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IsSupplementTo: De Vitis, M., Tabanelli, M., Breveglieri, R., Filippini, M., Galletti, C., Fattori, P. (2022). The Superior Parietal Lobule of Macaque Monkey: Relative Influence of Gaze and Static Arm Position during Reaching. Eneuro, 9(1), ENEURO.0362-21.2021. (https://doi.org/10.1523/ENEURO.0362-21.2021) IsSupplementTo: Hadjidimitrakis, K., De Vitis, M., Ghodrati, M., Filippini, M., Fattori, P. (2022). Anterior-posterior gradient in the integrated processing of forelimb movement direction and distance in macaque parietal cortex. Cell Reports, 41(6), 111608. (https://doi.org/10.1016/j.celrep.2022.111608) IsSupplementTo: Filippini, M., Borra, D., Ursino, M., Magosso, E., Fattori, P. (2022). Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks. Neural Networks, 151, 276–294. (https://doi.org/10.1016/j.neunet.2022.03.044) IsSupplementTo: Diomedi, S., Vaccari, F. E., Galletti, C., Hadjidimitrakis, K., & Fattori, P. (2021). Motor-like neural dynamics in two parietal areas during arm reaching. Progress in Neurobiology, 205, 102116. (https://doi.org/10.1016/j.pneurobio.2021.102116) IsSupplementTo: Diomedi, S., Vaccari, F. E., Filippini, M., Fattori, P., Galletti, C. (2020). Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand Reaching. IScience, 23(10), 101616. (https://doi.org/10.1016/j.isci.2020.101616) Six electrophysiological datasets were recorded from three regions (V6A, PEc, PE) within the superior parietal lobule (SPL) of two Macaca fascicularis monkeys during a task involving instructed-delay foveated reaching

This dataset containts the underlying data that was used to analyse the SmartCulTour identified interventions that were described under the production of abstracts and practice videos of D6.2. The data collection established: Context & background Reason why of the intervention Resources and tools Expected economic impact Expected social impact Expected cultural impact Expected environmental impact Success conditions These criteria were analysed by the SmartCulTour local experts and meant to feed into the updated taxonomy on cultural tourism interventions of D3.4. The interventions that are described are linked to the practice videos that can be found under the Living Labs section of the SmartCulTour website (www.smartcultour.eu) and pertain to: Rotterdam Living Lab: Planning for the future of Hoek van Holland & Bospolder-Tussendijken Scheldeland Living Lab: Hof van Coolhem: social employment and care project in tourism Scheldeland Living Lab: Bornem Castle: upgrades historical exhibitions & creates visitor centre Scheldeland Living Lab: Steam train Dendermonde-Puurs: volunteers protecting industrial heritage Utsjoki Living Lab: Traces in Utsjoki: inspiring respectful visitor behaviour in nature areas Utsjoki Living Lab: Placemaking as a technique to support meaningful visitor experiences Huesca Living Lab: The Somontano Wine Route: a resilient strategy for Huesca Huesca Living Lab: The Río Vero Cultural Park. From Palaeolithic human history to the present Split Living Lab: Making traditional Easter bread-Sirnica in Solin Split Living Lab: The cultural heritage of Sinj: the story of Alka Vicenza Living Lab: Vicenza: the city of Palladio Vicenza Living Lab: The international library "La Vigna" becomes an open innovation Living Lab

The interviews were part of the Marie Skłodowska-Curie research project “Digital Humanities Laboratory: Studying the Entanglement of Infrastructure and Technology in Knowledge Production”. The project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891155. The goal of the project was to investigate the mechanism of knowledge production in digital humanities and the influence of infrastructure on social and research practices in a laboratory. It aimed to contribute to understanding these complex processes and the development and improvement of digital humanities infrastructure. A set of interviews were conducted with members of King’s Digital Lab, management staff and researchers involved in the lab work. Discussions revolved around the institutional, research, and socio-technical infrastructure of King’s College London and King’s Digital Lab; the social practices of digital projects; the project lifecycle; the use of digital tools in everyday research practices; and the conditions and challenges of conducting research with regards to the lab management, organisation, location, policies, access to resources, and facilities. These files are the transcripts of the interviews that took place in 2021. Research website: https://dhinfra-org.github.io/

This dataset corresponds to the pollen-based REVEALS estimates of 25 plant taxa for Europe and associated standard errors, published in Marquer et al. (2017). This is part of the results from the Swedish project LandClim I (Gaillard et al., 2010; Trondman et al., 2015; Marquer et al., 2014, 2017).The study area includes a large part of northern and Central Europe, i.e. Ireland, Great Britain and a latitudinal transect from the Alps in the south to northernmost Norway.These REVEALS estimates are based on 151 pollen records (small/large, lakes/bogs/mires) that were selected from the European Pollen Database (Fyfe et al., 2009; Giesecke et al., 2014), the Alpine Palynological Data-Base (University of Bern, Switzerland), or were provided directly by individual data contributors.The selected pollen records are grouped into 36 1° x 1° grid-cells.Twenty-five consecutive time windows over the last 11,700 years BP are used: 0-100, 100-350, 350-700 BP for the three first time windows, and 500 calendar years each from 700 to 11,700 BP.For details about the REVEALS model, see Sugita (2007).In the excel file, the folder "Metadata" contains the explanation of abbreviations in the data folders and information about the pollen records used for the REVEALS reconstructions. All REVEALS estimates and their SEs are given in proportions of the grid cell (the total of all REVEALS estimates sum up to 1). The codes of the 25 consecutive time windows are given in the folder "Code time windows". The results of the 36 grid cells are in the folder "REVEALS 36GCs" and the related standard errors in the folder "SE_REVEALS 36GCs". Note that in the folder "Metadata", the GPS coordinates correspond to the upper left (NW) corners of each grid cell. Supplement to: Marquer, Laurent; Gaillard, Marie-José; Sugita, Shinya; Poska, Anneli; Trondman, Anna-Kari; Mazier, Florence; Nielsen, Anne Birgitte; Fyfe, Ralph M; Jönsson, Anna Maria; Smith, Benjamin; Kaplan, Jed O; Alenius, Teja; Birks, H John B; Bjune, Anne Elisabeth; Christiansen, Jörg; Dodson, John; Edwards, Kevin J; Giesecke, Thomas; Herzschuh, Ulrike; Kangur, Mihkel; Koff, Tiiu; Latalowa, Malgorzata; Lechterbeck, Jutta; Olofsson, Jörgen; Seppä, Heikki (2017): Quantifying the effects of land use and climate on Holocene vegetation in Europe. Quaternary Science Reviews, 171, 20-37

The PretoxTM Corpus is a gold standard corpus of preclinical treatment-related findings annotated from toxicology reports. Example documents annotated by domain experts, also known as a gold standard corpus, are needed in order to develop, train, and validate text mining tools. To this aim we designed and performed an annotation activity for the development of the corpus of treatment-related findings: the PretoxTM corpus. A treatment-related finding expression enclose several named entities; the most relevant one is the abnormal effect detected; which depending on the study domain of the finding, can be given by a measurement, test, or examination named Study Test and an abnormal Manifestation result obtained for that study test; or by an abnormal Finding in study domains where there is no associated test or measurement (e.g., clinical, macroscopic and microscopic). Other related named entities that could be present to complete the treatment-related finding are; the Specimen of the abnormal observation, the Sex of the subject, the Group of subjects in which the observation was detected and the Dose level administration of the compound. Examples of sentences with treatment-related findings are: “The decrease in food consumption and body weight of the animals from the mid dose onwards is regarded as evidence of general toxicity.” and "At dose level 3, absolute and relative liver weights were increased in male rats.”. Contributions: The PretoxTM corpus was developed by BSC, with the contribution of IMIM and a team of experts from the eTRANSAFE EFPIA partners. License: Creative Commons Attribution-ShareAlike 4.0 International (cc by sa 4.0). The PretoxTM resources have been developed as part of the eTRANSAFE project. For more information about PretoxTM please visit: PretoxTM Corpus Gitlab: https://gitlab.bsc.es/inb/etransafe/preclinical-toxicological-corpus/ PretoxTM central documentation: https://pretoxtm.gitlab.io/documentation/

This repository contains the datasets created and extracted for the paper: Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022. "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning". (Available online at http://gmql.eu/coveffect) -------------------------------------------------------------------------------- LIST OF FILES WITH DESCRIPTION: -------------------------------------------------------------------------------- AdditionalFile1-effects-taxonomy: Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy. AdditionalFile2-levels-taxonomy: Descriptions of legal values for the 'Level' field. AdditionalFile3-training_dataset_target: List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple). AdditionalFile4-validation_dataset_target: List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model. For each abstract, target tuples follow the schema defined for AdditionalFile3. AdditionalFile5-validation_dataset_highlighted: Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow. AdditionalFile6-validation_dataset_prediction: List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d). AdditionalFile7-keywords_query_list: Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'. AdditionalFile8-CORD-19_batch_dataset_metadata: Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7. These abstracts have been annotated by the prediction framework. AdditionalFile9-CORD-19_batch_dataset_prediction: List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19. AdditionalFile10-test_dataset_target: List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8. For each abstract, target tuples follow the schema defined for AdditionalFile3. AdditionalFile11-test_dataset_prediction: List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).

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 Date of data collection: 27/11/2018 Geographic location of data collection: Salerno, Italy. The venue of the data collection is Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy Activity of data collection: Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018 Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions 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 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 Researcher translating to English, transcribing data in the digital tabular dataset, and cleaning the data: Nadia Pintossi Original language of the data: English, Italian, and mix of English and Italian {"references": ["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", "Pintossi, N. (2022). Challenges and potential solutions for cultural heritage adaptive reuse: A comparative study employing the Historic Urban Landscape approach (Bouwstenen). Eindhoven University of Technology. https://research.tue.nl/files/205757720/20220706_Pintossi_hf.pdf"]}

The file contains the data used in the case studies of the paper "The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research" published on ORE.

The study was undertaken in a contemporary oil easel painting which was temporarily loaned from the Restoration-Conservation Laboratory of the Faculty of Fine Arts of the University of Barcelona. Different varnish removal procedures were assessed over this painting, and, in order to analyse and compare the efficiency of the methods, several non-invasive and invasive techniques were applied. This dataset consists of images of the artwork (general and detail) and the results of the different cleaning procedures; the varnish removal test conditions performed; Laser-induced Breakdown Spectroscopy (LIBS) spectra; Laser-induced Fluorescence (LIF) spectra; and Fourier-transform infrared spectroscopy (FTIR Spectroscopy). This information allows for assessing the results achieved with each cleaning methodology. Images are presented in JPG format. All spectra are presented in CSV format, on a single page. Descriptions of the selected areas and the experimental conditions in which the spectra were taken and the name of the column values are included at the top of each page. To illustrate the artwork, we took one image of the painting considered in the study (Fig. 1) and a general image under UV light of the picture (Fig. 2). Additionally, we can see the outcomes of varnish removal in the detail photos digital microscopy images (Fig. 3), and in detail UV light photograph of one of the cleaned areas (Fig. 4). For kinetic LIBS, 1 file per each uncleaned and cleaned by different methods area is included. Each file is composed of 41 columns, the first one (x) for wavelength and, the other 40 (y) for intensity. For LIF, 1 file per sample of the analysis of the fluorescence of each un/cleaned area. Each file is composed of 2 columns (wavelength and intensity). For FTIR, 1 file for the characterisation of the varnish, which is composed of 2 columns, the first one indicates the wavenumber (x), and the second one the transmittance (y). [EN] The removal of aged varnish on artistic paintings is a delicate intervention and the use of UV laser for this purpose is of special relevance. In particular, the use of nanosecond-pulsed lasers operating at 213 nm has been noted to produce good results in mock-up samples, but it has not been tested in real artworks. In this paper, we report on the application of this procedure for the first time on a contemporary oil easel painting with naturally aged varnish. The obtained results were compared with those achieved using traditional solvents, specifically a mixture of ligroin:acetone. Additionally, hot water was used to remove surface dirt. The performance of the different cleaning procedures was assessed with a range of techniques, including low-power microscopy, UV lamp illumination, laser-induced fluorescence (LIF), and laser-induced breakdown spectroscopy (LIBS). Of the tested treatments, the best performance is obtained by nanosecond laser irradiation at 213 nm using an adequate laser fluence (typically 0.14 J/cm2) that allows controlled and efficient removal of the outermost aged varnish layer without affecting the underlying non-aged varnish and paint layers. There are 4 folders that correspond to each technic employed for the analysis of the cleaning procedures, and 1 for the cleaning conditions description. The folder titled "PHOTOS" contains: Fig. 1 Image of the picture. Selected cleaned areas; Fig. 2 UV light image of the artwork; Fig. 3 Digital microscopy of area 6 (before and after cleaning); Fig. 4 Fluorescence under UV lamp before and after cleaning. The folder titled “LIBS” contains: LIBS_withoutcleaning; LIBS_deionizedwater; LIBS_solventmixture; LIBS_213nm,0.10 Jcm2; LIBS_213nm,0.14 Jcm2. The folder for “LIF” contains: LIF_withoutcleaning; LIF_deionizedwater; LIF_solventmixture; LIF_213nm,0.10 Jcm2; LIF_213nm,0.14 Jcm2. The folder “FTIR” contains: FTIR_varnish. The folder "Cleaning conditions description" contains the file "Cleaning treatments". This research was funded by the Spanish State Research Agency (AEI) through the project SCOCHLAS (Study and Conservation of Cultural Heritage with Lasers, PID2019-04124RBI00/AEI/1013039/501100011033), by the Community of Madrid through the project Top Heritage- CM (Tecnologías en Ciencias del Patrimonio, S2018/NMT_4372), and by the H2020 project IPERION HS (Integrated Platform for the European Research Infrastructure ON Heritage Science, GA 871034). L. Maestro-Guijarro’s participation was financed by a Youth Guarantee contract (CAM20_IQFR_AI_06) from the Community of Madrid. Peer reviewed