Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
64
datasets available to search
ShareScore release 0.9.0
Dataset results
64 results for “Cultural heritage”
Participatory activities good practices in the field of cultural heritage (REACH project)
<p>The REACH repository of good practices comprises over a hundred and twenty records of European and extra European participatory activities in the field of cultural heritage, with an emphasis on small-scale, localised examples, but including also larger collaborative projects and global or distributed online initiatives. Located in over twenty different countries, the activities showcased here cover a wide variety of topics and themes, from urban, rural and institutional heritage to indigenous and minority heritage; from preservation, and management to use and re-use of cultural heritage. This easy-to-use collection of good practices offers professionals, practitioners, researchers and citizens useful information about activities which could be transferred, adapted or replicated in new contexts.</p>
Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</p> <ul> <li>Date of data collection: 27/11/2018</li> <li>Geographic location of data collection: Salerno, Italy. The venue of the data collection is <em>Salone dei marmi, Palazzo di Città</em>, via Roma, 84121 Salerno, Italy </li> <li>Activity of data collection: Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions </li> <li>Methods for collection/generation of data: see the methodology section in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh</li> <li>Researcher translating to English, transcribing data in the digital tabular dataset, and cleaning the data: Nadia Pintossi</li> <li>Original language of the data: English, Italian, and mix of English and Italian</li> </ul>
SILKNOW Multimodal Cultural Heritage Dataset
<p>SILKNOW Multimodal Cultural Heritage Dataset. Includes text descriptions, images, labels, and predictions made by individual modality classifiers.</p> <p>The data resulted from an export of the SILKNOW Knowledge Graph. See: <a href="https://zenodo.org/record/5743090">https://zenodo.org/record/5743090</a></p> <p>Repository with code using this dataset available at: <a href="https://github.com/silknow/multimodal_cultural_heritage">https://github.com/silknow/multimodal_cultural_heritage</a></p>
Sutra to DNA : An interdisciplinary approach to cultural heritage preservation
<p>Materials pertaining to the project Sutra2DNA. We have chosen the Diamond Sūtra as the first Buddhist text to be encoded in DNA. The DNA is encased in metal capsule, which in turn are housed in specially designed, 3D printed miniature stūpas.</p> <p>A QR code with the DOI of this archive will be printed on the miniature stūpas. Interested finders who have arrived here because of the QR code should first read the final report of the project.</p> <p>The upload contains the following files:</p> <p>* Sutra2DNA_finalReport.odt<br> Explains the project and outcome.</p> <p>* diamondSutraT235_textOnly.txt<br> The Chinese text of the Diamond Sūtra as provided to the encoder.</p> <p>* Twist-Decoding Instructions-DS14.pdf<br> Contains some of the decoding options available in 2022.</p> <p>* twist_bioscience_codec_v2_external.tar.gz<br> Docker file with the decoder for the DNA sequence as provided by TwistBioscience. </p> <p>* miniatureStupa_STLfiles/*.stl<br> Three files to 3D print the three parts of the miniature stūpa that houses the capsule with the DNA.</p> <p>* dna-fountain-master.zip<br> Justin Brody's porting of Erlich's fountain code algorithm (2017) to Python 3. (2022-06 code from https://github.com/jdbrody/dna-fountain)</p>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
Recommendation dataset for Cultural Heritage
<p>A set of 24 JSON files that each represents a recommendation category and contains a set of items with all necessary information as extracted by the <a href="https://cuhe.in-two.com/">CUHE</a> platform. For each of the items this file also contains the apiResponse element, which corresponds to the complete API response from <a href="https://www.europeana.eu/">Europeana</a>. The Europeana API description is available <a href="https://pro.europeana.eu/page/intro">here</a>, the apiResponse follows the EDM (Europeana Data Model) specification which is available <a href="https://pro.europeana.eu/page/intro#edm">here</a>.</p> <p>The <a href="https://cuhe.in-two.com/">CUHE project</a> has indirectly received funding from the European Union's Horizon 2020 research and innovation action programme, via the AI4Media Open Call #1 issued and executed under the <a href="https://www.ai4media.eu/">AI4Media project</a> (Grant Agreement no. 951911).</p>
Experimental Protocol on Creative Engagement and Meaning Creation in Interactive Experiences for Cultural Heritage. Tabular Data
<p>The following document contains the material produced for the testing of the "caring prototype" <em>MyTISSE</em>, an interactive user experience on Matisse's painting <em>Bathers by a rive</em>r (Art Institute of Chicago). Focusing on the change of the pigments made by the artist during the gestation of this canvas, the UX exploits creative engagement to catalyze meaning creation processes.</p> <p>This pubblication contains the data collected through the form. All the participants agreed to the reuse and sharing of these data for academic purposes, filling in an <em>ad hoc </em>privacy form.</p>
PROCRAFT Final Meeting - Aeronautical cultural heritage in Greece by Vasilike Argyropoulos
Open the record for dataset details and reuse information.
ConnectionChecker: Results for Cultural Heritage Datasets
<p>ConnectionChecker: Results for Cultural Heritage Datasets. We have evaluated two datasets of CH domain:</p> <p>1.A subset of the dataset Persons of National Library of Netherlands with 3,000,000 triples, 698,527 entities, and 535,407 owl:sameAs relationships.</p> <p>2.The dataset World War I as LOD from the Universities of Aalto and Helsinki, Finland with 47,176 triples, 11,339 entities and 547 owl:sameAs relationships.</p> <p>ne can download the triples of these datasets, their metadata in CSV and VoID format, the provenance of entities and the inferred sameAs relationships. Moreover, it contains screenshots showing the produced visualizations for these datasets.</p> <p><a href="https://demos.isl.ics.forth.gr/ConnectionChecker">https://demos.isl.ics.forth.gr/ConnectionChecker</a></p>
Datasets for Linked Open Data Instance Level Analysis for Cultural Heritage
<p>This is the datasets used for Linked Open Data instant level quality analysis for cultural heritage (2020). 7Z and ZIP versions are available for both Excel 2006 and R 4.0.3. The compressed files include, Excel spreadsheets (.xlsx, .csv), VBA scripts (.bas), and R scripts (.r).</p> <p>Please read the full documentation in Linked_Open_Data_Instance_Level_Analysis_Procedure.pdf.</p>
A survey of access to the digital collections of 195 UK GLAMs across internal and external platforms - Appendix 1 for A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK
<p>Created for the<a href="https://doi.org/10.5281/zenodo.6242611"> 'A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK' </a>report, this sample replicates and expands the Open galleries, libraries, archives and museums (GLAMs) Survey data extraction and methodology to include a range of GLAMs across the UK and new data points. The initial sample of 350 organisations included Independent Research Organisations (IROs) and Research Centre Institutes (RCIs), GLAMs associated with <a href="https://www.nationalcollection.org.uk/">Towards a National Collection</a> Foundation and Discovery projects, UK GLAMs in the Open GLAM Survey, and other UK GLAMs and related organisations. An initial review was performed to identify and remove organisations outside the scope of inquiry (<em>e.g.</em>, no permanent collections). The final sample included 195 organisations.</p> <p>From the final sample, 24 are IROs (all RCIs were removed). Another 32 are Universities (including GLAMs within universities). This brings the total number of organisations eligible for AHRC funding to 56 (or 28.6%). The remaining 140 include public and private GLAMs at national, regional and local levels (<em>e.g., </em>councils, historic buildings) and research initiatives or data aggregators (<em>e.g.,</em> Portable Antiquities Scheme, Culture Grid, Archaeology Data Service). Organisations are distributed across the UK as follows: Channel Islands (1 total); England (154 total); Isle of Man (1 total); Northern Ireland (5 total); Scotland (28 total); Wales (6 total).</p> <p>A survey of the copyright and open access policies of 63 GLAMs from the UK GLAM Sample is available on Zenodo at: <a href="https://doi.org/10.5281/zenodo.6242559">https://doi.org/10.5281/zenodo.6242559</a></p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Wikidata's linked data for cultural heritage digital resources: an evaluation based on the Europeana Data Model
<p>Wikidata is an open data source with many potential applications. Our study aims to evaluate the usability of Wikidata as a linked data source for acquiring richer descriptions of digital objects within the context of Europeana, a data aggregator from the cultural heritage domain. Specifically, we aim to crawl and convert Wikidata using the standard approaches and operations developed for the (Semantic) Web of Data, i.e. using technologies like linked data consumption and RDF(S)/OWL ontology expression and reasoning. We also seek to re-use existing “semantic” specifications, such as conversions to and from generic data models like Schema.org and SKOS. We have developed an experimental set-up and accompanying software to test the feasibility of this approach. We conclude that Wikidata’s linked data is able to express an interesting level of semantics for cultural heritage, but quality can still be improved and a human operator still must assist linked data applications to interpret Wikidata’s RDF.</p>
Our Mythical History: Children's and Young Adults' Culture in Response to the Heritage of Ancient Greece and Rome
<p>A short movie from the international conference <strong><em>Our Mythical History: Children’s and Young Adults’ Culture in Response to the Heritage of Ancient Greece and Rome </em></strong>held at the Faculty of "Artes Liberales", University of Warsaw, May 22-26, 2019</p> <p>available at <a href="https://www.youtube.com/watch?v=jVeEjWSCXD8">https://www.youtube.com/watch?v=jVeEjWSCXD8</a> </p> <p>Music: <em>Brave </em>by WildKitty Tunes, Video: Mirosław Kaźmierczak, Coordination: Katarzyna Marciniak</p> <p>Art works used in the movie: Matylda Tracewska, Zbigniew Karaszewski</p>
CHAS - Cultural Heritage Architectural Segmentation dataset
<p>CHAS is a point cloud dataset from cultural heritage aimed to provide data for semantic segmentation techniques. The data in this repository were generated by terrestrial laser scanning and pictures from aerial survey using UAV. This dataset comprises relevant buildings representing religious and colonial Brazilian architecture. We hope to allow future research in point cloud segmentation and automatic Building Information Modeling.</p> <p>We use the .7z file format to compact the clouds, providing high compression ratio. File names with "raw" suffix refers to RGB registered cloud. The "gt" suffix stands for ground truth clouds.</p> <p> </p> <p><strong>Raw point cloud description</strong></p> <p>Name Architectural Style Construction Year TLS UAV Points* ID</p> <p>Good Death's Church Neoclassical 1867 Yes Yes 210 <em>boa_morte</em><br> Central Mill Industrial 1881 Yes Yes 26 <em>engenho</em><br> Ball House Modern 1940 Yes No 370 <em>baile</em><br> Quilombo Farm Colonial 1891 Yes Yes 55 <em>quilombo</em> St. Francis of Assisi Church Modern 1943 Yes Yes 46 <em>curia</em></p> <p>* millions </p> <p> </p> <p><strong>Equipment</strong></p> <ul> <li>FARO Focus3D X 330 HDR*</li> <li>DJI Inspire 2 with Gimbal DJI Zenmuse X4S 4K | H264 | F2.8 - F11 | 20MP**</li> <li>DJI Spark 12 MP Full HD GPS **</li> </ul> <p><em>*Terrestrial Laser Scanner (TLS)</em></p> <p><em>**Unmanned Aerial Vehicle (UAV)</em></p> <p> </p> <p><strong>Software</strong></p> <p>The following third-party software have been used to register the clouds.: FARO SCENE, CloudCompare and Pix4D Mapper. For a better understanding of the hybrid point cloud registration approach used in this dataset we recommend the following article: <a href="http://papers.cumincad.org/cgi-bin/works/paper/ecaade2018_295">BIM for Heritage Documentation An ontology-based approach</a>.</p> <p> </p> <p><strong>Ground-truth</strong></p> <p>Ground-Truth in this dataset was manually created with the Interactive Segmentation Tool on CloudCompare. Distinct colors represent different architectural elements. An “optimal” segmentation algorithm must partition the raw point clouds into equivalent single-color ground-truth clusters. </p> <p> </p> <p><strong>How to cite this dataset?</strong></p> <p>Paiva, P. V. V., Cogima, C. K., Dezen-Kempter, E. and Carvalho, M. A. G. "Historical building point cloud segmentation combining hierarchical watershed transform and curvature analysis." <em>Pattern Recognition Letters</em> 135 (2020): 114-121. DOI: <a href="https://doi.org/10.1016/j.patrec.2020.04.010">https://doi.org/10.1016/j.patrec.2020.04.010</a></p> <p>Paiva, P. V. V., Cogima, C. K., Dezen-Kempter, E. and Carvalho, M. A. G. "<em>CHAS - Cultural Heritage Architectural Segmentation dataset</em>". Zenodo, March 26 2019. DOI: <a href="https://doi.org/10.5281/zenodo.2609498">https://doi.org/10.5281/zenodo.2609498</a></p> <p> </p>
Script Classification and Writer Identification: Two Tasks for a Common Understanding of Cultural Heritage - Dataset
<p>Writer identification and Script classification are usually considered as two separated and very different tasks, as well in palaeography as in computer science. Following the ICDAR competition on the CLAMM corpus about script classification and dating, this dataset provides the output created by running two infrastructures created for Script classification at a large scale (medieval scripts) on a more homogeneous dataset with a focus on Writer Identification.</p> <p>If you use the present repository, its data and figures, please consult and cite:</p> <p>Stutzmann, Dominique, Christopher Tensmeyer, and Vincent Christlein. « Writer Identification and Script Classification: Two Tasks for a Common Understanding of Cultural Heritage ». manuscript cultures, 15 (2020): 11-24. <a href="https://www.csmc.uni-hamburg.de/publications/mc/files/articles/mc15-02-stutzmann.pdf">https://www.csmc.uni-hamburg.de/publications/mc/files/articles/mc15-02-stutzmann.pdf</a></p>
The linked data ecosystem for SSH and a case study from the cultural heritage domain
<p>Semantic Web, an extension of the current web, focuses on the structure of data. The aim is to develop data structures that will be more effectively processable by computers, in contrast to the limited functionality of the contemporary scriptocentric web. Semantic Web, therefore, aims to transform the web into a global ‘database’ and lead the developments toward the Web of Data. The specifications developed or related to this aim, as well as the processes and rules for achieving it, are known as Linked Data, i.e. (inter)connected data from different sources. Linked Data creates a content-agnostic, as well as software-agnostic ecosystem, which can be used for encoding and publishing all types of information from various domains.</p> <p>The first presentation of this webinar introduces the basic concepts of Linked Data and shows that Linked Data are particularly suitable for Social Sciences and Humanities (SSH) since they do not emphasize metrics and quantifications but conceptualizations and reason. After presenting the paradigm shift introduced by linked data concerning the development of the web, a case study from the cultural heritage domain will be discussed.</p> <p>The second presentation showcases SearchCulture.gr, the Greek cross-domain Cultural Data Aggregator, as a state-of-the-art case, for the use of Linked data in the cultural domain. The National Documentation Centre of Greece (EKT) develops this service, which has collected a growing number of 800.000 digitized Cultural Heritage Objects (CHOs) from 73 cultural institutions. Moreover, it is the Accredited National Aggregator for Europeana having provided more than 580.000 CHOs so far. Addressing metadata heterogeneity has been a key target from the start. Controlled Linked Data vocabularies for item types, historical periods, subjects and persons have been developed over the course of the past years and are being used for the semantic enrichment of the CHOs’ metadata. This presentation addresses the challenges, methodology and tools used over the past 7 years for the process of enriching the aggregated CHOs’ metadata. This process classifies and disambiguates the aggregated data, provides multilinguality and adds significant browse and search functionalities to the portal and, therefore, opens new horizons for SSH research.</p>
Wikidata selection of Cultural Heritage, Stars, Galaxies and Random entities and claims
<p>This work presents an analysis of the use of different representation methods in Wikidata to encode information with weaker logical status (e.g. contradictory information, temporally evolving information). The study examines three main approaches: ranked statements, null-valued objects, and qualified statements with properties P5102 (reason of statement), P1480 (sourcing circumstances) and P2241 (reason for deprecated rank) by analysing their prevalence, success, and clarity in Wikidata. The analysis is performed over cultural heritage artefacts stored in Wikidata divided in three subsets (i.e. visual heritage, textual heritage and audio-visual heritage) and compared with astronomical data (stars and galaxies entities from Wikidata) and a Random dataset (entities chosen randomly from the most used 100 classes). The findings indicate that (1) the representation of weaker logical status information is limited, with only a small proportion of items reporting such information, (2) the representation of WLS varies significantly between the two datasets. Finally, we propose several representation alternatives to simplify and standardize the representation of this type of information in Wikidata, with the hope of increasing its accuracy and richness</p>
User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops
<p><strong>Introduction</strong></p> <p>This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article. </p> <p><strong>Summary of the user evaluation</strong></p> <p>The structrue of the user evalution is summarized in the table below:</p> <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table> <p> </p> <p><strong>The structure of the data is as follows:</strong></p> <p>1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey:</p> <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> <p>2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>): </p> <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> <p><em>Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details</em></p>
Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana (dataset)
<p>The dataset contains all the data required to reproduce the experiments done in the paper "Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana", published in the 25th International Conference on Theory and Practice of Digital Libraries (<a href="http://www.tpdl.eu/tpdl2021/">TPDL'21</a>). In that work we run an experiment using the Europeana CH digital library as a use case, and we evaluated the effectiveness of a multilingual information retrieval strategy using machine translations to English as pivot language. We used the CEF translation service (eTranslation) for the translation of queries and content to English (<a href="https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation">https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation</a>).</p> <p>The dataset is also available at <a href="https://rnd-2.eanadev.org/share/crosslingual-search/">https://rnd-2.eanadev.org/share/crosslingual-search/</a>, and it is organized in four main folders:</p> <ul> <li><strong>queries</strong>: sample of 68 queries and their translations to English. The queries were issued in languages other than English from the Europeana Portal, using the Europeana’s 1914-1918 thematic collection, between January and August 2019.</li> <li><strong>transcriptions</strong>: sample of 18,257 handwriting transcriptions and its translations to English. The transcriptions are taken from the Europeana 1914-1918 thematic collection, and obtained from the Transcribathon crowdsourcing platform (https://europeana.transcribathon.eu/).</li> <li><strong>solr_configuration</strong>: Apache Solr search engine configuration used in the experiments (which replicates the one used in Europeana).</li> <li><strong>results</strong>: manual evaluation of the query translations, and automatic evaluation of the multilingual retrieval.</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.