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A report by the Software Sustainability Institute (www.software.ac.uk) (SSI) on the Arts and Humanities Research Council (UKRI AHRC) community to better understand views on digital/software tools, experience of development of such tools, practices, learning intentions and preferences around how projects involving digital/software should be resourced. The report was originally intended to help inform the digital infrastructure funding being envisaged by the AHRC. In its final form we believe it's of use and interest to the AHRC and other arts and humanities funders, organisations which support arts and humanities research and practice, communities of practice, members of AHRC's community and to individuals wanting to learn more about these topics whether to inform their own practice or to understand the space.

Reproducible research (Reproducible research is when data can be reanalysed taking the same steps and producing the same or similar result) is being implemented at different speeds in different disciplines, and Archaeology, as a discipline that sits at the intersection of the sciences and humanities, is at the start of this journey. Enabling reproducibility of your work by others is an important step in ensuring research quality. There are currently many barriers to moving towards reproducible research such as upskilling researchers in the practices, software and infrastructure needed to do reproducible research and also the need to address how we can, as a discipline, deal with issues like sensitive data. In this article, we seek to introduce reproducible research in an understandable manner so that archaeological researchers can learn where and how to start with this approach. We describe what reproducible archaeological research can look like and suggest three different computational skill levels of constructing reproducible research workflows (a research workflow is the different parts of a research lifecycle such as data collection, data analysis, data archiving, etc, and making all stages reproducible by using a history tracking system (version control) and transparent documation). Finally, in an extensive appendix, we address common questions about reproducible research to remove the stigma about these issues and suggest ways to overcome them.

FCL and AS were supported by the Swedish Research Council (Vetenskapsrådet, grant no. 2018-01272). FCL conducted the work with this article as a Pro Futura Scientia XIII Fellow funded by the Swedish Collegium for Advanced Study through Riksbankens Jubileumsfond. WT was supported by the German Research Foundation (DFG, TE 613/3-1). AD received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 677152). AH was supported by Riksbankens Jubileumsfond (grant no. IN20-0026). MD-D was funded by the Dutch Research Council (Nederlandse organisatie voor Wetenschappelijk Onderzoek, grant no. 016.Veni.195.502). TK, MR, and UB were supported the SustES project – “Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions” (CZ.02.1.01/0.0/0.0/16_019/0000797). LS was supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2150 ROOTS – 390870439. FL was supported by a European Research Council (ERC) Synergy Grant (4-OCEANS; grant agreement no. 951649) under the European Union’s Horizon 2020 research and innovation programme. Although variations in building activity are a useful indicator of societal well-being and demographic development, historical datasets for larger regions and longer periods are still rare. Here, we present 54,045 annually precise dendrochronological felling dates from historical construction timber from across most of Europe between 1250 and 1699 CE to infer variations in building activity. We use geostatistical techniques to compare spatiotemporal dynamics in past European building activity against independent demographic, economic, social and climatic data. We show that the felling dates capture major geographical patterns of demographic trends, especially in regions with dense data coverage. A particularly strong negative association is found between grain prices and the number of felling dates. In addition, a significant positive association is found between the number of felling dates and mining activity. These strong associations, with well-known macro-economic indicators from pre-industrial Europe, corroborate the use of felling dates as an independent source for exploring large-scale fluctuations of societal well-being and demographic development. Three prominent examples are the building boom in the Hanseatic League region of northeastern Germany during the 13th century, the onset of the Late Medieval Crisis in much of Europe c. 1300, and the cessation of building activity in large parts of central Europe during armed conflicts such as the Thirty Years’ War (1618–1648 CE). Despite new insights gained from our European-wide felling date inventory, further studies are needed to investigate changes in construction activity of high versus low status buildings, and of urban versus rural buildings, and to compare those results with a variety of historical documentary sources and natural proxy archives. Publisher PDF Peer reviewed

This article presents the history of the establishment and development of the Institute of Molecular Biology at Jagiellonian University, describes the reasons for the transformation of the Institute into the Faculty of Biochemistry, Biophysics and Biotechnology, and provides information on the current scientific and teaching potential of the Faculty and prospects for further development.

Although variations in building activity are a useful indicator of societal well-being and demographic development, historical datasets for larger regions and longer periods are still rare. Here, we present 54,045 annually precise dendrochronological felling dates from historical construction timber from across most of Europe between 1250 and 1699 CE to infer variations in building activity. We use geostatistical techniques to compare spatiotemporal dynamics in past European building activity against independent demographic, economic, social and climatic data. We show that the felling dates capture major geographical patterns of demographic trends, especially in regions with dense data coverage. A particularly strong negative association is found between grain prices and the number of felling dates. In addition, a significant positive association is found between the number of felling dates and mining activity. These strong associations, with well-known macro-economic indicators from pre-industrial Europe, corroborate the use of felling dates as an independent source for exploring large-scale fluctuations of societal well-being and demographic development. Three prominent examples are the building boom in the Hanseatic League region of northeastern Germany during the 13th century, the onset of the Late Medieval Crisis in much of Europe c. 1300, and the cessation of building activity in large parts of central Europe during armed conflicts such as the Thirty Years’ War (1618–1648 CE). Despite new insights gained from our European-wide felling date inventory, further studies are needed to investigate changes in construction activity of high versus low status buildings, and of urban versus rural buildings, and to compare those results with a variety of historical documentary sources and natural proxy archives.

Although variations in building activity are a useful indicator of societal well-being and demographic development, historical datasets for larger regions and longer periods are still rare. Here, we present 54,045 annually precise dendrochronological felling dates from historical construction timber from across most of Europe between 1250 and 1699 CE to infer variations in building activity. We use geostatistical techniques to compare spatiotemporal dynamics in past European building activity against independent demographic, economic, social and climatic data. We show that the felling dates capture major geographical patterns of demographic trends, especially in regions with dense data coverage. A particularly strong negative association is found between grain prices and the number of felling dates. In addition, a significant positive association is found between the number of felling dates and mining activity. These strong associations, with well-known macro-economic indicators from pre-industrial Europe, corroborate the use of felling dates as an independent source for exploring large-scale fluctuations of societal well-being and demographic development. Three prominent examples are the building boom in the Hanseatic League region of northeastern Germany during the 13th century, the onset of the Late Medieval Crisis in much of Europe c. 1300, and the cessation of building activity in large parts of central Europe during armed conflicts such as the Thirty Years’ War (1618–1648 CE). Despite new insights gained from our European-wide felling date inventory, further studies are needed to investigate changes in construction activity of high versus low status buildings, and of urban versus rural buildings, and to compare those results with a variety of historical documentary sources and natural proxy archives.

Objectives To investigate recorded poor insight in relation to mental health and service use outcomes in a cohort with first-episode psychosis. Design We developed a natural language processing algorithm to ascertain statements of poor or diminished insight and tested this in a cohort of patients with first-episode psychosis. Setting The clinical record text at the South London and Maudsley National Health Service Trust in the UK was used. Participants We applied the algorithm to characterise a cohort of 2026 patients with first-episode psychosis attending an early intervention service. Primary and secondary outcome measures Recorded poor insight within 1 month of registration was investigated in relation to (1) incidence of psychiatric hospitalisation, (2) odds of legally enforced hospitalisation, (3) number of days spent as a mental health inpatient and (4) number of different antipsychotic agents prescribed; outcomes were measured over varying follow-up periods from 12 months to 60 months, adjusting for a range of sociodemographic and clinical covariates. Results Recorded poor insight, present in 48.9% of the sample, was positively associated with youngest and oldest age groups, unemployment and schizophrenia (compared with bipolar disorder) and was negatively associated with Asian ethnicity, married status, home ownership and recorded cannabis use. It was significantly associated with higher levels of all four outcomes over the succeeding 12 months. Associations with hospitalisation incidence and number of antipsychotics remained independently significant when measured over 60 and 48 months, respectively. Conclusions Recorded poor insight in people with recent onset psychosis predicted higher subsequent inpatient mental healthcare use. Improving insight might benefit patients’ course of illness as well as reduce mental health service use.

{"references": ["Developing Data Attribution and Citation Practices and Standards: Summary of an International Workshop - https://www.nap.edu/read/13564/chapter/1", "State of the art report on open access publishing of research data in the humanities from Has (Humanities At Scale) project - https://hal.archives-ouvertes.fr/halshs-01357208", "CODATA report - http://datascience.codata.org/articles/abstract/10.2481/dsj.OSOM13-043", "http://www.nap.edu/catalog/13564/for-attribution-developing-data-attribution-and-citation-practices- and-standards", "The Problem of Citation in the Digital Humanities / Jonathan Blaney https://www.dhi.ac.uk/openbook/chapter/dhc2012-blaney", "Data citation practices across earth, life, social sciences and humanities - opportunities for OpenAIRE https://zenodo.org/record/54570/files/Data-Citation.pdf", "Chen, Xiaoli. (2017, October). Where are we with Data Citation. Zenodo. http://doi.org/10.5281/zenodo.1004744", "Graef, Florian, & McEntyre, Jo. (2016). OpenAIRE2020 D7.5 \u2013 Data citation standards and index requirements. Zenodo. https://doi.org/10.5281/zenodo.1257267", "Dorch, S. B. F. (2012, July 5). On the Citation Advantage of linking to data. Zenodo. http://doi.org/10.5281/zenodo.6772", "Parsons, Mark A., & Fox, Peter A. (2014). Why Data Citation Currently Misses the Point. Zenodo. http://doi.org/10.5281/zenodo.1241521", "Hourcl\u00e9, J., Chang, W., Linares, F., Palanisamy, G., & Wilson, B. (2012). Linking Articles to Data. Zenodo. http://doi.org/10.5281/zenodo.13802", "Kristian Garza. (2018, October). Exploring data citation in Crossref and DataCite's Event Data service. Zenodo. http://doi.org/10.5281/zenodo.1472279", "Parsons, Mark A., Duerr, Ruth E., & Jones, Matthew B. (2019). The History and Future of Data Citation. Zenodo. http://doi.org/10.5281/zenodo.2619468", "Hourcl\u00e9, J. (2014). Data Citation in Astronomy. Zenodo. http://doi.org/10.5281/zenodo.10505", "Wright, C., Hodson, S., Deventer, M. van ., Selematsela, D., L\u00f6tter, L., Vahed, A., ... Walt, I. van . der . (2016, January). CODATA Data Citation Workshop, South Africa: Presentations. Zenodo. http://doi.org/10.5281/zenodo.44946", "Rueda, Laura. (2016, November). Making research better by enabling people to find, share, use and cite data. Zenodo. http://doi.org/10.5281/zenodo.168213", "Hourcl\u00e9, Joseph. (2015, April). Data Realities: Citation Equals Funding. Zenodo. http://doi.org/10.5281/zenodo.2654574", "LIBER Research Data Group https://www.openaire.at/fileadmin/user_upload/openaire/LIBER_The_research_data_group_2012_v7_fina l.pdf", "Librarians' Competencies Profile for Research Data Management, 2016, https://www.coar- repositories.org/files/Competencies-for-RDM_June-2016.pdf", "The Anatomy of a Data Citation: Discovery, Reuse, and Credit https://academiccommons.columbia.edu/doi/10.7916/D8MW2STM", "Credit data generators for data reuse https://www.nature.com/articles/d41586-019-01715-4", "Data Citation as a computational problem https://frew.eri.ucsb.edu/private/preprints/bdf-cacm-data- citation.pdf", "Mary Vardigan: Data Citation for the Social Sciences, https://www.nap.edu/read/13564/chapter/9", "Sieber, Joan E. & Bruce E. Trumbo (1995). \"(Not) giving credit where credit is due: Citation of data sets\". Science and Engineering Ethics 1: 1, 11\u201320.", "Dodd, Sue A. (1979). \"Bibliographic References for Numeric Social Science Data Files: Suggested Guidelines\". Journal of the American Society for Information Science 30: 2, 77\u201382.", "Marwick, B. and S. E. Pilaar Birch (2018) A Standard for the Scholarly Citation of Archaeological Data. Advances in Archaeological Practice https://doi.org/10.1017/aap.2018.3", "Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., ... Bourne, P. E. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18", "\u00abPresentation of Data Papers\u00bb, Cybergeo : European Journal of Geography [En ligne], Data papers, http://journals.openedition.org/cybergeo/28922", "Brill Research Data Journal https://brill.com/view/journals/rdj/rdj-overview.xml?lang=en", "The Web Annotation Data Model (W3C 2017a, 2017b)", "Facilitating Fine-grained Open Annotations of Scholarly Sources. / Boot, P.; Haentjens Dekker, R.; Koolen, Marijn; Melgar, Liliana. https://pure.knaw.nl/portal/en/publications/facilitating-finegrained-open- annotations-of-scholarly-sources(c4fda9e2-bb42-4e8a-9c35-1361b89e0ee6).html", "Europe's Digital Humanities landscape https://doi.org/10.5281/zenodo.3247285", "The Language Resource Switchboard, Zinn, Claus, Computational Linguistics 2018 vol. 44 num. 4, p631- 639, retrieved from https://doi.org/10.1162/coli_a_00329", "Klein, M., Shankar, H., and Van de Sompel, H. (2018) Signposting for Repositories. Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL 2018. https://doi.org/10.1145/3197026.3203879", "Andreas Rauber; Ari Asmi; Dieter van Uytvanck; Stefan Proell (2015): Data Citation of Evolving Data: Recommendations of the Working Group on Data Citation (WGDC). DOI: 10.15497/RDA00016 https://zenodo.org/record/1406002#.XG2FVOhKhaQ"]} The SSHOC project aims to build the SSH (Social Science and Humanities) part of the EOSC (European Open Science Cloud). One of the main goals of the project is to ensure that SSH will be present in EOSC and that their specifics are taken into account. In this regard, an important point is to be able to give high visibility to the research data used in Social Science and Humanities following FAIR data principles. This can be achieved by fostering “Data Citation” through providing a common mechanism to cite SSH data and build stronger links between data and publications. An expected side effect would be to enhance the reproducibility of SSH research, which is not very common nowadays. In line with these goals, this report delivers an overview of existing data-citing mechanisms intended for citing data, that are currently used in different communities with a focus on the SSH. We then provide some guidelines describing what we think is relevant in the SSHOC context with respect to the technology to be implemented and also about the structuring of the content. This report aims to prepare further work in common with other SSHOC tasks and Work Packages. Currently, we see the following links with other SSHOC tasks: SSHOC task 3.6 will generalise and integrate the CLARIN Language Resource Switchboard (Switchboard) for the infrastructure of the other SSHOC stakeholders. The Switchboard will also make citations human actionable. SSHOC task 5.2 works on a SSHOC version of the DataVerse repository system for the SSH. The citations function in SSHOC DataVerse should align with the recommendations put forward in this document. collaboration in engagement and training activities of WP6; it is essential to be able to give a solid overview of practices and recommendations in the SSHOC training and outreach effort. WP6 will coordinate targeted training in the Social Sciences and Humanities. Appropriate audiences will be targeted for a workshop and a webinar dedicated to “Data Citation principles and practice”. WP7 is creating the future SSHOC Marketplace. This Marketplace will, of course, integrate SSH data sets and associated services. In this respect, a system that allows an extensive scientific description of data, strong identification of provenance and information that makes it possible to associate a relevant tool to a data set and make it actionable, would prove useful. This kind of information should be provided by Data Citation recommendations in cooperation with WP7 and other Work Packages. WP8, more specifically on certification aspects provided by task 8.2, “Trust and Quality Assurance”. Naturally, we should be involved in SSHOC activities whenever citation expertise is called on. This deliverable has been accepted by the European Commission on - 03 November 2020