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Figure 3 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 3. Proportion of number of plant species within soil water regime ecogroups in studied territories.
Figure 4 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 4. Proportion of number of plant species within total salt regime ecogroups in studied territories.
Figure 1 in Ecological Features And Anthropogenic Transformation Of Wetlands As Part Of Urban Floras Of Ukraine
Figure 1. The location of the studied cities in Ukraine. (The map from Nations online: https://www. nationsonline.org/oneworld/map/ukrainepoliticalmap.htm).
Supplementary Data for 'Transformers Significantly Improve Splice Site Prediction'
<p><strong>Description:</strong></p> <p>This repository contains supplementary data accompanying the manuscript <strong>"Transformers Significantly Improve Splice Site Prediction"</strong>. The data includes annotations used for training our splice site prediction models and the predictions made by our model and SpliceAI 10k. These datasets are provided to facilitate replication of our results and to support further research in RNA splicing and machine learning applications in genomics.</p> <p><strong>Contents:</strong></p> <ol> <li> <p><strong>Annotations Used for Model Training:</strong></p> <ul> <li> <p><strong>clinvar_splice_variants.tsv</strong></p> <ul> <li><em>Description:</em> Contains detailed information about ClinVar splice variants used in our study.</li> <li><em>Contents Include:</em> Variant identifiers, genomic coordinates, associated clinical significance, and relevant annotations.</li> </ul> </li> <li> <p><strong>splice_site_annotation_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> <li> <p><strong>splice_site_annotation_icelandic_whole_blood_plus_gtex.tsv</strong></p> <ul> <li><em>Description:</em> Splice site annotations derived from a combination of Icelandic whole blood samples and samples from all tissues in GTEx V8.</li> <li><em>Contents Include:</em> Coordinates of splice sites and transcript information.</li> </ul> </li> </ul> </li> <li> <p><strong>Model Predictions:</strong></p> <p><strong>a. SpliceAI 10k Model Predictions:</strong></p> <ul> <li> <p><strong>spliceai_10k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> Variant Call Format (VCF) file containing delta scores that indicate the predicted impact on splicing for ClinVar variants.</li> </ul> </li> <li> <p><strong>spliceai_10k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for variants unlikely to be splicing quantitative trait loci (sQTLs) in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs.</li> </ul> </li> <li> <p><strong>spliceai_10k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> SpliceAI 10k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs.</li> </ul> </li> </ul> <p><strong>b. Transformer 45k Model Predictions:</strong></p> <ul> <li> <p><strong>transformer_45k_clinvar_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for ClinVar splice variants.</li> <li><em>Contents Include:</em> VCF file with delta scores from our Transformer model, indicating the predicted impact on splicing.</li> </ul> </li> <li> <p><strong>transformer_45k_no_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for variants unlikely to be sQTLs in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants not associated with sQTLs, as predicted by our model.</li> </ul> </li> <li> <p><strong>transformer_45k_sqtl_delta.vcf</strong></p> <ul> <li><em>Description:</em> Transformer 45k delta scores for sQTLs detected in Icelandic whole blood.</li> <li><em>Contents Include:</em> VCF file with delta scores for variants identified as sQTLs, based on our Transformer model predictions.</li> </ul> </li> </ul> </li> </ol> <p><strong>Additional Information:</strong></p> <ul> <li> <p><strong>Delta Scores and Their Interpretation:</strong></p> <ul> <li>Each variant is assessed for its potential impact on splicing through four delta scores: <ul> <li><strong>Acceptor Site Creation (<code>top_a_creation_delta</code>):</strong> Predicts the likelihood of creating a new acceptor site.</li> <li><strong>Acceptor Site Disruption (<code>top_a_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing acceptor site.</li> <li><strong>Donor Site Creation (<code>top_d_creation_delta</code>):</strong> Predicts the likelihood of creating a new donor site.</li> <li><strong>Donor Site Disruption (<code>top_d_disruption_delta</code>):</strong> Predicts the likelihood of disrupting an existing donor site.</li> </ul> </li> <li><strong>Final Delta Score Calculation:</strong> <ul> <li>The overall impact of a variant is determined by taking the maximum of these four delta scores: <div> <div><code>final_delta_score = <span>max</span>(top_a_creation_delta, top_a_disruption_delta, top_d_creation_delta, top_d_disruption_delta) </code></div> </div> </li> <li>A higher final delta score indicates a greater predicted impact on splicing.</li> </ul> </li> <li><strong>Positions:</strong> <ul> <li>The positions (<code>*_pos</code>) indicate the genomic coordinates where the predicted splicing events occur, providing insight into the specific locations affected by the variant.</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li><strong>Interpreting Delta Scores:</strong> <ul> <li>Delta scores range from 0 to 1.</li> <li>Scores closer to 1 suggest a higher probability of the variant affecting splicing.</li> </ul> </li> </ul> </li> <li> <p><strong>Purpose:</strong></p> <ul> <li>These datasets support the findings reported in our manuscript by providing the raw data used for model training and evaluation.</li> <li>Researchers can use these datasets to replicate our experiments, compare model performances, or conduct further studies on splice site prediction.</li> </ul> </li> </ul> <p> </p> <p><strong>Citation:</strong></p> <p>Please cite this dataset as:</p> <blockquote> <p>B.A. Jónsson, G.H. Halldórsson, S. Árdal, S. Rögnvaldsson, E. Einarsson, P. Sulem, D.F. Guðbjartsson, P. Melsted, K. Stefánsson, M.Ö. Úlfarsson (2023). Supplementary Data for "Transformers Significantly Improve Splice Site Prediction". Zenodo. <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.14109868</a></p> </blockquote> <p> </p> <p><strong>Contact Information:</strong></p> <p>For questions or further information, please contact:</p> <ul> <li><strong>Benedikt A. Jónsson</strong></li> <li><strong>Affiliation:</strong> deCODE Genetics/Amgen, Inc., Reykjavik, Iceland</li> <li><strong>Email: </strong>benediktj@decode.is</li> </ul>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Transformation Rate Maps of Dissolved Organic Carbon in the Contiguous U.S.
<p>We develop two new maps of the dissolved organic carbon (DOC) transformation rate (\(P_r\)) over the contiguous United States. Those maps are derived by combining the USGS riverine DOC observations, soil organic carbon (SOC) data from two sources—HWSD v1.2 and SoilGrids 2.0, and the watershed characteristics from two existing datasets medium-resolution NHDplus and ScienceBase, and state-of-the-art machine learning techniques. </p>
Pauli Decomposition via the Fast Walsh-Hadamard Transform v3
<p>Scripts and data used to produce results for the paper "Pauli Decomposition via the Fast Walsh-Hadamard Transform" (https://arxiv.org/abs/2408.06206; Georges et al 2025 <em>New J. Phys.</em> <a href="https://doi.org/10.1088/1367-2630/adb44d" target="_blank" rel="noopener">https://doi.org/10.1088/1367-2630/adb44d</a>) . </p> <p>In the previous version, there was a bug in generating kinetic energy matrix where one of the FFTs should have been transformed and not only conjugated. This did not practically change the timings but the input matrix was incorrect. Now it's fixed in "kinetic_energy_matrix.zip". All other files are the same as in the second version.</p>
Dataset for publication "Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications"
<p>This is dataset related to paper published in 2020 IEEE I2MTC Conference proceedings:</p> <p>Crotti G., Giordano D., Letizia P. S., Delle Femine A., Gallo D., Landi C., Luiso M., Barbieri L., Mazza P., Pallidini D., (2020, October 29). Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications. https://doi.org/10.5281/zenodo.4154617</p> <p>Excel file contains the data for Figure 10 and following parameter evaluation.</p>
Data Set for Publication "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications"
<p>This is dataset for paper published in CPEM2020 Conference Proceedings:</p> <p>Crotti G., Delle Femine A., Gallo D., Giordano D., Landi C., Letizia P.,S., Luiso M., "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications".</p> <p>https://doi.org/10.5281/zenodo.4153819</p> <p>Excel file provides data for Figure 2 and following evaluation</p> <p> </p>
UK Power Station Transformer Dissolved Gas Analysis Data (2010-2015)
<p>This dataset includes dissolved gas analysis records from coolant oil in 13 UK power station transformers for various timespans between 2010-2015. They form the basis of the paper "Assessing the impact of weak and moderate geomagnetic storms on UK power station transformers" submitted to the AGU "Space Weather" Journal by Z.M. Lewis, J.A. Wild and M. Allcock in December 2021.<br> <br> Please cite Lewis et al. if using these data. The authors thank D. Barker, EDF Energy Nuclear Generation, for providing these data.</p> <p> </p> <p>The data are presented in comma separated variable format files, as described in the readme.txt file.</p>
Harmonized remodeled energy system transformation strategies for Germany - additional data
<p>This dataset compares 10 different scenarios for the transformation of the German energy system by 2050. These scenarios were used in the <a href="https://www.innosys-projekt.de">InNOSys project</a> as a starting point for a multidimensional impact assessment and evaluation of different transformation strategies (see also <a href="https://www.mdpi.com/2071-1050/13/9/5217">https://www.mdpi.com/2071-1050/13/9/5217</a>).<br> As a source of inspiration for these scenarios, 10 different transformation strategies were used, as published for Germany in 2012-2018. However, for the present document, the original scenarios were re-modeled in a harmonized way.</p> <p>An additional documentation of the scenarios is also available on ZENODO. </p>
Data for Horowitz et al. (2022). The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy
<p>Data repository for Horowitz, et al. 2022, "The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy"</p> <p>All data shown in the paper is included here. Data includes results from:</p> <p>1. U.S. LTS GCAM scenarios (labeled "GCAM")<br> 2. U.S. LTS NEMS scenarios (labeled "NEMS")<br> 3. Princeton's Net-Zero America scenarios (labeled "NZA")<sup>a</sup><br> 4. Williams et al. (2020) scenarios (labeled "Williams")<sup>b</sup></p> <p><br> Results are included in the following files:</p> <p>1. waterfall.csv - Figure 1. Emissions decomposition by scenario. GCAM only.<br> 2. clean_fuels.csv - Figure 2. Clean fuel (bioliquids, biogas, and blue/green hydrogen) consumption. All models.<br> 3. coal.csv - Figure 2. Primary energy consumption of coal without CCS. All models.<br> 4. ZEV_stock.csv - Figure 2. Zero-emission vehicle percentage of light-duty vehicle stock. All models.<br> 5. ghg_by_type.csv - Figure 3. Greenhouse gas emissions by gas/source. GCAM only.</p> <p> </p> <p>Reference:</p> <p>a) Larson, E., Greig, C., Jenkins, J.,Mayfield, E., Pascale, A., Zhang, C., Drossman, J., Williams, R., Pacala, S., Socolow, R., et al. (2021). Net-zero America: Potential pathways, infrastructure, and impacts. (Princeton University).</p> <p>b) Williams, J. H., Jones, R. A., Haley, B., Kwok, G., Hargreaves, J., Farbes, J., & Torn, M. S. (2021). Carbon-Neutral Pathways for the United States. AGU Advances, 2, e2020AV000284. <a href="https://doi.org/https://doi.org/10.1029/2020AV000284">https://doi.org/https://doi.org/10.1029/2020AV000284</a>.</p>
Data set for publication: Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves
<p>This is dataset for paper published:</p> <p>Crotti, G.; D’Avanzo, G.; Giordano, D.; Letizia, P.S.; Luiso, M. Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 1715. https://doi.org/10.3390/en14061715</p> <p> </p> <p>Excel file provides data for Table 2, Table 4 and Figure 10 a.</p>
Dataset for publication "Measuring Harmonics With Inductive Voltage Transformers in Presence of Subharmonics"
<p>This is a dataset for paper published:</p> <p>G. Crotti, G. D’Avanzo, P. S. Letizia and M. Luiso, "Measuring Harmonics With Inductive Voltage Transformers in Presence of Subharmonics," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 70, pp. 1-13, 2021, Art no. 9005013, doi: 10.1109/TIM.2021.3111995.</p> <p> </p> <p>Excel file provides data for Figures from 9 to 16.</p>
Data set for publication: A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers
<p>This is dataset for paper published:</p> <p>G. Crotti, D. Giordano, G. D'Avanzo, P.S. Letizia, M. Luiso, "A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers", Measurement, Volume 182, 2021, 109674, ISSN 0263-2241,<br> https://doi.org/10.1016/j.measurement.2021.109674.</p> <p> </p> <p>Excel file provides data for Figure 3, 4, 6 and 7.</p>
Basic tools for deriving photolysis endpoints for soil photo-transformation products in groundwater
<p>This spreadsheet has been designed to determine input parameters for consideration of the photolysis pathway in FOCUS-PELMO 5.5.3 and subsequent versions. It should be used alongside the EFSA scientific guidance on “Soil phototransformation products in groundwater – consideration, parameterisation and simulation in the exposure assessment of plant protection products”.</p> <p>The tools in the spreadsheet allow to identify the relevant grid points in the solar database 'AGRI4CAST', to retrieve irradiance values for the relevant grid points and for the period of the study from the 'AGRI4CAST' database via a spatial interpolation of the irradiance values (this can also be used for other solar databases besides 'AGRI4CAST'). Furthermore, the tools enable to convert the irradiance from kWh/m², kJ/m² or J/cm² into W/m², to perform a time-step normalisation for field studies, and to compare the normalised k<sub>fast</sub> values from different field studies.</p>
Fig. 1 in The Diversity Of Cuculiform And Piciform Species In Partly Transformed Riparian Zambezi Forest
Fig. 1. Location of the study area. Explanations: a — grassland (flooded area); b — Zambezi riparian forest; c — Colophospermum mopane forest; d — Kalahari Woodland; e — arable land; f — urbanized built-up areas; g — rural areas; h — Zambezi River; i — border of the study area.
Fig. 3 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 3. Adipose dystrophy and hypopigmentation of Fig. 4. Well-seen adipose dystrophy combined with common toad's liver. Vacuol and pigment remain- protein dystrophy and a foci of necrosis in the middle ings are seen in melano-macrophagal aggregations. of the picture. Destroyed cells and pigment remain- Hematoxyline-eosine staining, ×200. ings are seen in melano-macrophagal aggregations. Hematoxyline-eosine staining, ×200.
Fig. 1 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 1. Protein dystrophy in common toad. Protein Fig. 2. Foci of necrosis in common toad liver tissue. granules are visible inside hepatocytes. Hematoxy- Destructed cells are seen. Hematoxyline-eosine stainline-eosine staining, ×200. ing, ×200.
Fig. 7 in Histological Changes In Common Toad, Bufo Bufo (Anura, Bufonidae), Liver Tissue Under Conditions Of Anthropogenically Transformed Ecosystems
Fig. 7. Histological changes in liver tissue of common toads from breeding population in NNP "Holosiivskyi", %.
ScienceDex guides
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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.