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Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"
<p>The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day.</p> <p>The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence".</p> <p>The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2.</p> <p>The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3</p> <p><strong>end</strong><strong> </strong> 15</p>
Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update
<p>These maps are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields.</p> <p><strong>Objectives</strong>:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid<br> - Downscaling to 2x2 km grid</p> <p><strong>Model settings and predictors: </strong> <br> - Model from ENETWILD report August 2019<br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p><strong>Conclusions guiding future methodological steps</strong><br> - To update wild boar hunting yield data for some specific regions;<br> - To increase hunting yield data resolution;<br> - To explore model independent parametrization for each bioregion.</p> <p><strong>Files:</strong></p> <p>August_2019_HY_nut00_10x10 >> Model outputs based on hunting yield GLM analyses<br> August_2019_occurrences_bioclim >> Model outputs based on Bioclim analyses<br> August_2019_occurrences_glm >> Model outputs based on Generalised linear model<br> August_2019_occurrences_ksvm >> Model outputs based on Support vector Machine analyses<br> August_2019_occurrences_maxent >> Model outputs based on Maxent analyses<br> August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. <br> There are frequent updates in order to improve the results. For methodological approach and details check the paper: </p> <p>ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E.<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a></p> <p>.</p> <p>Permission for reuse occurrence outputs records is granted under the terms of a CC-BY-NC license.<br> Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.</p>
Model outputs for update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect, May 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.<br> <br> Objectives:<br> <br> - Incorporate additional data to provide new maps of wild boar suitability with a resolution of 2x2 km >>> file 3_June_2020_suitability_2x2.tif<br> - New model based on hunting yield with different approaches to handle the biorregion effect >>> files 1_June_2020_HY_nut01_10x10_twostep.tif & 2_June_2020_HY_nut01_10x10_pca.tif<br> <br> Model settings and predictors: <br> - Hunting yield modeling including biorregion effect as bioclimatic PCA scores<br> - Hunting yield addressing biorregion effect in a two-step procedure with independent parametrization for each bioregion<br> <br> Conclusions guiding future methodological steps:<br> - For wild boar suitability maps at 2x2 km, additional data on survey effort is critical in the southern bioregion<br> - Hunting yield model predictions at 10x10 km grids overestimated the hunting bag numbers obtained from the external datasets<br> - HY model with independent parametrization for each bioregion performed better that previous and new strategies<br> <br> For further details and methodological approach see the paper:<br> ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, Soriguer, J. Vicente (2020) update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect. EFSA supporting publication 2020 TO BE COMPLETED<br> <br> Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.</p>
Fig. 8 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 8. Thyropygus sutchariti sp. nov., from Kaeng Krachan, holotype (CUMZ-D00090), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Left telopodite, posterior-mesal view. D. Left telopodite, anterior-lateral view.
Fig. 11. A in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 11. A. Thyropygus navychula sp. nov., specimen from Surin Islands, living ♂ (paratype, CUMZ-D00089-1). B. Thyropygus forceps sp. nov., specimen from Namwang Srithammasokrach, living ♂ (paratype, CUMZ-D00073-1).
Fig. 5 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 5. Thyropygus mesocristatus sp. nov., from Srikasorn, holotype (CUMZ-D00094), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Lateral view. D. Left telopodite, posterior-mesal view. E. Left telopodite, anterior-lateral view.
Fig. 2 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 2. Thyropygus cimi sp. nov., from Namwang Srithammasokrach, holotype (CUMZ-D00086), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Lateral view. D. Left telopodite, posterior-mesal view. E. Left telopodite, anterior-lateral view.
Fig. 1 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 1. Phylogenetic relationships of Thyropygus species based on maximum likelihood analysis (ML) and Bayesian Inference (BI) of 1147 bp of concatenated gene fragments of COI (660 bp) and 16S rRNA (487 bp). Numbers at nodes indicate branch support based on bootstrapping (ML) / posterior probability (BI). Scale bar = 0.06 substitutions/site. # indicates branches which received <50% ML bootstrap support, - indicates non-supported branches by posterior probability. Clade memberships and designations are shown as vertical bars; 1A1 = T. allevatus, 1A2 = cuisinieri subgroup, 1A3 = opinatus subgroup and 1A4 = induratus subgroup. The coloured area marks the T. opinatus subgroup. Abbreviations after species names refer to locality names as shown in Table 1.
Fig. 7 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 7. Thyropygus planispina sp. nov., from Tham Sua temple, holotype (CUMZ-D00088), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Lateral view. D. Left telopodite, posterior-mesal view. E. Left telopodite, anterior-lateral view.
Fig. 6 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 6. Thyropygus navychula sp. nov., from Surin Islands, holotype (CUMZ-D00095), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Left telopodite, posterior-mesal view. D. Left telopodite, anterior-lateral view.
Fig. 4 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 4. Thyropygus forceps sp. nov., gonopods. – A, C–E. Holotype (CUMZ-D00092), ♂, from Namwang Srithammasokrach. A. Anterior view, left telopodite removed. C. Posterior view, left telopodite removed. D. Left telopodite, posterior-mesal view. E. Left telopodite, anterior-lateral view. – B. Specimen from Tham Pha Deang temple (CUMZ-D00093), ♂. Anterior view, left telopodite removed.
Fig. 10 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 10. Thyropygus ursus sp. nov., from Lanta Islands, holotype (NMHW-Inv.7855), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Left telopodite, posterior-mesal view. D. Left telopodite, anterior-lateral view.
Fig. 9 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 9. Thyropygus undulatus sp. nov., from Khao Phanom Bencha, holotype (CUMZ-D00087), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Lateral view. D. Left telopodite, posterior-mesal view. E. Left telopodite, anterior-lateral view.
Fig. 3 in A revision of the Thyropygus allevatus group. Part V: Nine new species of the extended opinatus subgroup, based on morphological and DNA sequence data (Diplopoda: Spirostreptida: Harpagophoridae)
Fig. 3. Thyropygus culter sp. nov., from Rorn waterfall, holotype (CUMZ-D00091), ♂, gonopods. A. Anterior view, left telopodite removed. B. Posterior view, left telopodite removed. C. Left telopodite, posterior-mesal view. D. Left telopodite, anterior-lateral view.
Transcription initiation peaks based on FANTOM5 CAGE data on rn6, canFam3, and galGal5
<p><strong>Overview</strong></p> <p>Decomposition-based peak identification (DPI, https://github.com/hkawaji/dpi1) is applied to the FANTOM5 data of rat (rn3), dog (canFam3), and chicken (galGal5):</p> <ul> <li>https://fantom.gsc.riken.jp/5/datafiles/phase2.6/basic/</li> </ul> <p>The same parameters to the ones used in the previous paper (Forrest ARR, Kawaji H, Rehli M, et al. Nature 507: 462–470, 2014) was used.</p> <p><strong>Data files</strong></p> <p>Four data files per assembly are prepared as below.</p> <ol> <li>tag cluster in the original definition (*.tc.bed.gz)</li> <li>full set of DPI peaks (*.tc.decompose_smoothing_merged.bed.gz)</li> <li>permissive set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts3.bed.gz)</li> <li>robust set of DPI peaks (*.tc.decompose_smoothing_merged.ctssMaxCounts11_ctssMaxTpm1.bed.gz)</li> </ol> <p><strong>Acknowledgement</strong></p> <p>This data set is supported by Research Grant from MEXT to RIKEN Preventive Medicine and Diagnosis Innovation Program, RIKEN Center for Integrative Medical Sciences, and JSPS KAKENHI Grant-in-Aid for Scientific Research No. 16H02902.</p>
Generalized model-based solutions to false positive error in species detection/non-detection data: DataS5.
<p>Data/code associated with empirical case study (Gray fox relative abundance estimation/prediction) in article "Generalized model-based solutions to false positive error in species detection/non-detection data" [doi pending].</p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
A comprehensive data-based assessment of forest ecosystem carbon stocks in the U.S. 1907-2012
<p>This excel file contains data on forest ecosystem Carbon stocks in the United States from 1907-2012 used to create figures 2 (a) (b), 3, 4, 5 (a) (b) presented in the article "A comprehensive data-based assessment of forest ecosystem carbon stocks in the U.S. 1907-2012".</p>
Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model
<p>This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.</p>
Fig. 7 in Eight new species of the genus Anaplecta Burmeister, 1838 (Blattodea: Blattoidea: Anaplectidae) from China based on molecular and morphological data
Fig. 7. Anaplecta cruciata Deng & Che sp. nov., holotype, ♂ (SWU). A. Habitus, dorsal view. B. Habitus, ventral view. C. Head, ventral view. D. Pronotum, dorsal view. E. Tegmina. F. Tegmina vein. G. Maxillary palp. H. Front femur, ventral view. I. Wings. J. Supra-anal plate, dorsal view. K. Subgenital plate, ventral view. L. Hook, ventral view. M. Left phallomere, ventral view. N. Right phallomere, ventral view. Scale bars: A–C = 1 mm; D–F, I–K = 0.5 mm; G–H = 0.2 mm; L–N = 0.25 mm.
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.