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Fig. 7. Restored habitat 3 in P R O J E C T S O N E M Y S O R B I C U L A R I S (R E P T I L I A: Testudines: Emydidae) In Latvia For Thirty Years (1984 - 2014): Biological Aspects, Results And Effect On Population And Ecosystems
Fig. 7. Restored habitat 3: ponds system and eggs-laying places.
Fig. 1 in Microhabitat Preference And Relationships B E T W E E N M E Ta Z O A N Pa R A S I T E S O N T H E G I L L A P Pa R At U S O F T H E E U R O P E A N E E L (A N G U I L L A Anguilla) From Freshwaters Of Latvia
Fig. 1. The gill apparatus and sectors of gill arch.
Fig. 5 in The First Records Of The Common Pheasant, P H A S I A N U S C O L C H I C U S (Av E S: G A L L I F O R M E S: Phasianidae), And Its Group In South-Eastern Latvia
Fig. 5. The distribution of observation places of Phasianus colchicus in Latvia since 2005.
Fig. 4 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig. 4. Effect of grain size (2.2-2.0 mm) on oat grain yield.
Fig. 4 in The First Records Of The Common Pheasant, P H A S I A N U S C O L C H I C U S (Av E S: G A L L I F O R M E S: Phasianidae), And Its Group In South-Eastern Latvia
Fig. 4. Phasianus colchicus female, Ainavas, Kalkunes parish, Daugavpils district; 2015.04.06.
Fig.3 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig.3. Effect of plant height on oat grain yield.
Fig. 2 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig. 2. Grain yield of tested cultivars (n=19) from 2012 to 2014, t ha-1.
Fig. 3 in The First Records Of The Common Pheasant, P H A S I A N U S C O L C H I C U S (Av E S: G A L L I F O R M E S: Phasianidae), And Its Group In South-Eastern Latvia
Fig. 3. Phasianus colchicus female, Ainavas, Kalkunes parish, Daugavpils district; 2015.03.27.
Dataset associated to Janas, D. M. et al., Inorg. Chim. Acta, 557, 121705 (2023)
<p>The raw data that support the findings of the study "Metalloporphyrins on oxygen-passivated iron: Conformation and order beyond the first layer" (https://doi.org/10.1016/j.ica.2023.121705). The data analysis was performed using the software "ImageJ" and "Igor Pro 8". Experimental files (.pxp) of the processed data are available from the corresponding authors upon reasonable request.</p>
Fig. 1.- M in Sobre alġns mirmecófilos galegos, cunha especie nova para España, Microdon myrmicae Schönrogge et al., 2002 (Diptera: Syrphidae), e Coccinella magnifica Redtenbacher, 1843 (Coleoptera: Coccinellidae) nova para Galicia.
Fig. 1.- M. myrmicae: a.- Pupario; b.- respiratorio anterior; c.- Detalle da escultura.
Fig. 3.- M in Sobre alġns mirmecófilos galegos, cunha especie nova para España, Microdon myrmicae Schönrogge et al., 2002 (Diptera: Syrphidae), e Coccinella magnifica Redtenbacher, 1843 (Coleoptera: Coccinellidae) nova para Galicia.
Fig. 3.- M. analis: a.- Órgano respiratorio 4 posterior; b.- Pupario.
Fig. 2.- M in Sobre alġns mirmecófilos galegos, cunha especie nova para España, Microdon myrmicae Schönrogge et al., 2002 (Diptera: Syrphidae), e Coccinella magnifica Redtenbacher, 1843 (Coleoptera: Coccinellidae) nova para Galicia.
Fig. 2.- M. myrmicae: imago no pupario.
Mapa 2.- M. subterraneus Chevrolat, 1835 in Acerca del conocimiento del género Myrmechixenus Chevrolat, 1835 en la Península Ibérica y Baleares (Coleoptera, Tenebrionidae, Diaperinae)
Mapa 2.- M. subterraneus Chevrolat, 1835.
[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)
<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>
Fig. 3 in Saussurea albifolia M. J. Nam & H. T. Im (Compositae), a new species from the Baekdudaegan Area, Korea
Fig. 3. Distribution of Saussurea albifolia M. J. Nam et H. T. Im. in the Baekdudaegan Area.
EARice10: A 10 m Resolution Annual Rice Distribution Map of East Asia for 2023
<p>This dataset provides a 10 m <span>Resolution </span><span>Annual Rice </span><span>Distribution </span><span>Map of East Asia </span><span>for </span><span>2023 </span>(<strong>EARice10</strong>). The accuracy of EARice10 <span>is </span>evaluated using more than 90,000 validation samples and achieves an overall accuracy of 90.48%, with both user’<span>s</span> and producer’<span>s</span> <span>accuracies</span> exceeding 90%. The reliability of the dataset is verified by an R<sup>2</sup> <span>values ranging</span> between 0.94 and 0.98 with respect to official statistics<span>,</span> and between 0.79 and 0.98 with respect to previous rice mapping datasets.</p> <p><strong>File format:</strong> tif</p> <p><strong>Spatial extent:</strong> East Asia</p> <p><strong>Pixel size:</strong> 10 m</p> <p><strong>Projection information:</strong> EPSG: 4326 (WGS84)</p> <p><strong>Crop type: </strong>Paddy rice</p> <p><strong>Year:</strong> 2023</p>
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
Data supplementing the article "Boosting DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal
<p>These data supplement the article "Enhancing DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal</p> <p>The directory contains the following files:</p> <p><strong>278 (139 x 2 replicates) samples fastq files.rar </strong>- contains the 278 fastq files provided by the sequencing platform with demultiplexed and contig DNA reads corresponding to the 139 samples with 2 sequencing replicates (A and B).</p> <p><strong>Counts_diatoms.xlsx </strong>- contains the morphological inventories with species list (Omnidia code) and valve abundances for the 139 samples.</p> <p><strong>Sites_list.xlsx </strong>- contains information regarding the 139 samples, including: River name, GPS coordinates, code used for molecular analysis and corresponding to sequencing fastq names.</p>
G³M 1.0 steady-state model output
<p>Steady-state model output of the global gradient-based groundwater model G³M 1.0. Related to the publication in GMD "Beyond the bucket - Developing a global gradient-based groundwater model (G³M v1.0) for a global hydrological model from scratch"</p> <p>Contains the following global outputs:</p> <p>* hydraulic head</p> <p>* surface water body to/from groundwater flows</p> <p>* conductance values for surface water bodies</p>
Raw data corresponding to Huestegge, S. M., Raettig, T., & Huestegge, L. (2019). "Are face-incongruent voices harder to process? Effects of face-voice gender incongruency on basic cognitive information processing." Journal: Experimental Psychology.
<p>Raw data file prior to subject-based aggregation. Variables and values are decribed within the file. For further reference and specifications please also refer to the original publication in the journal Experimental Psychology.</p>
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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.