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774 results for “novelty”
Data from: Immunogenetic novelty confers a selective advantage in host-pathogen coevolution
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Data from: Evolutionary novelties and losses in geometric morphometrics: a practical approach through hominin molar morphology
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Data from: Behavioral plasticity and the origins of novelty: the evolution of the rattlesnake rattle
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Data from: Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use
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Data from: Evolution and expression of the phosphodiesterase 6 genes unveils vertebrate novelty to control photosensitivity
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A Peacock Bass (Cichla) functional novelty relaxes a constraint imposed by the classic cichlid pharyngeal jaw innovation
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Data from: Evolutionary novelty in a butterfly wing pattern through enhancer shuffling
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Ribosomal protein S6 phospho-deficient mice display altered novelty-induced locomotion, synaptic plasticity, and specific mRNA translation
GEO Series GSE98029. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Genome-Wide Dosage-Dependent and -Independent Regulation Contributes to Gene Expression and Evolutionary Novelty in Plant Polyploids
GEO Series GSE50715. Arabidopsis suecica; Arabidopsis thaliana x Arabidopsis arenosa. 6 samples. Type: Expression profiling by high throughput sequencing.
Origin of extra-embryonic mesenchyme points to evolutionary novelty in primate early development [ChIP-seq]
GEO Series GSE219291. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Origin of extra-embryonic mesenchyme points to evolutionary novelty in primate early development
GEO Series GSE220844. Homo sapiens. 59 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Cis- and trans- regulatory divergence between progenitor species determines gene-expression novelty in Arabidopsis allopolyploids
GEO Series GSE29687. Arabidopsis suecica; Arabidopsis arenosa x Arabidopsis thaliana; Arabidopsis thaliana; Arabidopsis arenosa. 5 samples. Type: Expression profiling by high throughput sequencing.
Origin of extra-embryonic mesenchyme points to evolutionary novelty in primate early development [scRNA-seq]
GEO Series GSE220840. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Quantitative analysis of chromatin occupancy (ChIP-Seq) from wild-type mice and Kdm5c KO mice in standard conditions (H3K4me3, H3K4me1, and Kdm5C) or upon novelty exposure (H3K4me3), and control mice
GEO Series GSE85873. Mus musculus. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Supplemental Datasets for: WhatsGNU: A Tool For Identifying Proteomic Novelty.
<p>Five precompressed databases are available for WhatsGNU:</p> <p>Ortholog Mode:</p> <ol> <li>WhatsGNU_TB_Ortholog.zip: <em>Mycobacterium tuberculosis</em> Version-07/09/2019 (compressed 26,794,006 proteins in 6563 genomes to 434,725 protein variants).</li> <li>WhatsGNU_Pa_Ortholog.zip: <em>Pseudomonas aeruginosa</em> Version-07/06/2019 (compressed 14,475,742 proteins in 4712 genomes to 1,288,892 protein variants).</li> <li>WhatsGNU_Sau_Ortholog.zip: <em>Staphylococcus aureus</em> Version-06/14/2019 (compressed 27,213,667 proteins in 10350 genomes to 571,848 protein variants).</li> </ol> <p>Big Data basic Mode:</p> <ol> <li><em>Senterica_Enterobase_basic_216642.pickle.gz: Salmonella enterica</em> Enterobase Version-08/29/2019 (compressed 975,262,506 proteins in 216,642 genomes to 5,056,335 protein variants).</li> <li><em>Sau_Staphopia_basic_43914.pickle: Staphylococcus aureus</em> Staphopia Version-06/27/2019 (compressed 115,178,200 proteins in 43,914 genomes to 2,228,761 protein variants).</li> </ol> <p>Figures_datasets.zip: The FASTA files and the WhatsGNU reports as input and output, respectively, which were used to produce panels in Figures 1 and 2 and supplementary figures 1, 2 and 3.</p>
Data from: Conservation and novelty in the microRNA genomic landscape of hyperdiverse cichlid fishes
MicroRNAs (miRNAs) play crucial roles in the post-transcriptional control of messenger RNA (mRNA). These miRNA-mRNA regulatory networks are present in nearly all organisms and contribute to development, phenotypic divergence, and speciation. To examine the miRNA landscape of cichlid fishes, one of the most species-rich families of vertebrates, we profiled the expression of both miRNA and mRNA in a diverse set of cichlid lineages. Among these, we found that conserved miRNAs differ from recently arisen miRNAs (i.e. lineage specific) in average expression levels, number of target sites, sequence variability, and physical clustering patterns in the genome. Furthermore, conserved miRNA target sites tend to be enriched at the 5' end of protein-coding gene 3'UTRs. Consistent with the presumed regulatory role of miRNAs, we detected more negative correlations between the expression of miRNA-mRNA functional pairs than in random pairings. Finally, we provide evidence that novel miRNA targets sites are enriched in genes involved in protein synthesis pathways. Our results show how conserved and evolutionarily novel miRNAs differ in their contribution of the genomic landscape and highlight their particular evolutionary roles in the adaptive diversification of cichlids.
Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks
<p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p> <ul> <li> <p>Pollen images of 16 types: <code>images_16_types.zip</code></p> <ul> <li>Acer Pseudoplatanus</li> <li>Aesculus Carnea</li> <li>Alnus</li> <li>Anthoxanthum</li> <li>Betula Pendula</li> <li>Brassica</li> <li>Carpinus</li> <li>Corylus</li> <li>Dactylis Glomerata</li> <li>Fraxinus</li> <li>Pinus Nigra</li> <li>Platanus</li> <li>Populus Nigra</li> <li>Prunus Avium</li> <li>Sequoiadendron Giganteum</li> <li>Taxus Baccata</li> </ul> </li> <li> <p>Pollen video library <code>pollen_video_library.zip</code></p> <ul> <li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li> <li>In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].</li> </ul> </li> <li> <p>Field data over 3 days are gathered in Graz in spring 2020. <code>pollen_field_data.zip</code></p> </li> <li> <p>Sample code to load the data and visualize the images is in <code>plot_pollen_sample.py</code>. Download and extract the file <code>images_16_types.zip</code> in the same folder as <code>plot_pollen_sample.py</code> to run the example.</p> </li> </ul> <p> </p> <p><strong>Dependecies</strong></p> <ul> <li>opencv</li> <li>numpy</li> <li>matplotlib</li> </ul> <p> </p> <p><strong>Credit</strong></p> <p>[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119.</p> <pre><code>@inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, } </code></pre>
Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?
<p>Datasets, features, and some representative scripts utilized in the paper "Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?" </p>
Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks
<p><strong>Dataset description</strong></p><p>This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria.</p><p>Pollen images of 16 types: ...<strong>images_16_types.zip</strong></p><ul><li>Acer Pseudoplatanus</li><li>Aesculus Carnea</li><li>Alnus</li><li>Anthoxanthum</li><li>Betula Pendula</li><li>Brassica</li><li>Carpinus</li><li>Corylus</li><li>Dactylis Glomerata</li><li>Fraxinus</li><li>Pinus Nigra</li><li>Platanus</li><li>Populus Nigra</li><li>Prunus Avium</li><li>Sequoiadendron Giganteum</li><li>Taxus Baccata</li></ul><p>Pollen video library ...<strong>pollen_video_library.zip</strong></p><ul><li>Each type of pollen is in a separate folder, there may be multiple videos per type.</li><li>In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1].</li><li>Cropped file name structure [Video file name]_[TrackingID]_[Image index of a grain]_[Frame index in video]<ul><li>Example, if a grain has 5 images, the file name would be: Anthoxanthum-grass-20200530-122652_0000000_001_00001.jpg Anthoxanthum-grass-20200530-122652_0000000_002_00002.jpg ... Anthoxanthum-grass-20200530-122652_0000000_005_00005.jpg</li></ul></li></ul><p>Field data over 3 days are gathered in Graz in spring 2020. ...<strong>pollen_field_data.zip</strong></p><p> </p><p><strong>Version 2:</strong></p><p>For experiments of mitigating the distribution shift of pollen identification on field data, there are 5 types selected from field data and manually labeled by the expert. The data are zipped in <strong>"the manual_labeled_field_data_5_types.zip"</strong> </p><p>The "<strong>images_5_types_9010_train.zip</strong>" and "<strong>images_5_types_9010_val.zip</strong>" contain 5 types selected from library data (<strong>images_16_types.zip</strong>), and these correspond to field data. </p><p>The "<strong>images_3_types_for_ablation_study.zip</strong>" contains data on 3 levels of pollen grain hydration. These data are used for the ablation study of model generalization in pollen identification. </p><p>Sample code to load the data and visualize the images is in ...plot_pollen_sample.py. Download and extract the file ...<strong>images_16_types.zip</strong> in the same folder as ...plot_pollen_sample.py to run the example.</p><p><strong>Dependecies:</strong></p><ul><li>opencv</li><li>numpy</li><li>matplotlib</li></ul><p><strong>Credit</strong></p><p>[1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119.</p><p>@inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, }</p>
Figure 3 from: Flores-Argüelles A, López-Ferrari AR, González-Rocha E, Espejo-Serna A (2022) Pitcairnia abscondita (Pitcairnioideae, Bromeliaceae), a hidden novelty from north-western Jalisco, Mexico. PhytoKeys 189: 129-139. https://doi.org/10.3897/phytokeys.189.76464
Figure 3 Known distribution of Pitcairnia abscondita, P. imbricata and P. wendlandii in Mexico.
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.