features for ~700 European cities, for the reference year 2018.</div> <div>## Features</div> <div>The features are divided in three main thematic areas: land, climate and socioeconomic characteristics. Find more information about the features in the codebook `cities_features_collection_codebook.csv`.</div> <div>Codelists for categorical features are in the same folder `codelist_<feature>.csv`.</div> <div>## Cities</div> <div>City selection (and outline polygon) is taken from the Eurostat Urban Atlas. More information [here](https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit). The original list of cities with geometries can be downloaded at these links:</div> <div>- EPSG:4326 (WGS84) <https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_4326_CITIES.geojson></div> <div>- EPSG:3035 <https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_3035_CITIES.geojson></div> <div>Note: the dataset `city_features_collection.geojson` only contains the city outline in CRS EPSG:4326.</div> <div>## Example usage</div> <div>Clustering analysis of European cities: check out this interactive demo notebook: `notebooks\demo\cities_clustering_interactive_demo.ipynb`.</div> </div>

opencc-by-4.0Apr 2024View details →

Data from: ChatGPT performance on radiation technologist and therapist entry to practice exams

This dataset contains the data needed to reproduce all results and figures described in "ChatGPT performance on radiation technologist and therapist entry to practice exams". Details about the data collection can be found in the paper referenced below. Briefly, ChatGPT (GPT-4) was prompted with multiple choice questions from 4 practice exams provided by the Canadian Association of Medical Radiation Technologists (CAMRT). ChatGPT was promted with the questions from each exam 5 times between July 17 and August 13, 2023. Table 1, below, provides details about the dates for data collection. Variable descriptions <ul> <li><code>question</code>: Question number, provided by CAMRT. Skipped question numbers indicate image-based questions that were excluded from the study.</li> <li><code>discipline</code>: Indicates the CAMRT exam discipline, abbreviated as follows  <ul> <li>RAD: radiological technology</li> <li>MRI: magnetic resonance</li> <li>NUC: nuclear medicine</li> <li>RTT: radiation therapy</li> </ul> </li> <li><code>question_type</code>: Indicates the type of competency being assessed by the question (Knowledge, Application, or Critical thinking). Competency categories were assigned by CAMRT.</li> <li><code>corrrect_response</code>: The correct multiple choice response ("A", "B", "C", or "D"), assigned by CAMRT.</li> <li><code>attempt1-5</code>: ChatGPT's response to the multiple choice questions for attempts 1 through 5, indicated using the letters "A", "B", "C", or "D". In a few cases, ChatGPT did not provide a reference to a multiple choice response and "NA" is recorded in the dataset. </li> </ul> Note: The long-form questions from CAMRT and answers provided by ChatGPT are not available as a part of this dataset. Table 1: Dates for data collection <table> <tbody> <tr> <td> </td> <td>Attempt 1</td> <td>Attempt 2</td> <td>Attempt 3</td> <td>Attempt 4</td> <td>Attempt 5</td> </tr> <tr> <td>Radiological technology</td> <td>2 Aug 2023</td> <td>2 Aug 2023</td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>11 Aug 2023</td> </tr> <tr> <td>Magnetic resonance </td> <td>17 Jul 2023</td> <td>18 Jul 2023</td> <td>18 Jul 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td>Nuclear medicine</td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td>Radiation therapy</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>13 Aug 2023</td> <td>13 Aug 2023</td> </tr> </tbody> </table>

opencc-by-4.0Apr 2024View details →

A Benchmark dataset on Semantic Change in Scholarly Publications on Disability

This is a benchmark dataset for semantic shift detection in disability-related corpora, including collected title and abstract text from PubMed and ArXiv, annotation sets based on domain experts and LLMs, and extracted KGs (Wikidata entity claims). The corpus from PubMed covers the period from the 1900s to 2023, while the corpus from ArXiv covers the period from the 1990s to 2023. The corpus was filtered based on 16 disability-related target words. In the annotation sets, '1' indicates that a semantic shift occurred for a target word, while '0' indicates the opposite. In particular, the LLM-based annotation sets include their generated text, and we used the Llama2 and GPT-4 models. '7b' refers to the parameter size of the Llama2 model. Graph_data.zip contains Wikidata entity claims.