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87 results for “data documentation”
Wake data documentation for a wind turbine rotor with winglets
<p>This is the documentation of data, measured in a experimental campaign, in which the effects of winglets<br> on a model wind turbine rotor were investigated</p>
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</p>
What Is Metadata and How Do I Document My Data?
<p>This video has been realized in occasion of the CESSDA Training Days. The CESSDA Training Days were a two-day training event showcasing diverse training resources on both CESSDA tools and services. They took place on November 27 and 28, 2019, and were hosted by the GESIS – Leibniz-Institute for the Social Sciences in Cologne, Germany.</p> <p>In the video, Alexander Jedinger discusses metadata and data documentation. </p> <p>The video is also available for <a href="https://youtu.be/cjGz-I0GgKk">viewing on Youtube</a>.</p>
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability - Data, Code and Documentation
<p>Release version of the data, code and documentation used in and generated by our data analysis and literature search to ensure reproducibility, repeatability, and transparency, to be published alongside our paper "An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability".</p>
IPBES Data Management Tutorials - Session 3.3: Data management report details: Data and metadata documentation and curation
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session <em>Data management report details: Data and metadata documentation and curation </em>reviews what should be included in metadata and why it should be tracked in a data management report.</p>
Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector
<p>This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context.</p> <p>Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2021).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2021).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings.</p> <p>The modifications of the version 1.1.0 – compared to the previous version 1.0.0 – include the following updates:</p> <ul> <li>Minor formal corrections in the report</li> <li>An adjustment in the calculation basis of the district heating profiles</li> <li>Correction of an error in the calculation of the absolute number of individual type buildings in the database for the German building stock.</li> </ul> <p>Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A and FKZ: 03ET4055B.</p> <ul> </ul>
ESTRAM: data documentation
<p>This documentation offers an overview of data employed in energy system optimization models built with the ESTRAM framework by a research group of the Leibniz University Hannover and the Institute for Solar Energy Research Hamelin (ISFH). It is important to note that specific models may utilize distinct data as indicated in their respective studies.</p>
Data from: Documenting the progressions of secondary eyewall formations
<p>Intense tropical cyclones can form secondary eyewalls (SEs) that contract towards the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016–19, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following a SE formation.</p> <p>The results show two primary SE pathways exist, No Replacement, known as Path 1, and Replacement, known as the Classic Path. Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold air advection.</p> <p>This study highlights a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions regarding the physical processes driving SE formations. Most importantly, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.</p>
The Palaeoflora Database - Documentation and Data
<p><span>The Palaeoflora database has been designed to provide essential information about fossil taxa, their related nearest living relatives (NLRs), and their climatic requirements. The database was established in 1990 to foster quantitative palaeoclimate reconstructions and has been continually growing, as well as being updated and corrected on a regular basis. As of March 2024, Palaeoflora includes ca. 7000 macrobotanical and 3300 microbotanical taxa, and ca. 1900 modern taxonomical units (species, genera, families) for which climate data are available. </span></p> <p><span>The NLR concept proposed in the Palaeoflora refers to published palaeobotanical literature resources and kind advice of numerous colleagues. Being maintained in the context of the international scientific network NECLIME (www.neclime.de), Palaeoflora profits immensely from co-operations and the activities within NECLIME, especially of the working groups on palynology and plant macrofossils. Climatic ranges for extant plant taxa in the Palaeoflora Database are mainly based on records of meteorological stations located within the plant distribution area of the respective taxon and refer to a global context. Climate data of the Palaeoflora database are most suitable to reconstruct past regional climates based on palaeobotanical records. </span></p>
HT3D Data for Quarto document
<p>HT3D data in TIFF format containing the RI values of various samples.</p>
Documents used in the PLANET4B D1.1 analysis of biodiversity discourse by news outlets - 2010 and 2022 Data
<p>Data used to analyse biodiversity discourse in news outlet as part of Deliverable D1.1. of the Planet4B Project.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (8/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (14/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (7/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (9/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (12/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (11/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (13/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (16/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Documenting And Assessing Open Innovation: Co-creation Of An Open Data Model For Surgical Training (Additional materials, tables 2 & 3)
<p>Challenge competitions have recently resurged for promoting open innovation in areas where markets fail to provide incentives, such as the Sustainable Development Goals (SDGs). Challenges call for the general public to contribute novel solutions to a well-defined problem, in exchange for prizes, credentials and the promise of further development of selected solutions. The aim of this paper is to report on the development of an open and collaborative data model to document and evaluate innovations in the context of a challenge competition, while also being compatible with the work of other open source communities to validate and improve them. By reusing open documentation standards and embedding them into a semantic collaborative platform, the model aimed to be flexible enough to respond to the evaluation needs of the project organisers and self-assessment for participants. We expect our experience provides insights on the potential of semantic, collaborative platforms and standards for increasing the impact of innovations towards the SDGs.</p> <p>The developer team defined the goal and scope of the ontology in collaboration with the GSTC organisers. This was done by agreeing on scenarios where the ontology will be used and establishing competency questions that the ontology has to be able to respond to. Table 2 describes the four motivating scenarios, including actors involved, requirements, sequence of actions and main problems identified. Table 3 details the competency questions for each scenario.</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.