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Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
Daily modelled salinity values in the estuary of the river Ulla (Galicia, Spain) from 1961 to 2023
<p>The dataset includes the estimated daily salinity in the Ulla river estuary (Galicia, Spain) from 7 January 1961 to 31 December 2023. The values included in the data series are the results of an ocean-meteorological model that considers the runoff from the Ulla river basin, tidal amplitude, and wind direction and strength. A figure showing the area of interest is included.</p>
Episodes of critical salinity values in the estuary of the river Ulla (Galicia, Spain) in the hydrological years 1961 to 2022
<p>The dataset includes the number of critical salinity episodes, as a function of salinity thresholds and their duration, that could have caused mortality events of different intensity of bivalves of commercial interest in the Ulla river estuary (Galicia, Spain) for hydrological years 1962 to 2022. </p> <p>A hydrological year is defined as the one starting in October and ending in September of the following year. The series includes four of the possible critical episodes defined by Parada <em>et al., </em>2012 (DOI: 10.1007/s12237-011-9426-2): Salinity (S) below 10 for 1 day (S10-1d); S<30 for 18 consecutive days (S30-18d); S<5 for 1 day or more (S5-1d) and S<30 for 19 consecutive days or more (S30-19d). The first two types of events can cause moderate mortalities of commercial bivalve molluscs and the second severe mortalities (Parada <em>et al., </em>2012; DOI: 10.1007/s12237-011-9426-2). Moderate mortalities are defined as events where mortalities of less than 50% of the cockle (<em>Cerastodema edule</em>, L. 1758) and up to 15% of the clams <em>Ruditapes decussatus</em> (L., 1758) and <em>R. philippinarum</em> (A. Adams & Reeve, 1850) are recorded in the estuary. Severe mortalities are defined as events in which mortalities of 50% or more of the population of <em>C. edule</em> and the clam <em>Venerupis corrugata</em> (Gmelin, 1791), and 15% or more of <em>R. decussatus</em> and <em>R. phillipinarum</em> are recorded (Parada <em>et al., </em>2012; DOI: 10.1007/s12237-011-9426-2). The file is composed of 5 columns: the first column contains the hydrological year and the following columns contain the number of episodes recorded for each of the four categories (S10-1d, S30-18d, S5-1d and S30-19d).</p>
Data for : Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops
<p>Data file for article: Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops (https://doi.org/10.1016/j.jenvman.2022.116699).</p>
Data for publication: "Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression"
<p>This archive contains the two datasets used in the publication: Vermeiren, Charles, Munoz: Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression <br>Preprint: <a href="https://hal.science/hal-04764660" rel="nofollow">https://hal.science/hal-04764660</a><br><br>The first dataset is used to develop and test the model using cross-validation, the 2nd dataset is used as an independent, external dataset to test the model. For details, see the publication.</p> <p>The model code, combined with the data and outputs, are also available on GitHub: https://github.com/Peter-Vermeiren/EIVmodels </p>
Data underlying the assessment of biodiversity and diverse values for nature in University of Helsinki campuses / Aineisto luonnon monimuotoisuutta ja moninaisia arvoja Helsingin yliopiston kampuksilla käsittelevän selvitystyön taustalla
<p>These records contain the spatial and nonspatial datasets underlying the assessment of biodiversity and diverse values for nature in the four campuses of University of Helsinki, namely the Kumpula, Meilahti, Viikki, and City center campuses in Helsinki. The assessment was a part of the University of Helsinki Sustainability and responsibility plan 2022-2024.</p> <p>For further information on the types and contents of the data, see the pdf "README.pdf" and the assessment report.</p> <p> </p>
Data on Companies of the Hungarian Battery Value Chain
<p>This dataset compiles data on companies of the Hungarian electric vehicle battery value chain. The data is accurate as of November 2024. </p> <p>The following data is presented:</p> <ul> <li>Position in the value chain;</li> <li>Company name;</li> <li>Data on the investment and investment subsidies provided by the Hungarian Government;</li> <li>Company financials (2023),</li> <li>Employee numbers, information on agency workers, average wages.</li> </ul>
Dataset and code for submission of "Aesthetic values predict bird trade, but the association varies across product types and trade regions"
<p>Data and code used for analyses in the manuscript titled "Aesthetic values predict bird trade, but the association varies across product types and trade regions". The raw data files are given as .xlsx files for the trade data ("birdtrade_data_clean.xlsx" & "EU_birdtrade_data_clean.xlsx"), as a .csv file ("iratebirds_data_151122.csv") for the aeshtetic value data. and all final merged datasets used in the analysis and figure codes are given as .RData -files. All code is given as .R files.<br><br>The data descriptor is currently given in the submitted manuscript and it's supplements, and will be added here too in more detail upon acceptance of the manuscript.</p>
Conservation outcomes of dietary transitions across different values of nature - model outputs
<p>Archive of output reports produced for the MAgPIE v4.8.2 paper <strong>Conservation outcomes of dietary transitions across different values of nature</strong>.</p> <p><strong>Data</strong></p> <p>The data contain the MAgPIE model and post-processing outputs for all assessed scenarios, including the sensitivity test across the SSP1 and SSP3 scenarios.</p> <ul> <li>The file <em>rev9_healthyLscps_AOH_full_report.Rds </em>contains the outputs of our Area of Habitat (AOH) assessment for all assessed species and across all scenarios. The summary statistics shown in Figure 2 and Figure 3 have been derived from this data set. The data has also been used to create Extended Data Figures<span lang="EN-GB"> 5-</span>7<span lang="EN-GB"> and is shown in the Supplementary Information. </span>However, for Extended Data Figure<span lang="EN-GB"> 7</span> the data was <span lang="EN-GB">used in combination</span> with <span lang="EN-GB">rasterized </span>range polygons obtained from the <span lang="EN-GB">IUCN Red List Database (IUCN 2020).</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_pollSuff_report_all.Rds </em>reports the global and regional cropland area (Mha) that is subject to insufficient and sufficient pollination supply. This is specified in the column ‘poll_class’, where 1 is insufficient and 2 is sufficient pollination supply. The data is shown in Figure 4, Extended Data Figure 8 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_RUSLE_report_all.Rds</em> reports estimated global and regional soil loss in Pg per year across all modelled scenarios. The data is displayed in Figure 4, Extended Data Figure 9 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_allSSP_report.rds </em>contains regional and global output variables from the MAgPIE model used in this study. The data is shown in Figure 1 and 5, as well as in Extended Data Figures 2-4 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_validation.rds </em>contains validation data for the MAgPIE model output variables.</span></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Model code</span></strong></p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><em>MAgPIE model code</em><em>:</em></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.13833444">https://doi.org/10.5281/zenodo.13833444</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></li> </ul> <p><em>MAgPIE model documentation</em><em>:</em></p> <ul> <li><a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.8.2/</a></li> </ul> <p><em>SEALS model code</em><em>:</em></p> <ul> <li><a href="https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0">https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0</a></li> </ul> <p><em>SEALS mode documentation:</em></p> <ul> <li><a href="https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html">https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html</a></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Cited references</span></strong></p> <p><span>IUCN. (2020). <em>The IUCN Red List of Threatened Species. Version 2020-2</em>. https://www.iucnredlist.org.</span><em><span> </span></em><span>Downloaded on 25 November 2020</span></p>
A personalized value-based justification in food swaps to stimulate healthy online food choices
<p>This study examined the effect of a personalized value-based justification in explaining the rationale behind healthy food swaps. Additionally, consumers' willingness to share their personal information with retailers to personalize swap recommendations is explored.</p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
Highly curated hERG dataset of 8879 unique molecular compounds with corresponding potency values
<p>This dataset was built during a research project, in the field of Computer-Aided Drug Discovery (CADD), funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. The aim of the project was to build descriptor-based machine learning models for hERG cardiotoxicity liability predictions. The dataset includes a total of 8879 unique molecular compounds gathered from ChEMBL and PubChem publicly available bioactivity databases, as well as from literature mining. The list is split into 2 sets, 8380 for training and 499 for testing. All molecular compounds are represented in their SMILE format with their corresponding PIC50 potency values.</p> <p>To access the full original work, please visit the following link: <a href="https://arxiv.org/abs/2112.13467">Manuscript</a></p> <p><strong>Note:</strong> Upon usage of this data, kindly cite the original manuscript describing the curation process:<br><br>Arab, Issar, and Khaled Barakat. "ToxTree: descriptor-based machine learning models for both hERG and Nav1. 5 cardiotoxicity liability predictions." <em>arXiv preprint arXiv:2112.13467</em> (2021).</p> <p> </p> <p><strong>Refer to our latest manually curated and a much larger dataset here: <a href="../record/8359714">link</a> </strong></p>
Highly curated Nav1.5 dataset of 1723 unique molecular compounds with corresponding potency values
<p>This dataset was built during a research project, in the field of Computer-Aided Drug Discovery (CADD), funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. The aim of the project was to build descriptor-based machine learning models for Nav1.5 cardiotoxicity liability predictions. The dataset includes a total of 1723 unique molecular compounds gathered from ChEMBL and PubChem publicly available bioactivity databases. The list is split into 2 sets, 1550 for training and 173 for testing. All molecular compounds are represented in their SMILE format with their corresponding PIC50 potency values.</p> <p>To access the full original work, please visit the following link: <a href="https://arxiv.org/abs/2112.13467">Manuscript</a> <br><strong>Refer to a much larger and latest dataset here: <a href="../record/8359714">link</a> </strong></p> <p><br><strong>Note:</strong> Upon usage of this data, kindly cite both the dataset and the original manuscript describing the curation process as written below.<br><br><em>Arab I, Barakat K. ToxTree: Descriptor-based machine learning models for both hERG and Nav1. 5 cardiotoxicity liability predictions. arXiv preprint arXiv:2112.13467. 2021</em></p> <p><em>Arab, Issar, & Barakat, Khaled. (2021). Highly curated Nav1.5 dataset of 1723 unique molecular compounds with corresponding potency values [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5807731</em></p>
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 in Pseudocetherinae (Hemiptera: Reduviidae) revisited: phylogeny and taxonomy of the lobe-headed bugs
Fig. 1. Strict consensus tree resulting from equal weighting analysis. Jackknife values over 51 are reported for tree one of eight.
Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland
<p>Isolated trees are increasingly recognised as playing a vital role in supporting biodiversity in agricultural landscapes, yet their occurrence has declined substantially in recent decades. Most bats in Europe are tree-dependent species that rely on woody elements in order to persist in farmlands. However, isolated trees are rarely considered in conservation programs and landscape planning. Further investigations are therefore urgently required to identify which trees – based on both their intrinsic characteristics and their location in the landscape – are particularly important for bats. We acoustically surveyed 57 isolated trees for bats to determine the relative and interactive effects of size, tree-related microhabitat (TreM) diversity and surrounding landscape context on bat activity. Tall trees with large diameter at breast height and crown area positively influenced the activity of <em>Pipistrellus pipistrellus</em> and small Myotis bats (<em>Myotis</em> spp.) while smaller and thinner trees favoured <em>M. myotis</em> activity. The diversity of TreMs that can be used as roosts had a positive effect on (i) <em>Barbastella barbastellus</em> activity only when trees were relatively close (10% within 100 radius scale). The potential benefits of isolated trees for bats result from ecological mechanisms operating at both tree and landscape scales, underlining the crucial need for implementing a multi-scale approach in conservation programs. Maintaining the largest and most TreM-diversified trees located in the most heterogeneous agricultural landscapes will provide the greatest benefits.</p>
Simple attributes predict the value of plants as hosts to fungal and arthropod communities
Fungal and arthropod consumers constitute the vast majority of global terrestrial biodiversity. Yet, the link from richness and composition of producer (plant) communities to the richness of consumer communities is poorly understood. Fungal and arthropod species richness could be a simple function of producer species richness at a site. Alternatively, it could be a complex function of chemical and structural properties of the producer species making up communities. We used databases on plant-fungus and plant-arthropod trophic links to derive the richness of consumer biota per associated plant species (coined link score). We assessed how well link scores could be predicted by simple attributes of plant species. Next, we used a multi-taxon inventory of 130 sites, representing all major habitat types in a country (Denmark), to investigate whether link scores summed over plant species in communities (coined link sum) could outperform simple plant species richness as predictor of fungal and arthropod richness at the sites. We found plant species' link scores for both fungi and arthropods to be positively related to plant size, regional occupancy, nativeness and ectomycorrhizal status. Link-based indices generally improved the prediction of richness of fungal and arthropod communities. For fungal communities, both observed link sum (from databases) and predicted link sum (from plant attributes) had high predictive power, while plant richness alone had none. For arthropod communities, predictive performance varied between functional groups. For both fungi and arthropods, richness predictions were further improved by considering abiotic habitat conditions. Our results underline the importance of plants as niche space for the megadiverse groups of arthropods and fungi. The plant-attribute approach holds promise for predicting local and regional consumer richness in areas of the world lacking detailed plant-consumer databases.
Datasets and R code for: Brood as booty: The effect of colony size and resource value in social insect contests
<p><strong>From the Manuscript: </strong>Animals engage in contests for access to resources like food, mates, and space. Intergroup contests between groups of organisms have received little attention, and it remains unresolved what information groups might use collectively to make contest decisions. We staged whole-colony contests using ant colonies (<em>Temnothorax rugatulus</em>), which perceive conspecific colonies as both a threat and resource from which to steal brood. We recorded individual behaviors and used demographic characteristics as proxies for resource value (number of brood items) and fighting ability (number of workers). We found that ants altered their fighting effort depending on the relative number of workers of their opponent. While the proximate mechanism for this ability remains uncertain, we found that colonies increased fighting when their opponent had relatively more brood, but not if opposing colonies had relatively many more workers. This suggests that ant colonies can use information about opposing colonies that shapes contest strategies. Further, the behavior of opposing colonies were strongly correlated with each other despite colony size differences ranging from 4-51%, consistent with the hypothesis that colonies can use opponent information. The behavior of a distributed, collective system of many individuals, like a eusocial insect colony, thus fits several predictions of contest models designed for individuals if we consider the gain and loss of worker ants analogous to energetic costs accrued during typical dyadic contests.</p>
Data for the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice"
<p>All data related to the paper "Determining the value of preferred goods based on consumer demand in a home-cage based test for mice" will be made available to the scientific public here. The paper will be published soon in Behavioral Research Methods. </p>
Input data for ERIC values calculation for RAA process
<p>For each area (in total 9 areas of SEE) and for each week (in total 2 weeks of 2021) used in final demonstration of CROSSBOW TC1.1.1 one input file is prepared – in total 18 excel files (*.xlsx).</p> <p>Each file is consisted of 4 sheets:</p> <ul> <li>LOAD – load forecast for 168 timestamps of the given week;</li> <li>DISP_GEN_THERMAL – unit capacity and Forced Outage Rate of production for dispatchable thermal units;</li> <li>DISP_GEN_HYDRO – unit capacity and Forced Outage Rate of production for dispatchable hydro units;</li> <li>NONDISP_GEN – forecasted values for Photovoltaic, Wind, Run of River, Combined Heat and Power and Biomass non-dispatchable units for 168 timestamps of the given week.</li> </ul> <p>Sampling period: Week 9 and Week 10 of 2021</p>
Replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits"
<p>This repository contains replication data for Carleton et al. (Quarterly Journal of Economics, 2022), "Valuing the mortality consequences of climate change accounting for adaptation costs and benefits". All non-confidential data inputs are included, as well as intermediate data outputs, final data outputs, and final tables and figures for all main text and supplementary tables and figures. Some input data are confidential (e.g., mortality records in some countries); therefore, intermediate regression results files are included in the upload to ensure all later stages of the analysis are fully replicable. The full data output files resulting from Monte Carlo simulations of future climate change impacts on mortality far exceed Zenodo file size limits; therefore, key aggregates of the raw output files are included here, which allow for replication of all tables and figures in the paper.</p> <ul> <li><strong>data.zip </strong>contains raw, intermediate, and final datasets</li> <li><strong>outputs.zip </strong>contains output tables and figures </li> </ul> <p>All replication code for the paper is available on a public Github repository, accessible <a href="https://github.com/ClimateImpactLab/carleton_mortality_2022">here</a>.<br><br>The manuscript and supplementary information are available at the QJE, <a href="https://doi.org/10.1093/qje/qjac020">here</a>.</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.