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zenodo48/100

Dataset for 'A Matter of Culture? Conceptualising and Investigating 'Evidence Cultures' within Research on Evidence-Informed Policymaking'

Introduction This document describes the data collection and datasets used in the manuscript "A Matter of Culture? Conceptualising and Investigating ‘Evidence Cultures’ within Research on Evidence-Informed Policymaking" [1]. Data Collection To construct the citation network analysed in the manuscript, we first designed a series of queries to capture a large sample of literature exploring the relationship between evidence, policy, and culture from various perspectives. Our team of domain experts developed the following queries based on terms common in the literature. These queries search for the terms included in the titles, abstracts, and associated keywords of WoS indexed records (i.e. ‘TS=’). While these are separated below for ease of reading, they combined into a single query via the OR operator in our search. Our search was conducted on the Web of Science’s (WoS) Core Collection through the University of Edinburgh Library subscription on 29/11/2023, returning a total of 2,089 records. TS = ((“cultures of evidence” OR “culture of evidence” OR “culture of knowledge” OR “cultures of knowledge” OR “research culture” OR “research cultures” OR “culture of research” OR “cultures of research” OR “epistemic culture” OR “epistemic cultures” OR “epistemic community” OR “epistemic communities” OR “epistemic infrastructure” OR “evaluation culture” OR “evaluation cultures” OR “culture of evaluation” OR “cultures of evaluation” OR “thought style” OR “thought styles” OR “thought collective” OR “thought collectives” OR “knowledge regime” OR “knowledge regimes” OR “knowledge system” OR “knowledge systems” OR “civic epistemology” OR “civic epistemologies”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “policy decision” OR “policy decisions” OR “political decision” OR “political decisions” OR “political decision making”)) OR TS = ((“culture” OR “cultures”) AND ((“evidence-based” OR “evidence-informed” OR “evidence-led” OR “science-based” OR “science-informed” OR “science-led” OR “research-based” OR “research-informed” OR “evidence use” OR “evidence user” OR “evidence utilisation” OR “evidence utilization” OR “research use” OR “researcher user” OR “research utilisation” OR “research utilization” OR “research in” OR “evidence in” OR “science in”) NEAR/1 (“policymaking” OR “policy making” OR “policy maker” OR “policy makers”))) OR TS = ((“culture” OR “cultures”) AND (“scientific advice” OR “technical advice” OR “scientific expertise” OR “technical expertise” OR “expert advice”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “political decision” OR “political decisions” OR “political decision making”))  OR TS = ((“culture” OR “cultures”) AND (“post-normal science” OR “trans-science” OR “transdisciplinary” OR “transdisiplinarity” OR “science-policy interface” OR “policy sciences” OR “sociology of knowledge” OR “sociology of science” OR “knowledge transfer” OR “knowledge translation” OR “knowledge broker” OR “implementation science” OR “risk society”) AND (“policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers”)) Citation Network Construction All bibliographic metadata on these 2,089 records were downloaded in five batches in plain text and then merged in R. We then parsed these data into network readable files. All unique reference strings are given unique node IDs. A node-attribute-list (‘CE_Node’) links identifying information of each document with its node ID, including authors, title, year of publication, journal WoS ID, and WoS citations. An edge-list (‘CE_Edge’) records all citations from these documents to their bibliographies – with edges going from a citing document to the cited – using the relevant node IDs. These data were then cleaned by (a) matching DOIs for reference strings that differ but point to the same paper, and (b) manual merging of obvious duplicates caused by referencing errors. Our initial dataset consisted of 2,089 retrieved documents and 123,772 unretrieved cited documents (i.e. documents that were cited within the publications we retrieved but which were not one of these 2,089 documents). These documents were connected by 157,229 citation links, but ~87% of the documents in the network were cited just once. To focus on relevant literature, we filtered the network to include only documents with at least three citation or reference links. We further refined the dataset by focusing on the main connected component, resulting in 6,650 nodes and 29,198 edges. It is this dataset that we publish here, and it is this network that underpins Figure 1, Table 1, and the qualitative examination of documents (see manuscript for further details). Our final network dataset contains 1,819 of the documents in our original query (~87% of the original retrieved records), and 4,831 documents not retrieved via our Web of Science search but cited by at least three of the retrieved documents. We then clustered this network by modularity maximization via the Leiden algorithm [2], detecting 14 clusters with Q=0.59. Citations to documents within the same cluster constitute ~77% of all citations in the network. Citation Network Dataset Description We include two network datasets: (i) ‘CE_Node.csv’ that contains 1,819 retrieved documents, 4,831 unretrieved referenced documents, making for a total of 6,650 documents (nodes); (ii)’CE_Edge.csv’ that records citations (edges) between the documents (nodes), including a total of 29,198 citation links. These files can be used to construct a network with many different tools, but we have formatted these to be used in Gephi 0.10[3]. ‘CE_Node.csv’ is a comma-separate values file that contains two types of nodes: i.                    Retrieved documents – these are documents captured by our query. These include full bibliographic metadata and reference lists. ii.                  Non-retrieved documents – these are documents referenced by our retrieved documents but were not retrieved via our query. These only have data contained within their reference string (i.e. first author, journal or book title, year of publication, and possibly DOI). The columns in the .csv refer to: -          Id, the node ID -          Label, the reference string of the document -          DOI, the DOI for the document, if available -          WOS_ID, WoS accession number -          Authors, named authors -          Title, title of document -          Document_type, variable indicating whether a document is an article, review, etc. -          Journal_book_title, journal of publication or title of book -          Publication year, year of publication. -          WOS_times_cited, total Core Collection citations as of 29/11/2023 -          Indegree, number of within network citations to a given document -          Cluster, provides the cluster membership number as discussed in the manuscript (Figure 1) ‘CE_Edge.csv’ is a comma-separated values file that contains edges (citation links) between nodes (documents) (n=29,198). The columns refer to: -          Source, node ID of the citing document -          Target, node ID of the cited document Cluster Analysis We qualitatively analyse a set of publications from seven of the largest clusters in our manuscript. For this, we calculated the within cluster indegree of nodes, and read through the 10 most cited retrieved documents and 10 most cited unretrieved documents. To generate these lists, sub-graphs for each cluster needed to be generated, and then indegree was measured (i.e. counting the number of citations from papers within a cluster to other papers in that same cluster). Notes <a href="https://zenodo.org/records/6615221#_ftnref1">[1]</a> Bandola-Gill, J., Andersen, N., Leng, R. I., Pattyn, V., & Smith, K. E. (forthcoming). A Matter of Culture? Conceptualising and Investigating ‘Evidence Cultures’ within Research on Evidence-Informed Policymaking. Policy and Society <a href="https://zenodo.org/records/6615221#_ftnref6">[2]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a> <a href="https://zenodo.org/records/6615221#_ftnref5">[3]</a> Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a>

opencc-by-4.0Oct 2024View details →

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Research compendium for 'Refitting the Context: A Reconsideration of Cultural Change among Early Homo sapiens at Fumane Cave through Blade Break Connections, Spatial Taphonomy, and Lithic Technology'

<div> <h3>Compendium DOI:</h3> <a href="../doi/10.5281/zenodo.10965413">https://zenodo.org/doi/10.5281/zenodo.10965413</a>  </div> The content available at the above provided URL will reproduce the results as documented in the publication. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Refitting-The-Context">https://github.com/ArmandoFalcucci/Refitting-The-Context</a> represent the developmental versions and might have undergone modifications since the paper's publication. <div> <h3>Maintainer of this repository:</h3> </div> Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>) <div> <h3>Published paper:</h3> </div> Armando Falcucci, Domenico Giusti, Filippo Zangrossi, Matteo De Lorenzi, Letizia Ceregatti, Marco Peresani. Refitting the Context: Revisiting the Aurignacian sequence at Fumane Cave through blade fragment connections, spatial taphonomy, and lithic technology. Journal of Paleolithic Archaeology (2024). DOI: <a href="https://doi.org/10.1007/s41982-024-00203-0" rel="nofollow">10.1007/s41982-024-00203-0</a> <div> <h3>Abstract:</h3> </div> High-resolution stratigraphic frameworks are crucial for unraveling the biocultural processes behind the dispersals of Homo sapiens across Europe. Detailed technological studies of lithic assemblages retrieved from multi-stratified sequences allow archaeologists to precisely model the chrono-cultural dynamics of the early Upper Paleolithic. However, it is of paramount importance to verify the integrity of these assemblages before building explanatory models of cultural change. In this study, multiple lines of evidence suggest that the stratigraphic sequence of Fumane Cave in northeastern Italy experienced minor post-depositional reworking, establishing it as a pivotal site for exploring the earliest stages of the Aurignacian. By conducting a systematic search for break connections between blade fragments and applying spatial analysis techniques, we identified three well-preserved areas of the excavation containing assemblages suitable for renewed archaeological investigations. Subsequent technological analyses, incorporating attribute analysis, reduction intensity, and multivariate statistics, have allowed us to discern the spatial organization of the site during the formation of the Protoaurignacian palimpsest A2–A1. Moreover, diachronic comparisons between three successive stratigraphic units prompted us to reject the hypothesis of techno-cultural continuity of the Protoaurignacian in northeastern Italy after the onset of the Heinrich Event 4. Based on the variability of the lithic and osseous artifacts, the most recent assemblage analyzed, D3b alpha, is now ascribed to the Early Aurignacian, aligning the evidence from Fumane with the current understanding of the development of the Aurignacian across Europe. Overall, this study demonstrates the high effectiveness of the break connection method when combined with detailed spatial analysis and lithic technology, providing a methodological tool particularly amenable to be applied to sites excavated in the past with varying degrees of recording accuracy. <div> <h3>Keywords:</h3> </div> Protoaurignacian; Early Aurignacian; Lithics; Refittings; Assemblage integrity; Spatial analysis; Italy <div> <h3>Overview of contents and how to reproduce:</h3> </div> Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the blank and core datasets from the Aurignacian of Fumane Cave and the dataset of the blade fragment connection study. To replicate the results, download the entire repository and employ <code>Refitting-The-Context.Rproj</code> and open the folder <code>script</code>. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here. <div> <h3>Licenses:</h3> </div> Code: MIT <a href="http://opensource.org/licenses/MIT" rel="nofollow">http://opensource.org/licenses/MIT</a>, copyright holder: Armando Falcucci (2024). Data and intellectual work: Creative Commons Attribution 4.0 International License (<a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).

opencc-by-4.0May 2024View details →

zenodo44/100

Including Data Management in Research Culture Increases the Reproducibility of Scientific Results

General Information: This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended.  The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268 The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30 This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications,  a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data. Run the code: To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt".  Alternatively, you can import the .ipyab file in the colab [2] and run it.  [1]. https://www.anaconda.com/ [2]. https://research.google.com/colaboratory/

opencc-by-4.0May 2022View details →

zenodo44/100

Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy'

<h2>Research compendium for 'A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy' </h2> Compendium DOI:  <a href="https://doi.org/10.5281/zenodo.10639553">https://doi.org/</a><a href="../doi/10.5281/zenodo.10639552">10.5281/zenodo.10639552</a> The content available at the above provided URL will reproduce the results as documented in the first paper's submission. Instead, the files hosted at <a href="https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno">https://github.com/ArmandoFalcucci/Castelcivita-Aur-Techno</a> represent the developmental versions and might have undergone modifications since the paper's publication. Maintainer of this repository: Armando Falcucci (<a href="mailto:armando.falcucci@uni-tuebingen.de">armando.falcucci@uni-tuebingen.de</a>; <a href="https://orcid.org/0000-0002-3255-1005">https://orcid.org/0000-0002-3255-1005</a>)  Published paper: Armando Falcucci, Simona Arrighi, Vincenzo Spagnolo, Matteo Rossini, Owen Higgins, Brunella Muttillo, Ivan Martini, Jacopo Crezzini, Francesco Boschin, Annamaria Ronchitelli, Adriana Moroni. A pre-Campanian Ignimbrite techno-cultural shift in the Aurignacian sequence of Grotta di Castelcivita, southern Italy. Scientific Reports, 14: 12783. doi:10.1038/s41598-024-59896-6 (2024) Abstract: The Aurignacian is the first European technocomplex assigned to Homo sapiens recognized across a wide geographic extent. Although archaeologists have identified marked chrono-cultural shifts within the Aurignacian mostly by examining the techno-typological variations of stone and osseous tools, unraveling the underlying processes driving these changes remains a significant scientific challenge. Scholars have, for instance, hypothesized that the Campanian Ignimbrite (CI) super-eruption and the climatic deterioration associated with the onset of Heinrich Event 4 had a substantial impact on European foraging groups. The technological shift from the Protoaurignacian to the Early Aurignacian is regarded as an archaeological manifestation of adaptation to changing environments. However, some of the most crucial regions and stratigraphic sequences for testing these scenarios have been overlooked. In this study, we delve into the high-resolution stratigraphic sequence of Grotta di Castelcivita in southern Italy. Here, the Uluzzian is followed by three Aurignacian layers, sealed by the eruptive units of the CI. Employing a comprehensive range of quantitative methods—encompassing attribute analysis, 3D model analysis, and geometric morphometrics—we demonstrate that the key technological feature commonly associated with the Early Aurignacian developed well before the deposition of the CI tephra. Our study provides thus the first direct evidence that the volcanic super-eruption played no role in this cultural process. Furthermore, we show that local paleo-environmental proxies do not correlate with the identified patterns of cultural continuity and discontinuity. Consequently, we propose alternative research paths to explore the role of demography and regional trajectories in the development of the Upper Paleolithic. Keywords: Early Upper Paleolithic; Italy; Aurignacian; lithic technology; geometric morphometrics; 3D model analysis; cultural evolution; human-environment interaction; open science. Overview of contents and how to reproduce: Within this repository, various folders house data (<code>data</code>), code (<code>script</code>), and output files (<code>output</code>) pertinent to the paper. The data folder encompasses the complete dataset, the core dataset, and 2D outline coordinates utilized for the geometric morphometrics study. To replicate the results, download the entire repository and employ <code>Castelcivita-Aur-Techno.Rproj</code> and open the folder <code>script</code>, following the numbered folder structure. For ensuring reproducibility, the <code>renv</code> package (v. 1.0.3) was utilized, following the procedures detailed in its vignette. All analyses and visualizations in the paper were conducted using R 4.3.1 on Microsoft Windows 10.0.19045 (64-bit). As the necessary packages are available in the <code>renv</code> folder, they are not explicitly listed here. Licenses: Code: MIT (<a href="http://opensource.org/licenses/MIT">http://opensource.org/licenses/MIT),</a> copyright holder: Armando Falcucci (2024). Data and intellectual work: Creative Commons Attribution 4.0 International License (<a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>), copyright holder: the authors (2024).

opencc-by-4.0Feb 2024View details →

zenodo44/100

Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023) - PI

Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Dorota Sosnowska used as a basis for two papers: <a href="https://open.icm.edu.pl/items/9117fe29-528d-43ff-99c7-2e1fe0a8a93a">Blasted 1999. Sarah Kane’s Body Against the Archive (icm.edu.pl)</a> and <a href="https://open.icm.edu.pl/items/0aa7964c-b690-42af-8f7c-3d4fc62084b5">Brzydkie uczucia. O nudzie w sztuce i teatrze lat’ 90 (icm.edu.pl)</a>

opencc-by-4.0Jul 2024View details →

zenodo44/100

Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023)

Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Łukasz Kiełpiński used as a basis for two papers: <a href="../records/10625768">Zarządzanie ambiwalencją. Polski dyskurs ekspercki wokół HIV/AIDS na przełomie lat osiemdziesiątych i dziewięćdziesiątych XX wieku (zenodo.org)</a> and <a href="../records/10625805">Gra o sumie zerowej. Ekonomia wstydu w filmie "Pora na czarownice" (zenodo.org)</a>

opencc-by-4.0Jul 2024View details →

zenodo44/100

Dataset: Publication cultures and Dutch research output: a quantitative assessment

Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.2643360">Publication cultures and Dutch research output: a quantitative assessment</a>   On the report: Research into publication cultures commissioned by VSNU and carried out by Utrecht University Library has detailed university output beyond just journal articles, as well as the possibilities to assess open access levels of these other output types. For all four main fields reported on, the use of publication types other than journal articles is indeed substantial. For Social Sciences and Arts & Humanities in particular (with over 40% and over 60% of output respectively not being regular journal articles) looking at journal articles only ignores a significant share of their contribution to research and society. This is not only about books and book chapters, either: book reviews, conference papers, reports, case notes (in law) and all kinds of web publications are also significant parts of university output. Analyzing all these publication forms and especially determining to what extent they are open access is currently not easy. Even combining some the largest citation databases (Web of Science, Scopus and Dimensions) leaves out a lot of non-article content and in some fields even journal articles are only partly covered. Lacking metadata like affiliations and DOIs (either in the original documents or in the scholarly search engines) makes it even harder to analyze open access levels by institution and field. Using repository-harvesting databases like BASE and NARCIS in addition to the main citation databases improves understanding of open access of non-article output, but these routes also have limitations. The report has recommendations for stakeholders, mostly to improve metadata and coverage and apply persistent identifiers.

opencc-by-4.0Apr 2019View details →

zenodo44/100

Desk research of 107 case studies on state-of-the-art of cultural tourism interventions

The aim of Work Package 3 of the SmartCulTour project (<a href="http://www.smartcultour.eu">www.smartcultour.eu</a>) is to provide a state-of-the-art overview of cultural tourism interventions implemented in European cities and regions, thereby identifying best practices and the impacts and success conditions of cultural tourism interventions. To this end, the consortium identified 107 interesting cultural tourism interventions throughout Europe, with a geographical coverage of: Belgium (9), Italy (8), The Netherlands (8), Serbia (7), Romania (6), Croatia (5), Hungary (4), Portugal (4), Spain (4), United Kingdom (4), Finland (3), France (3), Sweden (3), Other countries (24), Multiple countries (15). The "Overview and taxonomy of 107 interventions" lists every intervention that was studied, as well as their respective classification given, based on the description and objective of the intervention. Within this table, a value of "1" is its primary categorization, with a value of "2" assigned to a secondary taxonomy. Each intervention can have multiple purposes and therefore belong to different categories.The taxonomy of cultural tourism interventions is further described in "State of the art of cultural tourism interventions" (DOI: 10.5281/zenodo.5270321). The 107 cultural tourism interventions were analyzed via desk research only during the period September 2020-January 2021, based on available secondary data and following a standardized data collection form. This form is included here as "Internal data collection form used for analysis". The forms collect data on: <ul> <li>Context and background information;</li> <li>The 'reason why' of the intervention;</li> <li>The intervention;</li> <li>Resources and tools necessary to design and implement the interventions;</li> <li>Impacts (expected, perceived and measured);</li> <li>(Perceived) success conditions and limiting factors.</li> </ul> The zip-file "Internal forms of 107 interventions" contains all 107 completed data collection forms.

opencc-by-4.0Aug 2021View details →

zenodo40/100

Guideline for a FAIR Cultural Studies Research Data Management

Dies ist die Datenpublikation (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung &bdquo;Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die Online-Handreichung ist verfügbar unter <a href="https://nfdi4culture.de/go/E3625">https://nfdi4culture.de/go/E3625</a>. Als PDF ist diese Handreichung verfügbar unter <a href="https://doi.org/10.5281/zenodo.7716941">https://doi.org/10.5281/zenodo.7716941</a>.

opencc-by-4.0Feb 2023View details →

zenodo40/100

"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice

"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19

opencc-by-4.0Dec 2019View details →

zenodo36/100

Research - a Public Culture in 2065

This presentation envisions an event from 2065, discussing the research environment around the year 2025 and how we enjoy research being a popular culture 40 years later. This short presentation draws parallels between Gastronomy and Research, originally two unpopular cultures that eventually spread to the masses in the 19ths and 21st centuries, respectively. Notes (for 2065): This event is not optimized for Meta environments. The organisers are compensating for the environmental impact.

opencc-by-nc-nd-4.0Oct 2022View details →

zenodo36/100

A controlled vocabulary for research and innovation in the field of Cultural Heritage & Heritage Sciences

This controlled vocabulary of keywords related to the field of Cultural Heritage and Heritage Sciences was built by SIRIS Academic in collaboration with IRPET (the Regional Institute for Economic Planning of Tuscany) and the ISPC (Institute of Heritage Science of CNR), in order to identify Cultural related research, development, and innovation activities. The work was carried out by consulting domain experts' advice, and it was ultimately applied to inform regional strategies on Cultural Heritage and research and innovation policy. The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of Cultural Heritage and Heritage Sciences. The aim of this classification is to identify research products in the domain of Cultural Heritage, ranging from documents in some of its “traditional” disciplines, but also from documents emerging from interdisciplinary projects that apply novel areas and technologies in the domain of Cultural Heritage. The identification of texts in the domain of Cultural Heritage requires a task of text classification. Developing a method that could be applied to decide if a text can be relevant or have some relation to the domain of Cultural Heritage is a challenging task. The definition of what Cultural Heritage is and what it includes is a complex activity, even for domain experts. This is in particular because Cultural Heritage is quite a broad field of knowledge, and there is no full agreement on where the borders of the domain are. To define the scope of the perimeter, in this project, many of the available definitions were taken into account. Because of the high number of resources available in the domain, among thesauruses and taxonomies, the construction of a weakly-supervised controlled vocabulary was considered as the best way of retrieving documents in the domain. Since there is no annotated corpus/dataset of research texts in the domain capable of generalising the diversity of publications that can be related to the cultural domain, but stemming from different disciplines, we have opted for a text classification technique based on rules – specifically, a weakly-supervised controlled vocabulary. As defined by the Getty Institute, a controlled vocabulary is an organized arrangement of words and phrases used to index content and/or to retrieve content through browsing or searching. It typically includes preferred and variant terms and has a defined scope or describes a specific domain. The purpose of controlled vocabularies is to organize information and to provide terminology to catalogue and retrieve information. While capturing the richness of variant terms, controlled vocabularies also promote consistency in preferred terms and the assignment of the same terms to similar content (Harping, 2010). In short, Cultural Heritage is a rather abstractly-defined field, and Heritage Science is a particularly “fuzzy” field within Cultural Heritage. One of the main limitations of the approach we used is that the controlled vocabularies never capture all the lexical and linguistic variants of a term, and we may miss relevant texts if we cannot find the correct pattern to match during the search. But on the other hand, the controlled vocabulary is built from available vocabularies and thesauruses in the domain of Cultural Heritage, which are large resources. All the concepts in these resources are not included directly in the controlled vocabularies, because they would add noise to the classification. Therefore, the automatic weak supervision and a human curation of the final controlled vocabulary is fundamental for achieving correct results. The controlled vocabulary is built taking advantage of these four resources: <ul> <li>The <a href="https://www.getty.edu/research/tools/vocabularies/aat/">Art and Architecture Thesaurus (AAT)</a>: this is a structured vocabulary with approximately 34,000 concepts, including 131,000 words, descriptions and other information related to art, architecture, decorative arts, archival material and material culture, commonly used for cataloguing and for information retrieval.</li> <li> Some cultural heritage categories in <a href="https://en.wikipedia.org/wiki/Category:Cultural_heritage">Wikipedia</a> and <a href="https://dbpedia.org/page/Cultural_heritage">DBpedia</a>: these categories have been used to collect all related articles and subcategories, in order to obtain relevant, similar and specific instances of concepts linked to the domain.  </li> <li> The <a href="https://www.riches-project.eu/riches-taxonomy.html">RICHES Taxonomy</a>: this taxonomy is a theoretical framework of related terms and their definitions, referring to the new concepts in the digital era, with the aim of defining the scope of some digital technologies applied to cultural heritage. </li> <li> <a href="https://www.heritagedata.org/blog/">Heritage Data - Linked Data Vocabularies for Cultural Heritage</a>: a dataset which includes several cultural heritage thesauruses and vocabularies and is recognised as a reference point in the United Kingdom in the domain of cultural heritage. </li> </ul> The collection of concepts extracted from these four resources was composed of more than 60,000 terms, which have been refined as described in the next section.   ## Automatic validation of the controlled vocabulary In order to refine the collection of concepts to have a final set of relevant concepts and terms in the domain of Cultural Heritage, a semi-automatic validation has been applied to remove the irrelevant, too general, and ambiguous terms. To keep the relevant ones, the <a href="https://ncses.nsf.gov/pubs/nsb20206/specialization-and-impact-analysis-combined#:~:text=The%20specialization%20index%20(SI)%20is,the%20total%20output%20across%20all">specialization index (SI) </a>metric has been calculated for each of the keywords in the collection. In this case, the SI can be obtained measuring the fraction of publications with a keyword in a set of publications in the domain of Cultural Heritage and normalizing over the fraction of publications in the open domain with that keyword. After the calculation of the SI, all the keywords below a certain threshold are removed, and a manual supervision step is applied in order to remove non-pertinent keywords. An example of this automatic validation can be observed in the next table: <table> <tbody> <tr> <td> Keyword </td> <td> Specialization Index </td> <td> Automatic threshold </td> <td> Manual supervision </td> </tr> <tr> <td> male </td> <td> 0.27 </td> <td> Removed </td> <td> Accepted </td> </tr> <tr> <td> 3-d laser scanning </td> <td> 0.7 </td> <td> Removed </td> <td> Accepted </td> </tr> <tr> <td> 78 rpm records </td> <td> 20.7 </td> <td> Accepted </td> <td> Removed </td> </tr> <tr> <td> vienna </td> <td> 3.48 </td> <td> Accepted </td> <td> Removed </td> </tr> <tr> <td> radiocarbon dating </td> <td> 13.6 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> graffiti </td> <td> 25 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> bark painting </td> <td> 20.7 </td> <td> Accepted </td> <td> Accepted </td> </tr> <tr> <td> pompeii </td> <td> 16.23 </td> <td> Accepted </td> <td> Accepted </td> </tr> </tbody> </table> The SI of the final keywords can be used as a probabilistic metric for each keyword. The final list of keywords was manually curated by domain experts.   ## Evaluation of the controlled vocabulary The final controlled vocabulary was evaluated with an external dataset with the aim of calculating its degree of precision. The evaluation dataset was composed of a collection of articles in 4 journals unequivocally considered to fall within the domain of Cultural Heritage. These four journals were: (1) Journal Of Cultural Heritage, (2) Journal On Computing And Cultural Heritage, (3) Journal Of Cultural Heritage Management And Sustainable Development and (4) Digital Applications In Archaeology And Cultural Heritage. This collection was composed of 5,000 articles, considered as the positive set, and another collection of randomly selected 5,000 articles outside of the Cultural Heritage domain, considered as the false set. The Cultural Heritage vocabulary was applied to the evaluation data set, obtaining a 95% of precision. After a set of improvements on the vocabulary, based on the exploration of publications not identified in the first test and the false positive results, we obtained a 98% of precision. The application of the vocabulary taking advantage of the probability of each keyword as its weight of being in the domain did not improve the results, and for this reason the probabilistic approach was discarded.   ## Using the vocabulary to classify publications concerning Cultural Heritage The definition of the vocabulary does not, per se, allow to identify research contributions in Cultural Heritage: this is performed by actually matching the terms in the controlled vocabulary to the content of the gathered research textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. In the following table we present some examples of the tagging process on some abstracts: <table> <tbody> <tr> <td> Publication title </td> <td> Publication abstract </td> </tr> <tr> <td> Egocentric visitor localization and artwork detection in cultural sites using synthetic data </td> <td> Computer vision and machine learning can be used in cultural heritage to augment the experience of visitors during the exploration of the cultural site, as well as to assist its management. To achieve such goals, two fundamental tasks should be addressed, i.e., localizing visitors and recognizing the observed artworks. Wearable cameras offer a convenient setting to address both tasks through the analysis of images acquired from the visitors’ points of view. However, the engineering of approaches to address such tasks generally requires large amounts of labeled data. We propose a tool which can be used to collect and automatically label synthetic visual data suitable to study image-based localization and artwork detection. The tool simulates a virtual agent navigating the 3D model of a real cultural site and automatically captures video frames along with the related ground truth camera poses and semantic masks indicating the position of artworks. We generate a dataset of synthetic images starting from the 3D model of a museum located in Siracusa, Italy. The experiments suggest that the proposed tool allows to drastically reduce the effort needed to collect and label data, providing a means to generate large-scale datasets suitable to study localization and artwork detection in cultural sites. </td> </tr> <tr> <td> Discovering Leonardo with artificial intelligence and holograms: A user study </td> <td> Cutting-edge visualization and interaction technologies are increasingly used in museum exhibitions, providing novel ways to engage visitors and enhance their cultural experience. Existing applications are commonly built upon a single technology, focusing on visualization, motion or verbal interaction (e.g., high-resolution projections, gesture interfaces, chatbots). This aspect limits their potential, since museums are highly heterogeneous in terms of visitors profiles and interests, requiring multi-channel, customizable interaction modalities. To this aim, this work describes and evaluates an artificial intelligence powered, interactive holographic stand aimed at describing Leonardo Da Vinci's art. This system provides the users with accurate 3D representations of Leonardo's machines, which can be interactively manipulated through a touchless user interface. It is also able to dialog with the users in natural language about Leonardo's art, while keeping the context of conversation and interactions. Furthermore, the results of a large user study, carried out during art and tech exhibitions, are presented and discussed. The goal was to assess how users of different ages and interests perceive, understand and explore cultural objects when holograms and artificial intelligence are used as instruments of knowledge and analysis. </td> </tr> <tr> <td> Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering </td> <td> Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories. </td> </tr> <tr> <td> "3D reconstruction and validation of historical background for immersive VR applications and games: The case study of the Forum of Augustus in Rome" </td> <td> "In the last decades, thanks to the success of the video games industry, the sector of technologies applied to cultural heritage has begun to envisage, in this domain, new possibilities for the dissemination of heritage and the study of the past through edutainment models. More recently, experimentation in the field of virtual archaeology has led to the development of virtual museums and interactive applications. Among these, the “serious game” segment – the application of interactive technologies to the cultural heritage domain – is rapidly growing, also including immersive VR technologies. Applied VR games and applications are characterized by a thorough historical background and a validated 3D reconstruction. Indeed, producing such products requires a tailored workflow and large effort in terms of time and professionals involved to guarantee such faithfulness. Drawing on our previous work in the field of virtual archaeology and referring to recent experiences related to the deployment of applied VR games on PlayStation VR, we describe and assess a workflow for the production of historically accurate 3D assets, targeting interactive, immersive VR products. The workflow is supported by the case study of the Forum of Augustus and different output applications, highlighting peculiarities and issues emerging from a multi and interdisciplinary approach. </td> </tr> </tbody> </table> Through this classification process, we identified projects and publications related to heritage, with different levels of relationship and relevance, but mostly relevant to understanding the research competencies in the domain. The resulting research records were reviewed by experts in the domain, given the occurrence of some false positives. Among the main strengths of this step, it’s worth mentioning the fact that the vocabulary is broad and not restricted to the field of Heritage Science (that is, to STEM applications in Cultural Heritage), as it takes advantage of a variety of available resources. Moreover, by looking directly at the textual data, instead of using the assigned bibliometric areas, we can better capture interdisciplinary research. The limitations of this approach were presented at the beginning of this document: for example, relevant texts could be missed if the correct pattern to match during the search is not found.   ## Vocabulary of concepts related to Key Enabling Technologies in the domain of Cultural Heritage and Culture For the development of this vocabulary, the definition of key enabling technologies in the domain of Cultural Heritage and Culture, was based on reference of the <a href="http://www.irpet.it/archives/53165">report 'Technologies, Cultural Heritage and Culture' published on  March 2019</a> by IRPET. A vocabulary for each Key Enabling Technology (hereafter, KET) was prepared by extracting the relevant concepts, words, technologies and examples from the Platform Report document 'Technologies, Cultural Heritage and Culture, within APPENDIX A. DESCRIPTION OF MAIN TECHNOLOGIES FOR ROADMAP (p. 45-61). Each vocabulary contains a set of terms divided into subdomains. The KETs have been divided into the following six groups: <ul> <li> ICT </li> <li> PHOTONICS, MICRO- AND NANO-ELECTRONICS </li> <li> PLATFORMS </li> <li> NANO AND BIOTECHNOLOGY, ADVANCED MATERIALS </li> <li> PARTICLE ANALYTICAL SYSTEMS </li> </ul> The initial keywords extracted from the document were enriched following the approach based on semantic keyword enrichment based on combination of concurrent keywords and word embeddings  (Duran-Silva et al., 2019; Duran-Silva et al., 2021). This second vocabulary has to be used in combination with the Cultural Heritage vocabulary to capture KETs within the domain of cultural heritage.   ## Use of the controlled vocabulary The definition of the vocabulary does not, per se, allow identifying STI contributions to the domain: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated ambiguous keywords. SIRIS Academic has developed the <a href="https://github.com/sirisacademic/VocTagger">voc_tagger tool</a>, a multiprocess information extraction system able to identify hidden knowledge in textual documents using “controlled vocabularies”, openly available at GitHub and compatible with these controlled vocabularies.   ## Bibliography Harpring, P. (2010). Introduction to controlled vocabularies: terminology for art, architecture, and other cultural works. Getty Publications. Nicolau Duran-Silva, Enric Fuster, Francesco Alessandro Massucci, César Parra-Rojas, Arnau Quinquillà, Fernando Roda, Bernardo Rondelli, Nicandro Bovenzi, & Chiara Toietta. (2021). A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5591987 Duran-Silva, Nicolau, Fuster, Enric, Massucci, Francesco Alessandro, & Quinquillà, Arnau. (2019). A controlled vocabulary defining the semantic perimeter of Sustainable Development Goals (1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3567769

opencc-by-sa-4.0Nov 2021View details →

dryad32/100

Data from: Research culture: a survey of travel behaviour among scientists in Germany and the potential for change

Awareness of the environmental impact of conferences is growing within the scientific community. Here we report the results of a survey in which scientists in Germany were asked about their attendance at conferences, their reasons for attending, and their willingness to explore new approaches that would reduce the impact of conferences on the environment. A majority of respondents were keen to reduce their own carbon footprint and were willing to explore alternatives to the traditional conference.

opencc-zeroMay 2020View details →

zenodo32/100

Supplementary material 2 from: Garcia Rodrigues J, Conides A, Rivero Rodriguez S, Raicevich S, Pita P, Kleisner K, Pita C, Lopes P, Alonso Roldán V, Ramos S, Klaoudatos D, Outeiro L, Armstrong C, Teneva L, Stefanski S, Böhnke-Henrichs A, Kruse M, Lillebø A, Bennett E, Belgrano A, Murillas A, Sousa Pinto I, Burkhard B, Villasante S (2017) Marine and Coastal Cultural Ecosystem Services: knowledge gaps and research priorities. One Ecosystem 2: e12290. https://doi.org/10.3897/oneeco.2.e12290

Correspondence between our classification and labels for marine and coastal CES as found in the literature

opencc-by-4.0May 2017View details →

zenodo32/100

Culture Collaboratory. Virtual Workspace for Interdisciplinary Collections Research and Management (Film)

This short video presents the design concept of »Culture Collaboratory«, an interdisciplinary and interactive research platform and collections management system for museum professionals. »Culture Collaboratory« provides a virtual workspace that supports interdisciplinary collaboration and allows researchers to engage with collection objects, organize research processes, and share their knowledge.

opencc-by-nc-nd-4.0Sep 2017View details →

zenodo32/100

Serbian Intangible Cultural Heritage in the Western Balkans: Perils and Prospects of Inclusive Research and Safeguarding (SICHWEB) project 1st year datasets

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →

zenodo32/100

MATERIALS OF ARCHAEOLOGICAL RESEARCH OF THE ARCHAEOLOGICAL HERITAGE OBJECT CULTURAL LAYER OF VORONEZH ON THE TERRITORY OF THE LAND PLOT AT 1, KLUBNAYA STREET AND 30D, 20-LETIYA OKTYABRYA STREET

Archaeological research on the land plot at the address: Voronezh, 1, Klubnaya Street, and 30d, 20-letiya Oktyabrya Street, was carried out as part of the implementation of the section of project documentation to ensure the safety of the identified cultural heritage site "Cultural layer of the city of Voronezh". As a result of the work, additional information was obtained on the history of the coastal part of the city in the XIX-th-XX-th centuries.

opencc-by-4.0Mar 2023View details →

ClinicalTrials.gov32/100

Cultural Congruence in International Genetics Research

ClinicalTrials.gov study NCT00767858. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

A Cross-Cultural Assessment of the Motivations of Healthy Participants in Phase I Research

ClinicalTrials.gov study NCT01485250. IPD Sharing: Not stated. Countries: 3. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

Comparative Effectiveness Research to Improve the Health of Sexual and Gender Minority Patients Through Cultural Competence and Skill Training of Community Health Center Providers and Non-clinical Sta

ClinicalTrials.gov study NCT03554785. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record