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Supplementary material 1 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Flyer: What is an Anthropocenic Object? Transdisciplinary Perspectives on Natural, Cultural and Hybrid Objects
Supplementary material 4 from: Sturm U, Heyne E, Herrmann E, Arends B, Dieter A-L, Dorfman E, Drauschke F, Heller N, Kahn R, Kaiser K, Koch G, Kramar N, Mansilla Sánchez A, Mauelshagen F, Nadim T, Pell R, Petersen M, Schmidt-Loske K, Scholz H, Sterling C, Trischler H, Wagner S (2022) Anthropocenic Objects. Collecting Practices for the Age of Humans. Research Ideas and Outcomes 8: e89446. https://doi.org/10.3897/rio.8.e89446
Keynote Exhibiting the Anthropocene - and the Technosphere - means breaking away from established knowledge orders
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-06]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-03]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-05]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-04]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier and NGC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
KITTI 3D Object Detection
<p>The dataset downloaded from official KITTI website was used in eGAC3D's evaluation. This dataset includes raw images (data_object_label_2.zip) and corresponding ground-truth (data_object_image_2.zip). </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-08]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-07]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method
<p>DataSet for UAV-based Untrained Small Object Detection using Distance Metric Method</p>
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
<p>A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): 1) few-shot concept learning, and 2) context-dependent reasoning. We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few-shot instances, from partial to no overlaps. Bongard-HOI presents a substantial challenge to today's visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy. With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning.</p>
Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Nat Commun 13, 7622 (2022). https://doi.org/10.1038/s41467-022-35190-9
<p>Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Tanimoto, Watakabe, and Higashijima.</p> <p> </p> <p>The spreadsheet files contain source data for the figures.</p> <p>The file named "register_rotated_images_demo.zip" contains the MATLAB code, example image data, and instruction text data. To use the code, unzip the file and follow the instructions in the "readme.txt" file. The file named "register_rotated_images_demo_output.zip" contains expected output image data.</p> <p> </p>
Figure 10 in How jumping spiders (Araneae: Salticidae) find and use indirect routes to reach their sighted objectives
Figure 10. Hypothetical pursuit on a coplanar route that leads to an unexpected break and then to a
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-09]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC / Barnard / Sharpless catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object)
<p>This workflow adapts the approach and parameter settings of <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">Trans-Omics for precision Medicine (TOPMed)</a>. The <a href="https://w3id.org/cwl/view/git/018d344b12e9e1b888e21e0819096f9b337d371d/topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl">RNA-seq pipeline</a> originated from the Broad Institute. There are in total five steps in the workflow starting from:</p> <ol> <li>Read alignment using <strong>STAR</strong> which produces aligned BAM files including the Genome BAM and Transcriptome BAM.</li> <li>The Genome BAM file is processed using <strong>Picard MarkDuplicates.</strong> producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).</li> <li><strong>SAMtools index</strong> is then employed to generate an index for the BAM file, in preparation for the next step.</li> <li>The indexed BAM file is processed further with <strong>RNA-SeQC</strong> which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.</li> <li>In parallel with transcript quantification, isoform expression levels are quantified by <strong>RSEM</strong>. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.</li> </ol> <p>For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. <em>Chromosome 12</em> was extracted from the <em>Homo Sapien Assembly 38</em> reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.</p> <p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwl">https://pypi.org/project/cwl</a></p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built with:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <ol> <li> <p>Install <strong>cwltool</strong></p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223</code></pre> </li> <li> <p>Install <strong>git lfs</strong><br> The data download with the git repository requires the installation of Git lfs:<br> <a href="https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs">https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs</a></p> </li> <li> <p>Get the data and make the analysis environment ready:</p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/cwl_workflows.git cd cwl_workflows/ git checkout CWLProvTesting ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh</code></pre> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256</code></pre> </li> </ol> <p>The <a href="https://github.com/FarahZKhan/cwl_workflows">https://github.com/FarahZKhan/cwl_workflows</a> repository is a frozen snapshot from <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL</a> commit <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL/tree/027e8af41b906173aafdb791351fb29efc044120">027e8af41b906173aafdb791351fb29efc044120</a></p>
Stone Object, Escoural cave, Portugal
Object made of stone, in an oval shape with a small concavity at the center. All of these pieces from the neolithic necropolis of the Escoural Cave were covered by a calcite layer, and were put next to the burial with a votive meaning. Source: Objaverse 1.0 / Sketchfab
SAYMON OBJECT
Garrafa de areia, direto do Nordeste/Recife. Source: Objaverse 1.0 / Sketchfab
Object Journeys Somali Incense Burner
Object Journeys (http://objectjourneys.britishmuseum.org/) is a Heritage Lottery (https://www.hlf.org.uk/) programme at the British Museum where community partners research and explore museum collections, working collaboratively with museum staff to produce displays, events and web content. It's about learning and exploring together and designed to facilitate a genuine sharing and exchanging of knowledge! Incense burner (Dabqaad/Girgire/Idin) from the British Museum collections http://bit.ly/2aNS980) is community-curated and will feature in a upcoming exhibition in the museum's Welcome Gallery. Source: Objaverse 1.0 / Sketchfab
Figure 5 in Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects
Figure 5. The Wallich Catalogue. Screenshot of Wallich Catalogue hosted by Royal Botanic Garden Edinburgh showing popup for stable URI containing information hosted at Botanic Garden and Botanical Museum Berlin-Dahlem.
Figure 4 in Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects
Figure 4. The CETAF Specimen URI Tester provides for any given Specimen URI an overview of the redirection process as well as a preview of machine-readable and human-readable data associated with the URI.
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.