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Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr).
Text-fig.10. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a–f: seeds extracted from seed-bearing capsule. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 cm. in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig.10. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a–f: seeds extracted from seed-bearing capsule. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 cm.
Text-fig. 9. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. Structure of seed extracted from seed-bearing capsule. a: general morphology; b, c: detailed cellular structure. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 mm (a), 100 µm (b, c). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 9. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. Structure of seed extracted from seed-bearing capsule. a: general morphology; b, c: detailed cellular structure. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 mm (a), 100 µm (b, c).
Text-fig. 4. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a: line drawing explaining female cone morphology after holotype; b: suggested reconstruction showing arrangement and vascularization of seed-bearing discs (left), and section through seed-bearing discs exhibiting seed attachment and marginal limb structure (right); c: seed scar structure (after Textfig. 3c), 1 – subepidermal and epidermal tissues under the cuticle, 2 – coaly tissues of mesophyll. Oval form at seed scar center is possible exit of conducting strand. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 cm (a, b), 100 µm (c). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 4. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a: line drawing explaining female cone morphology after holotype; b: suggested reconstruction showing arrangement and vascularization of seed-bearing discs (left), and section through seed-bearing discs exhibiting seed attachment and marginal limb structure (right); c: seed scar structure (after Textfig. 3c), 1 – subepidermal and epidermal tissues under the cuticle, 2 – coaly tissues of mesophyll. Oval form at seed scar center is possible exit of conducting strand. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 cm (a, b), 100 µm (c).
Figs 8–10 in A new species of Afrothaumalea Stuckenberg, 1960 (Diptera: Thaumaleidae) from the Western Cape (South Africa) and first description of the pupa of this genus
Figs 8–10. Afrothaumalea stuckenbergi sp. n., terminalia: (8) male, ventral view, left gonostylus removed; (9) male, lateral view; (10) female, lateral view. Scale bars= 0.1 mm. Abbreviations: cerc – cercus; goncx – gonocoxite; goncx apod – gonocoxal apodeme; goncx bl – gonocoxal blade; gonst – gonostylus; hyp vlv – hypogynial valve; pm – paramere; T – tergite.
Figs 5–7 in A new species of Afrothaumalea Stuckenberg, 1960 (Diptera: Thaumaleidae) from the Western Cape (South Africa) and first description of the pupa of this genus
Figs 5–7. Afrothaumalea stuckenbergi sp. n.: (5) wing; (6) pupa, dorsal view; (7) pupa, ventral view. Scale bars = 1.0 mm. Abbreviations: CuA – anterior branch of cubital vein; M1,2,4 – medial veins; R1,2,3,4,5 – radial veins; sp – spiracle.
Figs 1–4 in A new species of Afrothaumalea Stuckenberg, 1960 (Diptera: Thaumaleidae) from the Western Cape (South Africa) and first description of the pupa of this genus
Figs 1–4. Afrothaumalea stuckenbergi sp. n.: (1) female; (2) Steve Marshall collecting immatures on rock face at type locality; (3) pupae; (4) larva (photographs: S.A. Marshall).
Fig. 11 in A remarkable new genus of robber flies, Akatiomyia gen. n., from the Western Cape Province of South Africa, and a new key to the genera of Afrotropical Stenopogoninae (Diptera: Asilidae)
Fig. 11. Site at which the holotype of Akatiomyia eremnos gen. et sp. n. was collected (note Clanwilliam Dam in background).
Figs 3–9 in A remarkable new genus of robber flies, Akatiomyia gen. n., from the Western Cape Province of South Africa, and a new key to the genera of Afrotropical Stenopogoninae (Diptera: Asilidae)
Figs 3–9. Akatiomyia eremnos gen. et sp. n.: (3) holotype head, anterior; (4) holotype antenna, lateral; (5) holotype thorax, dorsal; (6–8) paratype male terminalia, lateral (6), dorsal (7), ventral (8); (9, 10) paratype female terminalia, lateral (9), dorsal (10). Scale bars = 1 mm.
Levantamento das teses e dissertações relacionadas à Divulgação Científica - CAPES - 1987 a 2020
<p>Os dados disponíveis foram coletados a partir dos conjuntos de dados “Catálogo de Teses e Dissertações – Brasil” disponibilizados no Portal Dados Abertos CAPES < <a href="https://dadosabertos.capes.gov.br/dataset?groups=catalogo-de-teses-e-dissertacoes-brasil">https://dadosabertos.capes.gov.br/dataset?groups=catalogo-de-teses-e-dissertacoes-brasil</a> >.</p> <p>Em seguida, com os arquivos em formato de planilha “*xlsx”, e com o auxílio do <em>Microsoft Excel</em>, filtramos os registros que mencionam as expressões “divulgação científica” ou “popularização da ciência” no título, resumo ou palavras-chave.</p> <p>Considerando que haviam diferenças no uso de metadados entre os conjuntos de dados disponíveis no Catálogo de Teses e Dissertações – Brasil, selecionamos apenas os dados relacionados a: Ano, Região, Uf, Sigla IES, Nome IES, Nome Programa, Grande Área, Área Conhecimento, Área Avaliação, Autor, Titulo, Nível, Palavras - Chaves, Número Páginas, Idioma, Resumo e Orientadores e URL para visualização do registro.</p> <p>Legenda das colunas:<br> ALL = Dados do campo Título, Resumo e Palavras-Chaves, concatenados.<br> AnoBase = Ano da defesa<br> Regiao = Região do Programa de Pós-Graduação<br> Uf = Unidade Federativa do do Programa de Pós-Graduação<br> SiglaIes = Sigla da Instituição de Ensino Superior do Programa de Pós-Graduação<br> NomeIes = Nome da Instituição de Ensino Superior do Programa de Pós-Graduação<br> NomePrograma = Nome do Programa de Pós-Graduação<br> GrandeAreaDescricao = Grande Área de Descrição - CAPES<br> AreaConhecimento = Área de Conhecimento - CAPES<br> AreaAvaliacao = Área de Avaliação - CAPES<br> Autor = Autor da tese ou dissertação<br> TituloTese = Título da tese ou dissertação<br> Nivel = Indicação se Mestrado / Doutorado / Mestrado Profissional / Profissionalizante<br> PalavrasChave = Palavras-Chaves da tese ou dissertação<br> NumeroPaginas = Número de páginas da tese ou dissertação<br> Idioma = Idioma da tese ou dissertação<br> ResumoTese = Resuumo da tese ou dissertação<br> ORIENTADORES E CO-ORIENTADORES = Nome do orientador<br> URLTextoCompleto = Link para a página do Catálogo de Teses e Dissertações – Brasil</p> <p> </p> <p>Observações:</p> <p>Os dados foram preenchidos pelos coordenadores dos Programas de Pós-Graduação, convém para melhor utilização dos dados, revisar os dados em repositórios e bibliotecas digitais de teses e dissertações das Universidades.</p> <p>Acesse o Plano de Gestão de Dados em:<a href="https://doi.org/10.48321/D1J04F"> https://doi.org/10.48321/D1J04F</a></p> <p>Havendo dúvidas. Entre em contato com o autor. Prof. Dr. <a href="http://pedroandretta.info">Pedro Andretta</a>. E-mail: pedro.andretta @unir.br</p> <p>Acesse também:<br> ANDRETTA, P. I. S.; FREIRE, I. M.. A pesquisa em "Divulgação Científica": um panorama a partir das teses e dissertações brasileiras. In: ENCONTRO NACIONAL DE PESQUISA EM CIÊNCIA DA INFORMAÇÃO, 22., 2022, Porto Alegre. Anais [...]. Porto Alegre: ANCIB, UFRGS, 2022. Disponível em: <a href="https://enancib.ancib.org/index.php/enancib/xxiienancib/paper/view/1064">https://enancib.ancib.org/index.php/enancib/xxiienancib/paper/view/1064</a> Acesso em 26 fev. 2023.<br> </p> <p>Painel interativo: Teses e dissertações brasileiras relacionadas à divulgação científica (1987-2020). Disponível em: <a href="https://lookerstudio.google.com/u/0/reporting/92c2ca82-a47e-45c5-ae8a-6c830cb979ab/page/NtMuC">https://lookerstudio.google.com/u/0/reporting/92c2ca82-a47e-45c5-ae8a-6c830cb979ab/page/NtMuC</a></p>
Fig. 5. Namaquanthus cephalophylloides, Klak 2917 in Two New Species of Aizoaceae (Ruschieae, Ruschoideae) from the Cape, South Africa.
Fig. 5. Namaquanthus cephalophylloides, Klak 2917 (BOL) (A -E): A. Clump- forming plants among quartz-gravel. Maturing fruits are pointing downwards. B. Solitary magenta flowers on long pedicels. C. Side view of closed capsule. The fruit is no longer attached to the pedicel. The lower part of fruit is indented in the area where the pedicel is attached. D. Top view of open capsule. E. Seed distinctly echinate. Namaquanthus vanheerdei, Klak 493 (BOL): F. Seed.
Fig. 4 in Two New Species of Aizoaceae (Ruschieae, Ruschoideae) from the Cape, South Africa.
Fig. 4. Distribution of Namaquanthus cephalophylloides (circle), N. vanheerdei (triangle), Smicrostigma viride (inverted triangle), S. warmwaterbergense (square).
Fig. 3. Smicrostigma warmwaterbergense, Bruyns 14033 in Two New Species of Aizoaceae (Ruschieae, Ruschoideae) from the Cape, South Africa.
Fig. 3. Smicrostigma warmwaterbergense, Bruyns 14033 (BOL) (A, B, D-F): A. Flowering branch, December 2021, South Africa. B. Close-up of flower with stamens visible. D. Side view of closed capsule. E. Top view onto open capsule. F. Seed. Smicrostigma viride: C. Close-up of flower showing the stamens and stigmas concealed by the staminodes, Klak 3035 (BOL).
Fig. 2. Namaquanthus vanheerdei, Klak 493 in Two New Species of Aizoaceae (Ruschieae, Ruschoideae) from the Cape, South Africa.
Fig. 2. Namaquanthus vanheerdei, Klak 493 (BOL). (A-D): A. Large colony of N. vanheerdei on a rocky slope in northern Namaqualand. B. Dense cushion forming habit, with dark green leaves. C. Solitary magenta flowers showing the different stages of flowering: during the first days after opening the inner petals hide the center (left), in older flowers (right) the center is visible. Note: pollen is yellow in some plants. D. Close-up of a young flower showing the inner petals curved over the center.
Ocean Data Hours - Ocean Race - Ocean Village - Genova Pavilllion - Cape Town 23.02.2023
<p>The ocean plays a significant role in the Earth's system, and understanding its importance is crucial for ensuring sustainable management and exploitation. Ocean observation should not be a task for experts and scientists alone.</p> <p>Citizen science is a form of scientific collaboration where members of the public participate in scientific research projects, providing data and observations that can be analyzed by researchers.</p> <p>This workshop presents a series of CS initiatives exploiting low cost technologies for ocean data collection by showing and discussing their maturity level and how scientists can already use these data.</p> <p>By supporting citizen science initiatives, policymakers can democratize marine observation science, creating a new type of self-driven, sustainable, and cost-efficient observatory concept, and at the same time, providing for the making of informed decisions based on the best available information.</p> <p>Ocean Data Hours is a series of workshops and talks organized in the Genova Pavillion at the Ocean Village - Ocean Race 2023. </p>
Figs 6–11 in Four new narrow-range endemic species of Gulella from Eastern Cape, South Africa (Mollusca: Pulmonata: Streptaxidae)
Figs 6–11. Gulella dejae sp. n., holotype. 6, 7, apertural and lateral views of shell, length = 2.8 mm; 8, enlarged view of the aperture, bar = 0.3 mm; 9, umbilical region, bar = 0.3 mm; 10, detail of sculpture (start of last whorl), bar = 50 µm; 11, microsculpture on embryonic shell, bar = 40 µm.
Figs 1–5 in Four new narrow-range endemic species of Gulella from Eastern Cape, South Africa (Mollusca: Pulmonata: Streptaxidae)
Figs 1–5. Gulella hamerae sp. n., holotype. 1, 2, apertural and lateral views of shell, length = 2.7 mm; 3, oblique view into aperture, bar = 0.25 mm; 4, detail of sculpture (start of last whorl), bar = 100 µm; 5, microsculpture on embryonic shell, bar = 100 µm.
Figs 18–23 in Four new narrow-range endemic species of Gulella from Eastern Cape, South Africa (Mollusca: Pulmonata: Streptaxidae)
Figs 18–23. Gulella newmani sp. n., holotype. 18, 19, apertural and lateral views of shell, length = 3.68 mm; 20, enlarged view of the aperture, bar = 0.5 mm; 21, umbilical region, bar = 0.25 mm; 22, detail of sculpture (first quarter of last whorl, bar = 75µm; 23, microsculpture on embryonic shell, partly eroded, bar = 30µm.
Towed ADCP DATA off Cape Canaveral from Oct 2013 to Jun 2016
<p>ADCP data collected underway with Teledyne ADCP instrumentation over 12-hr tidal cycles during experiments shown in 'experiments.jpg' that appear with all ADCP files. ADCP data files are in ASCII format (with extension 000 or 001 or 002 or...) with the format description given in the PDF file that appears with te ADCP data. The filenames contain the month, day and year (e.g., MMDDYY000t.000) in which they were collected. Location of each profile is within its corresponding header, as described in the PDF file.</p>
Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Zoo
<p># Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Gym</p> <p>## Overview<br> * Test datasets and files for testing the [segmentation gym](https://github.com/Doodleverse/segmentation_gym) program for image segmentation<br> * Data set made by Daniel Buscombe, Marda Science LLC. This is version 5.0<br> * Dataset consists of a time-series of Landsat-8 images of Cape Hatteras National Seashore, courtesy of the U.S. Geological Survey.<br> * Imagery spans the period February 2015 to September 2021.<br> * Labels were created by Daniel Buscombe, Marda Science, using the labeling program [Doodler](https://github.com/Doodleverse/dash_doodler).</p> <p>Download this file and unzip to somewhere on your machine (although *not* inside the `segmentation_gym` folder), then see the relevant page on the [segmentation gym wiki](https://github.com/Doodleverse/segmentation_gym/wiki) for further explanation.</p> <p>This dataset and associated models were made by Dr Daniel Buscombe, Marda Science LLC, for the purposes of demonstrating the functionality of Segmentation Gym. The labels were created using [Doodler](https://github.com/Doodleverse/dash_doodler/).</p> <p>Previous versions:</p> <p>1.0. https://zenodo.org/record/5895128#.Y1G5s3bMIuU original release, Oct 2021, conforming to Segmentation Gym functionality on Oct 2021</p> <p>2.0 https://zenodo.org/record/7036025#.Y1G57XbMIuU, Jan 23 2022, conforming to Segmentation Gym functionality on Jan 23 2022</p> <p>This is version 5.0, created 7/20/23, and has been tested with Segmentation Gym using doodleverse-utils 0.0.33 https://pypi.org/project/doodleverse-utils/0.0.33/</p> <p> </p> <p>## file structure</p> <p>```{sh}<br> /Users/Someone/my_segmentation_zoo_datasets<br> │ ├── config<br> │ | └── *.json<br> │ ├── capehatteras_data<br> | | ├── fromDoodler<br> | | | ├──images<br> │ | | └──labels<br> | | ├──npzForModel<br> │ | └──toPredict<br> │ └── modelOut<br> │ └── *.png<br> │ └── weights<br> │ └── *.h5</p> <p>```</p> <p>## config<br> There are 4 config files:<br> 1. `/config/hatteras_l8_resunet.json`<br> 2. `/config/hatteras_l8_vanilla_unet.json`<br> 3. `/config/hatteras_l8_resunet_model2.json`</p> <p>4. `/config/hatteras_l8_segformer.json`<br> </p> <p> </p> <p>The first two are for res-unet and unet models respectively. The third one differs from the first only with specification of kernel size. It is provided as an example of how to conduct model training experiments, modifying one hyperparameter at a time in the effort to create an optimal model. The last one is based on the new Segformer model architecture.</p> <p>They all contain the same essential information and differ as indicated below</p> <p>```<br> {<br> "TARGET_SIZE": [768,768], # the size of the imagery you wish the model to train on. This may not be the original size<br> "MODEL": "resunet", # model name. Otherwise, "unet" or "segformer"<br> "NCLASSES": 4, # number of classes<br> "KERNEL":9, # horizontal size of convolution kernel in pixels<br> "STRIDE":2, # stride in convolution kernel<br> "BATCH_SIZE": 7, # number of images/labels per batch<br> "FILTERS":6, # number of filters<br> "N_DATA_BANDS": 3, # number of image bands<br> "DROPOUT":0.1, # amount of dropout<br> "DROPOUT_CHANGE_PER_LAYER":0.0, # change in dropout per layer<br> "DROPOUT_TYPE":"standard", # type of dropout. Otherwise "spatial"<br> "USE_DROPOUT_ON_UPSAMPLING":false, # if true, dropout is used on upsampling as well as downsampling<br> "DO_TRAIN": false, # if false, the model will not train, but you will select this config file, data directory, and the program will load the model weights and test the model on the validation subset<br> if true, the model will train from scratch (warning! this will overwrite the existing weights file in h5 format)<br> "LOSS":"dice", # model training loss function, otherwise "cat" for categorical cross-entropy<br> "PATIENCE": 10, # number of epochs of no model improvement before training is aborted<br> "MAX_EPOCHS": 100, # maximum number of training epochs<br> "VALIDATION_SPLIT": 0.6, #proportion to use for validation<br> "RAMPUP_EPOCHS": 20, # [LR-scheduler] rampup to maximim<br> "SUSTAIN_EPOCHS": 0.0, # [LR-scheduler] sustain at maximum<br> "EXP_DECAY": 0.9, # [LR-scheduler] decay rate<br> "START_LR": 1e-7, # [LR-scheduler] start lr<br> "MIN_LR": 1e-7, # [LR-scheduler] min lr<br> "MAX_LR": 1e-4, # [LR-scheduler] max lr<br> "FILTER_VALUE": 0, #if >0, the size of a median filter to apply on outputs (not recommended unless you have noisy outputs)<br> "DOPLOT": true, #make plots<br> "ROOT_STRING": "hatteras_l8_aug_768", #data file (npz) prefix string<br> "USEMASK": false, # use the convention 'mask' in label image file names, instead of the preferred 'label'<br> "AUG_ROT": 5, # [augmentation] amount of rotation in degrees<br> "AUG_ZOOM": 0.05, # [augmentation] amount of zoom as a proportion<br> "AUG_WIDTHSHIFT": 0.05, # [augmentation] amount of random width shift as a proportion<br> "AUG_HEIGHTSHIFT": 0.05,# [augmentation] amount of random width shift as a proportion<br> "AUG_HFLIP": true, # [augmentation] if true, randomly apply horizontal flips<br> "AUG_VFLIP": false, # [augmentation] if true, randomly apply vertical flips<br> "AUG_LOOPS": 10, #[augmentation] number of portions to split the data into (recommended > 2 to save memory)<br> "AUG_COPIES": 5 #[augmentation] number iof augmented copies to make<br> "SET_GPU": "0" #which GPU to use. If multiple, list separated by a comma, e.g. '0,1,2'. If CPU is requested, use "-1"<br> "WRITE_MODELMETADATA": false, #if true, the prompts `seg_images_in_folder.py` to write detailed metadata for each sample file<br> "LOSS_WEIGHTS": false, #if true, apply per-class weights to loss function</p> <p> "SET_PCI_BUS_ID": true, #if true, make keras aware of the PCI BUS ID (advanced or nonstandard GPU usage)</p> <p> "TESTTIMEAUG": true, #if true, apply test-time augmentation when model in inference mode</p> <p> "WRITE_MODELMETADATA": true,# if true, write model metadata per image when model in inference mode</p> <p> "OTSU_THRESHOLD": true# if true, and NCLASSES=2 only, use per-image Otsu threshold rather than decision boundary of 0.5 on softmax scores</p> <p>}<br> ```</p> <p>## capehatteras_data<br> Folder containing all the model input data</p> <p>```{sh}<br> │ ├── capehatteras_data: folder containing all the model input data<br> | | ├── fromDoodler: folder containing images and labels exported from Doodler using [this program](https://github.com/dbuscombe-usgs/dash_doodler/blob/main/utils/gen_images_and_labels_4_zoo.py)<br> | | | ├──images: jpg format files, one per label image<br> │ | | └──labels: jpg format files, one per image<br> | | ├──npz4gym npz format files for model training using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/train_model.py) that have been created following the workflow [documented here](https://github.com/dbuscombe-usgs/segmentation_zoo/wiki/Create-a-model-ready-dataset) using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/make_nd_dataset.py)<br> │ | └──toPredict: a folder of images to test model prediction using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/seg_images_in_folder.py)<br> ```</p> <p>## modelOut<br> PNG format files containing example model outputs from the train ('_train_' in filename) and validation ('_val_' in filename) subsets as well as an image showing training loss and accuracy curves with `trainhist` in the filename. There are two sets of these files, those associated with the residual unet trained with dice loss contain `resunet` in their name, and those from the UNet are named with `vanilla_unet`.</p> <p>## weights<br> There are model weights files associated with each config files.</p>
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.