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Figures 135–143. Cybocephalus skelleyi T. R. Smith. 135 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 135–143. Cybocephalus skelleyi T. R. Smith. 135) Antenna. 136) Proleg. 137) Mesoleg. 138) Metaleg. 139) Median lobe, as it appears in holotype. 140) Median lobe, lateral view, as it appears in the holotype. 141) Basal plate. 142) Median lobe, hypothetical reconstruction. 143) Median lobe, lateral view, hypothetical reconstruction.
Supplemental Data and Code for "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE"
<p>This dataset enables the user to repeat the analyses presented in the manuscript "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE." It is comprised of two compressed archives: one which contains code, and one which contains data.</p>
Los Alamos National Laboratory (LANL) and Geostationary Operational Environmental Satellite (GOES)–R Geosynchronous Particle Data for Ferradas, C. P., et al. (2023)
<p>This repository contains data from a set of charged particle analyzers onboard the Los Alamos National Laboratory (LANL) and the Geostationary Operational Environmental Satellite (GOES)–R geosynchronous orbit satellites. The data are used in the following open access publication submitted to Frontiers in Astronomy and Space Sciences.</p> <p>Ferradas, C. P., M.-C. Fok, N. Maruyama, M. G. Henderson, S. Califf, S. A. Thaller, and B. T. Kress (2022), <strong>The effects of particle injections on the ring current development during the 7-8 September 2017 geomagnetic storm</strong>, <em>Frontiers in Astronomy and Space Sciences</em>, <em>submitted.</em></p>
Dataset and R Code for Species-level Avian Influenza Phylogenetic Generalized Least Squares Regression
<p>Dataset for Species-level Avian Influenza Phylogenetic Generalized Least Squares (PGLS) Regression:<br> Variables include taxonomic information for each species, # of IAV-positive individuals, # of IAV-tested individuals, the prevalence of IAV, the proportion of diet made up of different food types, the proportion of foraging time spent in different strata (below water, water surface, ground, understory, etc), sampling-related variables (mean latitude, mean date, the proportion of hatch year individuals), migration and territoriality category, climatologic variables, mean clutch size, and mating system.</p> <p>R Code for PGLS and Avian Influenza Prevalence ContMap.</p>
Effects of exogenous elevation of corticosterone on immunity and the skin microbiome of Eastern Newts (Notophthalmus viridescens): Data & R scripts
<p>The amphibian chytrid fungus, <em>Batrachochytrium</em> <em>salamandrivorans</em> (<em>Bsal</em>) threatens salamander biodiversity. The factors underlying <em>Bsal</em> susceptibility may include glucocorticoid hormones (GCs). The effects of GCs on immunity and disease susceptibility are well studied in mammals, but less is known in other groups, including salamanders. We used <em>Notophthalmus</em> <em>viridescens</em> (Eastern Newts) to test the hypothesis that GCs modulate salamander immunity. We first determined the dose required to elevate corticosterone (CORT; primary GC in amphibians) to physiologically relevant levels. We then measured immunity (neutrophil-lymphocyte ratios, plasma bacterial killing ability [BKA], skin microbiome, splenocytes, melanomacrophage centers [MMCs]) and overall health in newts following treatment with CORT or an oil vehicle control. Treatments were repeated for a short (2 treatments over 5 days) or long (18 treatments over 26 days) time period. Contrary to our predictions, most immune and health parameters were similar for CORT and oil-treated newts. Surprisingly, differences in BKA, skin microbiome, and MMCs were observed between newts subjected to short and long-term treatments, regardless of treatment type (CORT, oil vehicle). Taken together, CORT does not appear to be a major factor contributing to immunity in Eastern Newts, although more studies examining additional immune factors are necessary.</p>
Fig. 9. R in To Registrations Of Bottom Macroinvertebrates, Fish, Amphibians And Reptiles Of The Upper Tskhenistskali River Basin (Georgia, Lower Svanetia)
Fig. 9. R. macrocnemis, registration points and habitat in the vicinity Lentekhi town and Sasashi village, Lower Svanetia; specimens are also shown in the table of records (table 2).
CAP1 - Planning and control of asphalt production - Planning algorithm implemented in R
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Data and R code for: Survival to weaning in arid-country vervet monkeys
<p>We addressed the relative contributions of maternal rank and sociability to the survival of infant vervet monkeys (Chlorocebus pygerythrus) to nutritional independence (~210 days) in a resource-poor environment. Data from 153 infants across three troops and 10 birth cohorts indicated a pre-weaning mortality of 30% (Range: 9%-85%), with a median age at death of 50 days. In addition to the consequences of resource availability, increased infant survival was independently and equivalently associated with higher maternal rank and a greater number of maternal spatial partners. We use this outcome to suggest that apparent discrepancies in the relative importance of different maternal attributes in determining reproductive outcomes may be resolved by considering more closely local sources of infant mortality.</p>
Text-fig. 1. The Catefica exposure along the road between Catefica and Mugideira photographed in 1989 when the mesofossil flora was discovered. The exposed strata are mainly cross-bedded light colored sands, darker horizons of clay and dark lenses with mesofossils. The most productive sample, Catefica sample 49, was collected in the basal part of the exposed sequence (arrow head). One of the authors (PRC) exploring the middle part of the section. Photo K. R. Pedersen. in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 1. The Catefica exposure along the road between Catefica and Mugideira photographed in 1989 when the mesofossil flora was discovered. The exposed strata are mainly cross-bedded light colored sands, darker horizons of clay and dark lenses with mesofossils. The most productive sample, Catefica sample 49, was collected in the basal part of the exposed sequence (arrow head). One of the authors (PRC) exploring the middle part of the section. Photo K. R. Pedersen.
Data and R code from: Haemosporidian infections influence risk-taking behaviours in young male blackcaps Sylvia atricapilla
<p>This repository contains all data and code necessary to reproduce the results and figures of the paper:</p> <p>Remacha, C., Ramírez, A., Arriero, E. and Pérez-Tris, J. 2023. Haemosporidian infections influence risk-taking behaviours in young male blackcaps <em>Sylvia atricapilla</em>. Animal Behaviour, 196, 113-126. <a href="https://doi.org/10.1016/j.anbehav.2022.12.001">https://doi.org/10.1016/j.anbehav.2022.12.001</a></p> <p>The repository contains a readme file (README_SYAT_MS_ANIBEH_Scripts.txt) with a description of the code and the data. The code is organised in eight R script files. Instructions to run the code are provided in the readme file. The data are organised in two separate files. One file (SYAT_MS_BH_ANIBEHdata.txt) contains data of exploratory and antipredatory behaviours of 43 young male blackcaps. The other one (SYAT_MS_BH_BIOL_ANIBEHdata.txt) contains biological and experimental attributes of the same individuals: status and intensity of parasite infection, experimental treatment, morphology and body mass.</p>
Codes in R for spatial statistics analysis, ecological response models and spatial distribution models
<p>In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg <em>et al.</em>, 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).</p> <p>It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter <em>et al.</em>, 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (<em>e.g.</em>, Naoki <em>et al.</em>, 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:</p> <p>In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin <em>et al. </em>(2012) recommend the use of 10,000 background points when using regression methods (<em>e.g.</em>, Generalized Linear Model) or distance-based models (<em>e.g.</em>, DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (<em>e.g.</em>, bioclimatic, topographic, demographic, habitat) in function of presence and background points (<em>e.g.</em>, Hijmans and Elith, 2017).</p> <p>Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).</p> <p>After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (<em>e.g.</em>, Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.</p> <p>Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (<em>e.g.</em>, Awan <em>et al.</em>, 2021).</p> <p>Once the above codes were run, we uploaded the same subgroups (<em>i.e.</em>, presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the <em>p</em>-significance value of each variable (alpha ≤ 0.05); we selected the value one (<em>i.e.</em>, presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (<em>e.g.</em>, Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.</p> <p>On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).</p> <p>Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p>Validation set</p> </td> </tr> <tr> <td> <p>Model</p> </td> <td> <p>True</p> </td> <td> <p>False</p> </td> </tr> <tr> <td> <p>Presence</p> </td> <td> <p>A</p> </td> <td> <p>B</p> </td> </tr> <tr> <td> <p>Background</p> </td> <td> <p>C</p> </td> <td> <p>D</p> </td> </tr> </tbody> </table> <p>We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006).</p> <p>The last code (<em>i.e.</em>, <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter <em>et al.</em>, 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.</p> <p>Regarding the model evaluation and estimation, we selected the following estimators:</p> <p>1) partial ROC, which evaluates the approach between the curves of positive (<em>i.e.</em>, correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).</p> <p>2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long <em>et al.</em>, 1988).</p>
Stowaways Supplement S6. Relative Size Index (RSI) Comparative Dataset & R Code for Mediterranean Ship Rats
<p>Relative size index (RSI) comparative dataset and R code used to compare the RSI <em>sensu </em>Lomolino (1985) of the ship rats from the Ma'agan Mikhael B (MMB) shipwreck with other Mediterranean island populations. </p> <p>Worksheet 1: RSI_Dataframe - data frame used in R to produce RSI bar chart.</p> <p>Worksheet 2: RSI Data - Mainland and island samples' mean condylobasal lengths (CBL) and further calculations used to derive the RSI values per island population.</p> <p>Worksheet 3: CBL values for each Provençal and Corso-Sardinian Islet that was combined in the RSI analysis, taken directly from Granjon & Cheylan (1990).</p> <p>Data sources:</p> <p>MMB - Southern Levant (this study)</p> <p>Galite & Zembra Islands - Tunisia (Ibrahim et al., 2017)</p> <p>Congreso Island - Morocco (Ventura & Lopez Fuster, 2000)</p> <p>São Miguel & Terceira Island (Azores) - Portugal (Ramalhinho et al., 1996)</p> <p>Corsica & Provençal Islets - Provence (Granjon & Cheylan, 1990)</p> <p>Sardinia & Corso-Sardinian Islets - Tuscany (Granjon & Cheylan, 1990)</p>
CRDS absorption spectra recorded for the P(16) and P(28) transitions of the 20012-00001 band, and the R(24) and R(30) transitions of the 20013-00001 band of 12CO2 in air
<p>The database is made of the set of the CRDS absorption spectra recorded for the P(16) and P(28) transitions of the 20012-00001 band, and the R(24) and R(30) transitions of the 20013-00001 band of 12CO2 in air, following the procedures and with the experimental setups described in the paper entitled "Line shape parameters of air-broadened 12CO2 transitions in the 2.0 µm region, with their temperature dependence" by D. Mondelain, A. Campargue, H. Fleurbaey, S. Kassi, S. Vasilchenko published in JQSRT 298 (2023) 108485. <br> Each averaged spectrum corresponds to a .csv file including the following columns: <br> absolute wavenumber (in cm-1), cavity loss rate 1/ct (in 10-6 cm-1), temperature (in °C) and total pressure (in Torr).</p> <p>Two gas mixtures of CO2 in air (Ar : ~1 Mol-% ; O2 :~20.95 Mol-% ; N2 : ~78.05 Mol-%) are used in this work. <br> The stated sample CO2 mixing ratios are 6.013±0.060 Mol-ppm for the 20012-00001 band and 40.10±0.24 Mol-ppm for the 20013-00001 band.</p>
CRDS absorption spectra recorded for the R(06), R(12), R(16), R(18) and R(20) transitions of the 30013-00001 band of 12CO2 in air
<p>The database is made of the set of the CRDS absorption spectra recorded for the R(06), R(12), R(16), R(18) and R(20) transitions of the 30013-00001 band of 12CO2 in air following the procedures and with the experimental setups described in the paper entitled "CRDS measurements of air-broadened lines in the 1.6 µm band of 12CO2: Line shape parameters with their temperature dependence" by D. Mondelain, A. Campargue, H. Fleurbaey, S. Kassi, S. Vasilchenko published in JQSRT 288 (2022) 108267. <br> Each averaged spectrum corresponds to a .csv file including the following columns: <br> absolute wavenumber (in cm-1), cavity loss rate 1/ct (in 10-6 cm-1), temperature (in °C) and total pressure (in Torr).</p> <p>A gas mixture of 398.9±2.0 (2sigma) mol-ppm of CO2 in air (Ar: ~1 mol-% ; O2: ~20.95 mol-%; N2: ~ 78.05 mol-%) is used in this work.</p>
Data for: rtrees: An R package to assemble phylogenetic trees from megatrees
<p>Despite the increasingly available phylogenetic hypotheses for multiple taxonomic groups, most of them do not include all species. In phylogenetic ecology, there is still strong demand to have phylogenies with all species in a study included. The existing software tools to graft species to backbone megatrees, however, are mostly limited to a specific taxonomic group such as plants or fishes. Here, I introduce a new user-friendly R package `rtrees` that can assemble phylogenies from existing or user-provided megatrees. For most common taxonomic groups, users can only provide a vector of species' scientific names to get a phylogeny or a set of posterior phylogenies from megatrees. It is my hope that `rtrees` can provide an easy, flexible, and reliable way to assemble phylogenies from megatrees, facilitating the progress of phylogenetic ecology.</p>
Dataset od: "Towards a Taxonomy of Roxygen Documentation in R Packages"
<p>Replication package for the paper titled "Towards a Taxonomy of Roxygen Documentation in R Packages"</p>
TRANSFORM video - Introducing RRI into the governance of regional R&I
<p>TRANSFORM is a European project (under Horizon 2020) aiming at introducing citizen engagement practices into the governance of regional research and innovation (dealing in particular with Smart Specialisation Strategies – S3) in three European territories: Lombardy (Italy), Catalonia (Spain), Brussels-Capital (Belgium). Under the common framework of Responsible Research and Innovation (RRI), each region runs pilot actions aimed at implementing open and inclusive processes that make local research and innovation priorities more connected to societal needs while generating socially acceptable solutions through the involvement of multiple stakeholders. Three methodologies are being tested: participatory research agenda setting, citizen science and design thinking for social innovation.</p> <p>This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains. This project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM video: Citizen engagement TRANSFORMing regional R&I policy making
<p>Over the past three years, the TRANSFORM project (funded under H2020) has explored and tested citizen engagement practices in the governance of regional research and innovation (R&I), in particular in the context of Smart Specialisation Strategies (S3) in three regional clusters: Lombardy (Italy), Catalonia (Spain), Brussels-Capital (Belgium). Under the common framework of Responsible Research and Innovation (RRI), each territory has implemented different methodologies and pilot actions with the common goal of better aligning regional R&I priorities with societal needs and aspirations.</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM final conference: RRI and citizen engagement to open up regional R&I policymaking
<p>Three regions, three different approaches testing citizen and stakeholder engagement in regional R&I policy making... TRANSFORM partners from three regions - Catalonia, Brussels-Capital and Lombardy on their experiences, activities and impact beyond the project. The video was filmed during the TRANSFORM Final Conference which took place on 1 and 2 December in a hybrid format in Miland and online via Zoom. An opportunity to share project results and outcomes, learn and exchange with the RRI community.</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view. The European Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.