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#41 in MCF-7 tumor model at different time points
<p>Original PET and CT image files for compound #41 in MCF-7 tumor model at different time points</p>
Observation of quantum many-body effects due to zero point fluctuations in superconducting circuits
<p>Scripts used for the data treatment of sample A</p>
Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999). in An Annotated List Of The Oligocene Fish Fauna From The Osíčko Locality (Menilitic Fm.; Moravia, The Czech Republic)
Text-fig. 1. The geographic position of the localities mentioned in the text. A – position within the Czech Republic. B – Detailed map of the area. Subsilesian Unit: 1 – Kelč, 2 – Špičky, 3 – Horní Těšice; Silesian Unit: 4 – Loučka, 5 – Osíčko, 6 – Rožnov pod Radhoštěm; Ždánice Unit: 7 – Bohuslavice, 8 – Jestřabice, 9 – Kožušice, 10 – Křepice, 11 – Litenčice, 12 – Mouchnice, 13 – Nikolčice, 14 – Nítkovice, 15 – Nosislav, 16 – Židlochovice. The distribution of the units according to Čtyřoký and Stráník (1995). C – map of the Osíčko vicinity with distribution of the Silesian Unit sediments (gray spots) and collecting points (P1 and P2). The distribution of the Silesian Unit sediments according to Stráník (1999).
Fig. 2. Mid-point phylogenetic trees using SAG3 in Toxoplasma gondii infection in Amami spiny rat on Amami-Oshima Island, Japan
Fig. 2. Mid-point phylogenetic trees using SAG3 (A), GRA6 (B), and ROP18 (C) sequences of Toxoplasma gondii detected from the Amami spiny rat specimen and the references available in the public databases. *, type I strain; **, type II strain; ***, type III strain; ****, atypical strain. Bars represents the number of nucleotide substitutions per site.
Text-fig. 9A. Eospondylus cf. primigenius (STÜRTZ) "Červený lom" quarry near Praha-Klukovice, Loděnice Limestone, Lower Devonian, Pragian, NM L 36910, x 40. Left lateral plate. Inner surface view from below. The spine ridge faces distally (posteriorly) and has blunt pointed denticles. Anterior and parallel to denticled part of spine ridge is internal rounded ridge. Ventrolaterally ossicle has slit-like sockets for groove spines. in Isolated Ossicles Of The Family Eospondylidae Spencer Wright, 1966, In The Lower Devonian Of Bohemia (Czech Republic) And Correction Of The Systematic Position Of Eospondylid Brittlestars (Echinodermata: Ophiuroidea: Oegophiurida)
Text-fig. 9A. Eospondylus cf. primigenius (STÜRTZ) "Červený lom" quarry near Praha-Klukovice, Loděnice Limestone, Lower Devonian, Pragian, NM L 36910, x 40. Left lateral plate. Inner surface view from below. The spine ridge faces distally (posteriorly) and has blunt pointed denticles. Anterior and parallel to denticled part of spine ridge is internal rounded ridge. Ventrolaterally ossicle has slit-like sockets for groove spines.
NIBIO_MLS: a forest point cloud panoptic segmentation dataset from mobile laser scanning (Geoslam Horizon)
<h1>General description</h1> <p>This dataset consists of a ML-ready labelled mobile laser scanning (MLS) point cloud dataset including 16 manually labelled forest plots (approx. 250 m2) for forest panoptic segmentation and thus including both semantic and instance labels. The data was collected using a Geoslam Horizon RT and processed using Geoslam Hub.</p> <h1>Labels</h1> <p>The data were then labelled into the following semantic classes (<em>label</em>):</p> <ul> <li>1= ground</li> <li>2= vegetation: these include both branches, leaves, and low vegetation</li> <li>3= lying deadwood</li> <li>4= stems</li> </ul> <p>In addition for each tree, a unique tree identifier (<em>treeID</em>) was also assigned to each point.</p> <h1>Data split</h1> <p>Each plot was split into train (50%), validation (25%), and test (25%) sets by dividing the circular plot into four slices, out of which the first two were used for training, the third for validation, and the fourth for test. </p> <p>Thus the users might play around with merging the train and validation dataset as they prefer. These two sets can be used during model training, hyperparameter tuning, and model selection. However, the test set should be kept as an independent set to be used for benchmarking against the values reported in the two studies indicated below. </p> <h1>Citation</h1> <p>To cite this datasets and for a more detailed description use:</p> <p>Wielgosz, M., Puliti, S., Xiang, B., Schindler, K. and Astrup, R., 2024. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data. <em>Remote Sensing of Environment; </em></p> <h2>Other studies using these data</h2> <p>Wielgosz, M., Puliti, S., Wilkes, P. and Astrup, R., 2023. Point2Tree (P2T)—Framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest. <em>Remote Sensing</em>, <em>15</em>(15), p.3737; available <a href="https://www.mdpi.com/2072-4292/15/15/3737" target="_blank" rel="noopener">here</a></p> <h1>Funding</h1> <p>This work is part of the Center for Research-based Innovation SmartForest: Bringing Industry 4.0 to<br>the Norwegian forest sector (NFR SFI project no. 309671, smartforest.no).</p> <h1>⚖️ Licensing</h1> <p>📄 Please refer to the specific licenses below for details on how the data can be used.</p> <h4>🔑 Key Licensing Principles:</h4> <ul> <li>✅ You may access, use, and share the dataset and models freely.</li> <li>🔄 Any derivative works (e.g., trained models, code for training, or prediction tools) must also be made publicly available under the same licensing terms.</li> <li>🌍 These licenses promote <strong>collaboration</strong> and <strong>transparency</strong>, ensuring that research using this dataset benefits the broader scientific and open-source community 🙌</li> </ul>
Fémurs intermédiaires avec une longue pointe in Les abeilles du genre Xylocopa Latreille (Hymenoptera : Apoidea : Apidae) au Burundi, de bons pollinisateurs des légumineuses
Fémurs intermédiaires avec une longue pointe
Data and Code for "Snapshotting Quantum Dynamics at Multiple Time Points"
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EMC data of non-point source pollution in key cities in China from 2005 to 2022 (mainly including four pollution indicators: TSS, TN, TP, COD)
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Data for "Relative contribution rates of influencing factors of urban non-point source pollutants (TSS, TN, TP, COD)"
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Three-point bending test
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Single time-point analysis of product and substrate inhibition
<p>Spectrophotometric Monitoring of Hydrolysis Kinetics</p>
Stockhausen_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Karlheinz Stockhausen, Gruppen für drei Orchester (1955–57), excerpt
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Stockhausen_dataset_PoD </strong>contains eight files:</p> <ul> <li>Stockhausen_01_ReadMe.pdf</li> <li>Stockhausen_02_data.xlsx (processed data for this excerpt)</li> <li>Stockhausen_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Stockhausen_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Stockhausen_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Stockhausen_04_audio.mp3 to display correctly]</li> <li>Stockhausen_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Stockhausen_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Stockhausen_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Huber_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Nicolaus A. Huber, 6 Bagatellen für Kammerensemble (1981), Nr. 6: Romanze
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Huber_dataset_PoD </strong>contains eight files:</p> <ul> <li>Huber_01_ReadMe.pdf</li> <li>Huber_02_data.xlsx (processed data for this work)</li> <li>Huber_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Huber_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Huber_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Huber_04_audio.mp3 to display correctly]</li> <li>Huber_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Huber_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Huber_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
MuSHRoom iPhone dataset colmap pose & point cloud
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Supplementary Data for "Li+ Diffusion in Crystalline Lithium Silicides: Influence of Intrinsic Point Defects"
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Performance Point
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Avian point count data from riparian corridors of protected areas in Marin County California
<p>The data set is a simplified version of avian point count data collected in Marin County, CA, USA between 1997 and 2019. Point count surveys (5 minutes) were conducted twice during the breeding season (May and June) along transects in riparian corridors within protected areas (Point Reyes National Seashore, Golden Gate National Recreation Area, Mount Tamalpais State Park, and Bolinas Lagoon Open Space Preserve). Data were collected by Point Blue Conservation Science biologists and were integrated into the National Park Service Inventory and Monitoring Program. All individuals detected at any distance were recorded during the surveys, but this simplified data set only includes the observations analyzed for this publication, specifically detections (aurally and visually) within 50 meters of each point count station for each of the fourteen species selected for analysis.</p>
FIGURE 1. Distribution map, showing collecting points for 2 in The first records of P. d. dexamenus from Costa Rica and new records of Prepona pylene gnorima (Nymphalidae: Charaxinae)
FIGURE 1. Distribution map, showing collecting points for 2 species of Prepona in Costa Rica.
Geographic locations of presence points and the size of 14 skull measurements
<p>The phylogeny and species boundaries of <i>Gazella subgutturosa</i> and <i>G. marica</i> have been long debated. The achievements of past conservation efforts have been compromised by a lack of knowledge about the phylogeny and taxonomic status of different populations. We integrated the recent genetic findings by previous studies with morphometric analyses and ecological niche modelling (ENM) to assess discreteness among populations of these gazelle species in Asia. Taxonomic diversity of gazelles was investigated by using principal components analysis (PCA) based on 14 cranial measures of male skulls. Ecological niche divergence was examined based on a PCA on climatic factors and a species distribution modelling (SDM) with environmental variables. Morphometric results indicated substantial differentiation in size between skulls of the western Zagros Mountains including west and south-western Iran and Arabian Peninsula from all other samples east of the Zagros Mountains from Iran to China. ENM also revealed that gazelles in the east and west of Zagros Mountains occupy distinct niches and that there are apparent areas of disconnection across the goitered gazelle suitable range. A complete divergent niche occupation was also observed between gazelles of Mongolia and other Asiatic gazelles, except for China. Taking the inferences from ENM and morphology together with previous genetics results, we conclude that gazelles in the west and southwest of Iran may represent <i>G. marica</i>. Also, our combined analyses revealed divergence among gazelles of Iran, Central Asia, and Mongolia/China. These results may pave the way for future studies and have conservation implications particularly for reintroduction/supplementation programs.</p>
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.