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Baseline glucocorticoids alone do not predict reproductive success across years but in interaction with enzymatic antioxidants
<p>Glucocorticoids are known to adjust organismal functions, such as metabolism, in response to environmental conditions. Therefore, these hormones are thought to play a key role in regulating the metabolically demanding aspects of reproduction, especially in variable environments. However, support for the hypothesis that variation in glucocorticoid concentrations predicts reproductive success is decidedly mixed. Two explanations may account for this discrepancy: a) glucocorticoids might not act independently but could interact with other physiological traits, jointly influencing reproduction, and b) such an association could become apparent primarily in challenging environments when glucocorticoid concentrations increase. To address these two possibilities, we determined natural variation in circulating baseline glucocorticoid concentrations in parental great tits (<em>Parus major</em>) alongside two physiological systems known to be related to an individual's metabolism: oxidative status parameters (i.e., concentrations of pro-oxidants, dietary, and enzymatic antioxidants) and body condition. These systems interact with glucocorticoids and can also influence reproductive success. We measured these variables in two breeding seasons that differed in environmental conditions. When accounting for the interaction of baseline glucocorticoids with other physiological traits, we found a positive relationship between baseline glucocorticoids and the number of fledglings in adult great tits. The strength of this relationship was more pronounced for those individuals who also had high concentrations of the enzymatic antioxidant glutathione peroxidase. When studied independently, glucocorticoids were not related to fitness proxies, even in the year with more challenging environmental conditions. Together, our study lends to support the hypothesis that glucocorticoids do not influence fitness alone, but in association with other physiological systems</p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
[DCASE2024 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br><br>This audio dataset serves serves as supplementary material for the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">DCASE2024 Challenge Task 3: Audio and Audiovisual Sound Event Localization and Detection with Distance Estimation</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="../record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2024 is based on spatial recordings of real scenes, captured in the <a href="../records/7880637">STARS23</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/partha2409/DCASE2024_seld_baseline">baseline model</a>. For more details on the task setup, please refer to the <a href="https://dcase.community/challenge2024/task-audio-and-audiovisual-sound-event-localization-and-detection-with-source-distance-estimation">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="../record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 3</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p> </p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Baseline of the WEAI for East and West African case studies within EWA-BELT Project
<p>The Women’s Empowerment in Agriculture Index (WEAI) is a survey-based tool that measures women’s empowerment and inclusion in agricultural activities.</p> <p>This database contained the baseline "pre-project activities" collected from October 2022 to January 2023 in 40 households per each Country involved (Ethiopia, Kenya, Tanzania, Burkina, Ghana). A total of 219 households were involved collecting 381 interviews, in 65 households the primary respondent was a woman, so we had not to interview a male secondary respondent. </p> <p>There are 3 types of surveys to calculate the WEAI: the original WEAI, the Accelerated WEAI (A – WEAI) and the PRO WEAI. The original WEAI is the first developed by IFPRI, the A-WEAI is a shorter and faster version, while the PRO WEAI is an extended version aimed to cover more areas of empowerment. We chose A-WEAI because it was faster and simpler to administer, considering that it is the first time for most of the partners to work on this survey and that the participants have already answered to many surveys, hence they might answer with more attention to a short and fast survey.</p>
Spatially mapping the baseline and bisphenol-A exposed Daphnia magna lipidome using desorption electrospray ionisa-tion - mass spectrometry
<p>Data from desorption electrospray ionisation - mass spectrometry of <em>Daphnia magna</em> tissue section from control and daphnids exposed to 5 ppm of bisphenol A over their 7<sup>th</sup> adult instar, specifically four sampling points; 8, 24, 48 and 72 h after the 6<sup>th</sup> brood, as well as data acquired from a blank DESI slide.</p> <p>Data is provided in the form of *imzML files with the associated *.ibd of the same name.</p>
Supplementary data to the Baseline Methodological User Needs Analysis
<p>Supplementary data to the Baseline Methodological User Needs Analysis</p>
A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c
<p>Atlantic herring in ICES Divisions 6.a, 7.b-c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographic delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 SNPs, derived from whole genome sequencing, were used to genotype 3,480 baseline spawning samples (2014-2021). A temporally stable baseline comprising 2,316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy >90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003-2004) baseline samples (270 individuals) with a high level of accuracy. Assignment of non-baseline samples (1,514 individuals) from Division 6.a, 7.b-c indicated previously unrecognised levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a 'toolbox' that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy.</p>
Dataset for the paper "A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors"
<p>The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.</p> <p>Additional data for more days (from January-August 2022) for the same locations presented in our paper can be requested for research purposes by contacting the authors.</p> <p>Our GitHub repository - https://github.com/ai4society/PowerIoT-State-Identification</p> <p>If you are using this data, please cite,</p> <blockquote> <pre>@inproceedings{midas-state-id, author = {Bharath C Muppasani and C J Anand and Chinmayi Appajigowda and Biplav Srivastava and Lokesh Johri}, title = {A Dataset and Baseline Approach for Identifying Usage States from Non-Intrusive Power Sensing With MiDAS IoT-based Sensors}, booktitle = {Proc. Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-23)}, year = {2023}, keywords = {Signal Processing (eess.SP), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences}, copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International} }</pre> </blockquote>
FIGURE 3 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 3 | Freshwater fish species from the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid. A = Hemigrammus brevis, endemic species of São Francisco Ecoregion (SFRE); B = Moenkhausia costae and C = Psellogrammus kennedyi, shared species between SFRE and Mid-Northeastern Caatinga Ecoregion (MNCE); D = Aspidoras menezesi, endemic species of Jaguaribe basin (JAG); E = Hypostomus sertanejo, endemic species of MNCE; F = Parotocinclus spilurus, endemic and endangered species of JAG; G = Tatia bockmanni, endemic species of SFRE; H = Cichlasoma orientale, shared species from all basins of SFR-IWT; I = Geophagus brasiliensis, shared species between SFRE and MNCE; J = Colossoma macropomum, non-native species shared between SFRE and Piranhas-Açu basin; K = Parachromis managuensis, non-native species shared between SFRE and Paraíba do Norte basin; L = Xiphophorus helleri, non-native species of JAG.
FIGURE 2 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 2 | Sampling sites in the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid. A = Canals under construction near the rio São Francisco main channel, and B = near Sertânia, Pernambuco State (PE), C = Rio Pajeú, tributary of the São Francisco basin, PE, D = Rio São Francisco near Petrolina, PE, E = Temporary pool in rio Jaguaribe basin in Russas, Ceará State (CE), F = Rio Jaguaribe in Crato, CE, G = Rio Apodi-Mossoró in Pau dos Ferros, Rio Grande do Norte State (RN), H = Rio Apodi-Mossoró in Pau dos Ferros, RN, I = Rio Seridó, tributary of the Piranhas-Açu basin in Caicó, RN, J = Rio Piranhas-Açu, Jardim de Piranhas, RN, K = Rio Paraíba do Norte in Barra de Santana, Paraíba State (PB), L = Rio Paraíba do Norte in São João do Cariri, PB.
FIGURE 1 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 1 | Sampling sites of the freshwater fish species in the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid.
5m land cover for baseline and 3-30-300 scenarios in Paris, Aarhus, and Velika Gorica
<p>This dataset supports scenario analysis using a high-resolution (5m) land cover classification of three European cities: Paris Region (France), Aarhus Municipality (Denmark), and Grad Velika Gorica (Croatia). The scenarios are: current (baseline) land cover, and a created new land cover that meets the 3-30-300 rule for urban greening (Konijnendijk 2023). In the 3-30-300 scenario, every building has two or more tree raster cells within a 30 m buffer, every neighbourhood has 30% or more green and blue space cover within a 300 m buffer, and each building has an accessible green space of at least 1 ha within 300 m. This rule was applied to the urban footprint of each city. In Paris, this applied only to the four central départements and not the entire Paris Region, Île-de-France. </p> <p> </p> <p><strong>Associated Paper</strong></p> <p>The full methodology behind the datasets is described in the following paper. This paper analyses the extent to which each city currently meets, and measures the land cover change required to meet the 3-30-300 rule. Please also cite this paper when using the dataset.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., Jones, L. 2024. Opportunities and constraints of implementing the 3-30-300 rule for urban greening. <em>Urban Forestry & Urban Greening,</em> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128393" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.ufug.2024.128393</span></a></p> <p> </p> <p><strong>Original Data Sources</strong></p> <p>These layers are based on the high resolution land cover layers produced by Knopp (2021, 2022a, 2022b). For Paris, the baseline land cover was modified, using the 10 m land cover by Wu (2022), to reclassify trees to either coniferous or deciduous. </p> <p> </p> <p><strong>Data</strong></p> <p><strong><em>LC_Classification_Lookup_Table.docx</em></strong></p> <p>This word document is a lookup table for the baseline and 3-30-300 scenario land cover classification.</p> <p><strong><em>Baseline_and_3_30_300_HRLC_all_cities.zip </em></strong></p> <p>This file contains the baseline and 3-30-300 scenarios for Velika Gorica, Aarhus, and the four central départements of Paris Region (clipped to a 1km buffer). These files include all interventions from the 3-30-300 rule.</p> <p><strong><em>Original_and_Final_HRLC_3_30_300_Paris_Region.zip</em></strong></p> <p>This file contains the baseline and 3-30-300 scenario for the entire Paris Region only. Whilst there is land cover data for the entire Paris Region, the interventions from the 3-30-300 rule were only applied to the four central départements. This file has been uploaded separately because the file size is greater.</p> <p> </p> <p><strong>References</strong></p> <p>Knopp, J. M. (2021). High resolution land cover 2015 Aarhus, Denmark [Data set]. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Version v1, Vol. 16, pp. 6545–6555). Zenodo. <a href="https://doi.org/10.5281/zenodo.5215792" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5215792</a></p> <p>Knopp, J. (2022a). High resolution land cover 2016 Velika Gorica (Version v1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7107514" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7107514</a></p> <p>Knopp, J. (2022b). High resolution land cover 2017 Ile-de-France [Data set]. REGREEN - Fostering nature‐based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. <a href="https://doi.org/10.5281/zenodo.7110027" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7110027</a></p> <p>Konijnendijk, C.C., 2023. Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. <em>Journal of forestry research</em>, <em>34</em>(3), pp.821-830.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., & Jones, L. (2024). Opportunities and constraints of implementing the 3–30–300 rule for urban greening. Urban Forestry & Urban Greening, 98, 128393. <a href="https://doi.org/10.1016/j.ufug.2024.128393">https://doi.org/10.1016/j.ufug.2024.128393 </a> </p> <p>Wanben Wu. (2022). Europe and China Refined Land cover (ECRLC) (10m) (Version V2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5846090" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5846090</a></p> <p> </p>
Baseline embeddings from the BBBC022 dataset used in "Semisupervised contrastive learning for bioactivity prediction using Cell Painting image data"
<p>3 Baseline embeddings aclculated from the BBBC022 dataset. A self-supervised contrastive learning-based model, DINO and CellProfiler were used to calculate the embeddings.</p>
Fig. 5 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 5: Size-class (S: small, M: medium and L: large) frequency distribution (%) of relevant target fishes in Unprotected (UP) and Future Protected (FP) zones at the three studied locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka), (Number of individuals used to calculate percentages is given in Supp. Mat. 2).
Fig. 3 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 3: Mean density (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
Fig. 1 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 1: Locations where MPAs will be established along the Tunisian coast. Location of future protected sites (FP) and those outside (that will remain unprotected) (UP) (separated with dotted lines indicating borders of future MPAs as they are proposed in management plans).
Fig. 4 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 4: Mean biomass (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
Fig. 2 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 2: Mean species richness (a), mean density (b) and mean biomass (c) (±standard error) per location (KU: Kuriat islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and protection level (UP: Unprotected, FP: Future Protected).
Fig. 1 in Baseline surveys for ants (Hymenoptera: Formicidae) of the western Everglades, Collier County, Florida
Fig. 1. Sampling sites in western Everglades. CS – Collier Seminole State Park, FP – Florida Panther National Wildlife Refuge, FS – Fakahatchee Strand Preserve State Park, SG – Picayune Strand State Forest (Southern Golden Gate Estates), and TT – Ten Thousand Islands National Wildlife Refuge.
A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration
<p>Selected model output data for supporting this paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the Jablonowski-Williamson Baroclinic Wave</p> <p> </p> <p>see https://doi.org/10.5281/zenodo.3544795 for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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.