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35 results for “research objects”
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
W2Share Case Study: Workflow Research Object (WRO)
<p>Case Study - Molecular Dynamics</p> <p>Our case study is based on a molecular dynamics simulation defined in the following article:</p> <p>Silveira, R.L. and Skaf, M. S. Molecular Dynamics Simulations of Family 7 Cellobiohydrolase Mutants Aimed at Reducing Product Inhibition. J. Phys. Chem. B 119, 9295-9303 (2015). DOI: <a href="https://doi.org/10.1021/jp509911m">https://doi.org/10.1021/jp509911m</a></p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
TPDL2022 - Experiment - Research Objects Data
<p>This is the collection of research objects used in TPDL2022. It contains workflow research objects downloaded form the platform workflowhub (https://www.workflowhub.eu)</p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object 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/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</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> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></p>
CWL run of Alignment Workflow (CWLProv 0.6.0 Research Object)
<p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the <a href="http://commonwl.org/">Common Workflow Language</a> execution provenance, see <a href="https://w3id.org/cwl/prov/0.6.0">CWLProv 0.6.0</a> or use the <a href="https://pypi.org/project/cwlprov/">cwlprov Python tool</a> to explore.</p> <p> </p> <p>The CWL <a href="https://w3id.org/cwl/view/git/923ae325fa3a6d3ee3871539c65afa06ac86ac54/aligner/sbg-alignment-cwl/topmed-alignment.cwl">alignment workflow</a> included in this case study is designed by <a href="https://github.com/DataBiosphere">Data Biosphere</a>. It adapts the alignment pipeline originally developed at Abecasis Lab, The University of Michigan. This <a href="https://github.com/FarahZKhan/topmed-workflows/tree/cwlprov_testing">workflow</a> is part of NIH Data Commons initiative and comprises of four stages.</p> <p>First step, <strong><em>Pre-align</em></strong><em>,</em> accepts a <em>Compressed Alignment Map</em> (CRAM) file (a compressed format for BAM files developed <em>by European Bioinformatics Institute</em> (EBI)) and human genome reference sequence as input and using underlying software utilities of <strong>SAMtools</strong> such as <em>view</em>, <em>sort</em> and <em>fixmate</em> returns a list of fastq files which can be used as input for the next step.</p> <p>The next step <strong><em>Align</em></strong> also accepts the human reference genome as input along with the output files from <em>Pre-align</em> and uses <strong>BWA-mem</strong> to generate aligned reads as BAM files. <strong>SAMBLASTER</strong> is used to mark duplicate reads and <strong>SAMtools</strong> view to convert read files from SAM to BAM format.</p> <p>The BAM files generated after <em>lign</em> are sorted with <strong><em>SAMtool sort</em></strong>'.</p> <p>Finally, these sorted alignment files are merged to produce single sorted BAM file using SAMtools merge in <strong><em>Post-align</em></strong> step.</p> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>This analysis was run using a 16-core Linux cloud instance with 64GB RAM and pre-installed docker.</p> <ol> <li> <p>Install <strong>gsutils</strong><br> </p> <pre><code class="language-bash">export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | \ sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \ sudo apt-key add - sudo apt-get update && sudo apt-get install google-cloud-sdk</code></pre> <p> </p> </li> <li> <p>Get the data and make the analysis environment ready:<br> </p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/topmed-workflows.git cd topmed-workflows git checkout cwlprov_testing cd aligner/sbg-alignment-cwl # this is a custom script download google bucket files from json files and create a local json # it needs gsutil to be installed though git clone https://github.com/DailyDreaming/fetch_gs_frm_json.git # Wait... this should download ~18Gb. python2.7 fetch_gs_frm_json/dl_gsfiles_frm_json.py topmed-alignment.sample.json </code></pre> <p> </p> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">time cwltool --no-match-user --provenance alignmnentwf0.6.0 --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-alignment.cwl topmed-alignment.sample.json.new zip -r alignment_0.6.0_linux.zip alignment_0.6.0_linux sha256sum alignment_0.6.0_linux.zip > alignment_0.6.0_linux.zip.sha25</code></pre> <p> </p> </li> </ol>
Data of FAIR Assessment in Research Objects with FAIROs
<p>This data has been used to test the FAIRness of Research Objects of the platform ROHub (https://reliance.rohub.org).</p> <p>It is composed by two sets:</p> <p>* Folder research objects: It contains the collection of Research Objects used.</p> <p>* Folder assessment: It contains the assessment of each Research Object using FAIROs.</p>
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Sample Research Object for Zenodo
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque sodales, dolor imperdiet viverra gravida, turpis est aliquet nisi, vel fermentum augue nisi ut mi. Curabitur placerat egestas scelerisque. Nunc sit amet justo non eros rhoncus egestas a semper dui. Sed at ipsum nec elit sodales rhoncus. Fusce vel ante id mi consectetur tincidunt. Proin lobortis dui vel nunc lacinia pellentesque. Sed pretium justo quis dolor ornare, in egestas turpis eleifend. Sed tellus tellus, fermentum cursus ligula vitae, tincidunt consectetur velit. Vivamus a magna varius, pulvinar purus quis, molestie odio. Nam auctor semper nisi at vehicula. Vestibulum et dui quis metus fringilla tempus. Integer vitae purus nisi. Curabitur a elit at eros mattis lobortis et at lectus.
Dataset for "Evaluation Methods and Replicability of Software Architecture Research Objects"
<p># Content<br> In this package, please find the following content:</p> <p>* Investigated Papers.bib<br> A BibTeX file with all papers investigated in the paper "Evaluation Methods and Replicability of Software Architecture Research Objects"<br> * Raw-Data-Table Content-Data.html and Raw-Data-Table Meta-Data.html<br> Tables with the raw data as extracted during the systematic literature review<br> * Colection of Data Visualizations.pdf<br> Multiple visualizations of the raw data for analysis. A copy of summary.pdf as described below.<br> * Data and Visualization<br> Contains:<br> - The data as CSV files,<br> - scripts for creating visualizations<br> - *.awk -- Awk scripts are used to create the corresponding of the *.csv files in data<br> - *.rb -- Ruby scripts to build the respective figures in figs as *.tex files<br> - make-all.sh -- A script to call all other scripts for creating diagrams and the summary<br> - make-paper-figures.sh -- A script to build "paper-figures.pdf" with all diagrams used in the accompanying paper<br> - A documentation of the contained scripts (Data and Visualization/README.md)<br> - summary.pdf -- A collection of diagrams (as built by make-all.sh)<br> - paper-figures.pdf -- A collection of all diagrams as used in the accompanying paper (as built by make-paper-figures.sh and make-all.sh)<br> * Wiki/<br> A copy of the wiki used during data extraction.<br> Constains:<br> - descriptions of all data items<br> - the process description<br> - the taxonomy used for data extraction</p> <p><br> # Reproduction<br> You can reproduce the visualizations with the following commands in a UNIX command line environment.</p> <p>> cd "Data and Visualization"<br> > ./make-paper-figures.sh<br> > ./make-all.sh</p> <p>The requirements are:<br> * A UNIX command line environment (e.g., bash) with awk installed<br> * Ruby (>2.5)<br> * latex (e.g., tex-live)</p> <p>The command "./make-paper-figures.sh" produces the file “paper-figures.pdf”, which contains all diagrams that are used in the paper.<br> The command "./make-all.sh" produces the file "summary.pdf", which contains diagrams used for data analysis, and the file "paper-figures.pdf". All figures describing the results in the paper are also in "summary.pdf".<br> These commands each take about 2 ("/make-paper-figures.sh") / 8 ("./make-all.sh") minutes to run on current standard laptop (Intel i5-8250U, 16 GB memory).<br> Calling the commands produces many log statements (information and warnings), which show the progress and can be ignored.</p>
Supplementary material 2 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: How to collect, store and curate objects in the Anthropocene? On participatory and digital collections.
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
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>
DataCrate: a method of packaging, distributing, displaying and archiving Research Objects
<p>In characterizing the term <i>Research Object</i> the call for proposals for Research Object 2018 uses the phrase "multi-part research outcomes with their context". The <a href="https://github.com/UTS-eResearch/DataCrate/">DataCrate specification</a><a href="https://data.research.uts.edu.au/examples/v1.0/datacrate-RO-2018/data/paper.html#fn1">1</a> is a research data packaging and dissemination specification designed to capture exactly that; outcomes (also inputs) and context.</p><p>DataCrate specifies how to gather together data in such a way that it can (a) be packaged via zip, tar, a disc image, a multi-part package or (b) be hosted on a web server or file share for inspection by potential users and/or used directly on High Performance Computing systems or otherwise accessed and analyzed.</p><p>DataCrates can contain any kind of data, and the contextual information may include, but is not limited to, data about the people, software and equipment used in the research as well as supporting documents such as publications, funding agreements or README files.</p><p> </p>
MATERIALS OF ARCHAEOLOGICAL RESEARCH OF THE ARCHAEOLOGICAL HERITAGE OBJECT CULTURAL LAYER OF VORONEZH ON THE TERRITORY OF THE LAND PLOT AT 1, KLUBNAYA STREET AND 30D, 20-LETIYA OKTYABRYA STREET
<p>Archaeological research on the land plot at the address: Voronezh, 1, Klubnaya Street, and 30d, 20-letiya Oktyabrya Street, was carried out as part of the implementation of the section of project documentation to ensure the safety of the identified cultural heritage site "Cultural layer of the city of Voronezh". As a result of the work, additional information was obtained on the history of the coastal part of the city in the XIX-th-XX-th centuries.</p>
Figure 1 from: Zou Y (2020) Distance measurement using the spherical wave fact of astronomical objects. Research Ideas and Outcomes 6: e60713. https://doi.org/10.3897/rio.6.e60713
Figure 1 A schematic figure shows the spherical wave observed with 3 telescopes located at positions A, O, and B. The 3 telescopes are settled at the spherical wavefront. The source S is the center of the circle. With the measuring of distance \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} b \end{equation*} \end{varwidth} \end{document} =OO', one is able to get the distance \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} D = {d^2 \over 2b} \end{equation*} \end{varwidth} \end{document} , where \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} d \end{equation*} \end{varwidth} \end{document} is the distance between B and O'.
FAIR Digital Object and DiSSCo Research Infrastructure Design
<p>This presentation is about how <a href="https://dissco.eu">DiSSCo</a> (Distributed System of Scientific Collections) is using the FAIR Digital Object as the cornerstone of a research infrastructure design. DiSSCo -- a new Research Infrastructure currently in the preparation phase -- will digitally unify European natural science assets under common curation and access and will ensure that data is Findable, Accessible, Interoperable and Reusable (FAIR). DiSSCo will integrate a fragmented landscape of natural science collections, transforming it into a unified, robust, quality ensured knowledge base of unprecedented scale for bio- and geodiversity.</p> <p>With a brief historical context of FAIR Digital Object, this presentation introduces the concept of Digital Specimen. Then it highlights the incorporation of outputs from several <a href="https://rd-alliance.org/">RDA</a> (Research Data Alliance) interests and working groups in the design decision.</p>
Data from: An ecosystem services perspective on brush management: research priorities for competing land use objectives
1. The vegetation of semi-arid and arid landscapes is often comprised of mixtures of herbaceous and woody vegetation. Since the early 1900s, shifts from herbaceous to woody plant dominance, termed woody plant encroachment and widely regarded as a state change, have occurred world-wide. This shift presents challenges to the conservation of grassland and savanna ecosystems and to animal production in commercial ranching systems and pastoral societies. 2. Dryland management focused on cattle and sheep grazing has historically attempted to reduce the abundance of encroaching woody vegetation (hereafter, 'brush management') with the intent of reversing declines in forage production, stream flow or groundwater recharge. Here, we assess the known and potential consequences of brush management actions, both positive and negative, on a broader suite of ecosystem services, the scientific challenges to quantifying these services and the trade-offs among them. 3. Our synthesis suggests that despite considerable investments accompanying the application of brush management practices, the recovery of key ecosystem services may be short-lived or absent. However, in the absence of such interventions, those and other ecosystem services may be compromised, and the persistence of grassland and savanna ecosystem types and their endemic plants and animals threatened. 4. Addressing the challenges posed by woody plant encroachment will require integrated management systems using diverse theoretical principles to design the type, timing and spatial arrangement of initial management actions and follow-up treatments. These management activities will need to balance cultural traditions and preferences, socio-economic constraints and potentially competing land-use objectives. 5. Synthesis. Our ability to predict ecosystem responses to management aimed at recovering ecosystem services where grasslands and savannas have been invaded by native or exotic woody plants is limited for many attributes (e.g. primary production, land surface–atmosphere interactions, biodiversity conservation) and inconsistent for others (e.g. forage production, herbaceous diversity, water quality/quantity, soil erosion, carbon sequestration). The ecological community is challenged with generating robust information about the response of ecosystem services and their interactions if we are to position land managers and policymakers to make objective, science-based decisions regarding the many trade-offs and competing objectives for the conservation and dynamic management of grasslands and savannas.
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.