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2,682 results for “implementation”
WSC 2007 - 2012 Yahara Watershed surface water quality policies and practices created and implemented by public agencies
This dataset was created June 2012 - August 2013 to contribute to research under the Water Sustainability and Climate project. Interventions collected are those land-based policies and practices written and implemented by public agencies. Policies were implemented in Wisconsin's Yahara Watershed the period 2007-2012. They aim to improve surface water quality through nutrient (phosphorus and nitrogen) and sediment reduction. Interventions included in the mapping must have spatially-explicit, publicly available data through personal communication or website.
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
IPBES Data Management Tutorials - Session 2.4: Implementation of the data management policy
<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 Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session on the<em> Implementation of the data management policy </em>provides a brief overview of the contents of the following chapters and how it all works together to improve the transparency and credibility of IPBES. </p>
GCMMA-MMA-Python: Python implementation of the Method of Moving Asymptotes
This record contains the Python implementation of the Method of Moving Asymptotes (MMA), originally developed and written in MATLAB by Krister Svanberg. The MMA algorithm is used for solving non-linear programming problems. Users of this code are encouraged to inform Krister Svanberg of their application and intentions via email, as provided on his website. When publishing work that uses this code, please cite Krister Svanberg's original academic work.
Data from PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model
<p>In our study, we describe the implementation of an adaptive proglacial lake boundary in the Parallel Ice Sheet Model (PISM). The model was tested by applying it to the glacial retreat of the North American ice sheets after the LGM.</p> <p>This dataset contains selected timeslices and variables of the model output for our three main experiments (LAKE, CTRL and DEF). More details about the experiments can be found in our study:</p> <blockquote> <p>Hinck, S., Gowan, E. J., Zhang, X., and Lohmann, G.: PISM-LakeCC: Implementing an adaptive proglacial lake boundary in an ice sheet model, The Cryosphere, 16, 941–965, https://doi.org/10.5194/tc-16-941-2022, 2022.</p> </blockquote>
Field data obtained from the implementation of the Field Protocols (1, 2, and 3) from WP3 in B-GOOD Project
<p>Dataset of the field data obtained from the implementation of the field protocols 1, 2, and 3, developed under B-GOOD (Giving Beekeeping Guidance by cOmputatiOnal-assisted Decision making, Grant agreement No. 81762) project in WP3.</p> <p>The primary goal of field data collection was to gather data that could be used for the validation of floral resources maps and models developed across the various tasks within WP3, and also to fill specific information gaps during the development of the phenological model. The fieldwork was conducted in three countries (Portugal, Belgium, and the United Kingdom) using three field protocols developed under B-GOOD and described in Milestone MS15.</p> <p>These protocols are part of Tasks 3.1 and 3.3 and serve various purposes. The primary goal of Field Protocol 1: "<em>Assessment of plant species composition on key landscape elements/habitats important for bees</em>" was to determine the species composition in selected key plant communities (<em>i.e.</em> landscape elements or habitats) important for bees. Field Protocol 1 was used to determine the plant species composition of specific ALMaSS landscape elements and to confirm/validate the plant composition of some BIOEUNIS habitat types, developed in Task 3.1. The main goal of Field Protocol 2: "<em>Assessment of Phenology of Floral Resources for Bees</em>" was to determine the flower phenology of targeted plant species to construct flowering phenological curves for targeted plant species. Field Protocol 2 was used to validate the floral resource models developed in Task 3.2 (see Section 2.4). The primary goal of Field Protocol 3: "<em>Floral resources evaluation (detailed method)</em>" was to conduct a detailed evaluation of the floral resources in each landscape window with B-GOOD mini-apiaries to map resource availability. Field protocol 3 was divided into two parts: Part 1 - “<em>Assessment and quantification of floral resources</em>”, aiming to determine the species composition, species cover, and flower abundance; and Part 2 - “<em>Flowering species characterization</em>”, aiming to quantify the number of flowers per individual plant, and the nectar and pollen production of target plant species. Field Protocol 3 was also used to make a detailed assessment of plant species composition and plant resources at the landscape level, as well as to fill the gaps in knowledge about pollen and nectar production of some target plant species. The field data collected by the implementations of the field protocols could be categorized into three main groups: Plant Species Composition (Field Protocol 1 and Field Protocol 3: Part 1), Phenology of Floral Resources (Field Protocol 2), and Flowering Species Characterization (Field Protocol 3: Part 2).</p> <p>Field protocols have been implemented in Portugal, the United Kingdom, and Belgium. Field protocols 2 and 3 were fully implemented in the three countries. However, field protocol 1 was not implemented as a stand-alone field protocol in Portugal and the United Kingdom due to logistical and time constraints primarily caused by the COVID pandemic. However, this does not hamper our ability to obtain landscape-specific plant composition data because the information gathered in this protocol can be derived entirely from the implementation of the first part of Field Protocol 3. As a result, for Portugal and the United Kingdom, Field Protocol 1 data was derived from the first part of Field Protocol 3. The full dataset gathered by the implementation of these three protocols is available here.</p> <p>The files “field-data-protocol-1-be.xlsx”, “field-data-protocol-1-pt.xlsx” and “field-data-protocol-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 1: "Assessment of plant species composition" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-2-be.xlsx”, “field-data-protocol-2-pt.xlsx” and “field-data-protocol-2-uk.xlsx” have the field data obtained from the implementation of Field Protocol 2: "Assessment of Phenology of Floral Resources" in Belgium, Portugal, and the UK, respectively.</p> <p>The files “field-data-protocol-3-part-1-be.xlsx”, “field-data-protocol-3-part-1-pt.xlsx “and “field-data-protocol-3-part-1-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 1 “Assessment and quantification of floral resources” in Belgium, Portugal, and the UK, respectively.</p> <p>The file “field-data-protocol-3-part-2-be-pt-uk.xlsx” have the field data obtained from the implementation of Field Protocol 3 - Part 2 - “Flowering species characterization” in Belgium, Portugal, and the UK.</p> <p>For further details, see Alves da Silva et al. 2020. Field protocols for the assessment of Floral Resources. Milestone MS15 EU Horizon 2020 B-GOOD Project. GA No. 817622 and Ziółkowska et al 2022. Floral Resource Models Validation Deliverable D3.4 EU Horizon 2020 B-GOOD Project, GA No. 817622.</p>
Dataset for the publication "Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper"
<p>This dataset contains geometric and graphics data associated with the referenced paper, enabling the reproduction of the conducted research. </p>
Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"
<p><em>Research data supporting the publication:</em></p> <p><em>Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021.</em></p> <p>This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages.</p> <p>Main files:</p> <ul> <li>"ELN_Review_Higgins_2021_Survey.csv" = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> <p>Derived files, generated from those above:</p> <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> <p>This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).</p>
Implementation of Frailty Care Bundle (FCB) for older people in acute care settings
<p>A study aimed to implement a Frailty Care Bundle (FCB) for orthopaedic trauma patients to increase mobilisation, nutrition and cognitive well-being in order to reduce hospital associated decline risk.</p>
Data for "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments"
<p>The processed data supporting the Global Environmental Change publication "Deforestation in the Brazilian Amazon could be halved by scaling up the implementation of zero-deforestation cattle commitments".</p> <p>These data can be analyzed and visualized with the code at: <a href="https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare">https://github.com/sam-a-levy/Levyetal2023_cattlemarketshare</a></p> <p>For a description of each file & the variables contained, please look to the README file.</p>
The tpm metabarcoding DNA sequence database for taxonomic allocations using RDP classifier implemented in DADA2.
<p><strong>The </strong><em>tpm</em><strong> metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools</strong></p> <p>A.C.M. Pozzi<sup>1</sup>, R. Bouchali<sup>1</sup>, L. Marjolet<sup>1</sup>, B. Cournoyer<sup>1</sup></p> <p><sup>1 </sup><em>University of Lyon, UMR Ecologie Microbienne Lyon (LEM), CNRS 5557, INRAE 1418, Université Claude Bernard Lyon 1, VetAgro Sup, Research Team “Bacterial Opportunistic Pathogens and Environment” (BPOE), 69280 Marcy L’Etoile, France.</em></p> <p><strong>Corresponding authors: </strong></p> <ul> <li>A.C.M. Pozzi, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 39 47. Fax. (+33) 472 43 12 23. Email: <a href="mailto:adrien.meynier_pozzi@vetagro-sup.fr">adrien.meynier_pozzi@vetagro-sup.fr</a></li> <li>B. Cournoyer, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 56 47. Fax. (+33) 472 43 12 23. Email: and <a href="mailto:benoit.cournoyer@vetagro-sup.fr">benoit.cournoyer@vetagro-sup.fr</a></li> </ul> <p><strong>Keywords:</strong></p> <p>BACtpm, Bacteria, <em>tpm</em>, thiopurine-<em>S</em>-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU</p> <p><strong>Description:</strong></p> <ul> <li>The <em>tpm</em> gene codes for the thiopurine-<em>S</em>-methyltransferase (TPMT), an enzyme that can detoxify metalloid-containing oxyanions and xenobiotics (Cournoyer et al., 1998). Bacterial TPMTs radiated apart from human and animal TPMTs, and showed a vertical evolution in line with the 16S rRNA gene molecular phylogeny (Favre‐Bonté et al., 2005).</li> <li>The <em>tpm</em> database, named BACtpm, was designed to apply the <em>tpm</em>-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full <em>tpm</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long <em>tpm</em> sequences of 840 unique taxa belonging to 139 genera.</li> <li>Nucleotide sequences of <em>tpm</em> (range: 190-233 nucleotides) were either retrieved from public repositories (GenBank) or made available by B. Cournoyer’s research group. Colin et al. (2020) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>tpm</em> sequences.</li> <li>BACtpm v.2.0.1 (June 2021 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>tpm </em>sequences down to the species and strain levels. Data is stored in the csv format enabling future user to reformat it to fit their specific needs.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>We thank the worldwide community of microbiologists who made contributions to public databases in the past decades, and made possible the elaboration of the BACtpm database. We also thank the Field Observatory in Urban Hydrology (OTHU, <a href="http://www.graie.org/othu/">www.graie.org/othu/</a>), Labex IMU (Intelligence des Mondes Urbains), the Greater Lyon Urban Community, the School of Integrated Watershed Sciences H2O'LYON, and the Lyon Urban School for their support in the development of this database. This work was funded by the French national research program for environmental and occupational health of ANSES under the terms of project “Iouqmer” EST 2016/1/120, l'Agence Nationale de la Recherche through ANR-16-CE32-0006, ANR-17-CE04-0010, ANR-17-EURE-0018 and ANR-17-CONV-0004, by the MITI CNRS project named Urbamic, and the French water agency for the Rhône, Mediterranean and Corsica areas through the Desir and DOmic projects. We thank former BPOE lab members who contributed to start and expand the BACtpm database: Céline COLINON, Romain MARTI, Emilie BOURGEOIS, Sébastien RIBUN and Yannick COLIN.</p> <p><strong>References:</strong></p> <p>Aigle, A., Colin, Y., Bouchali, R., Bourgeois, E., Marti, R., Ribun, S., Marjolet, L., Pozzi, A.C.M., Misery, B., Colinon, C., Bernardin-Souibgui, C., Wiest, L., Blaha, D., Galia, W., Cournoyer, B., 2021. Spatio-temporal variations in chemical pollutants found among urban deposits match changes in thiopurine S-methyltransferase-harboring bacteria tracked by the tpm metabarcoding approach. Sci. Total Environ. 767, 145425. https://doi.org/10.1016/j.scitotenv.2021.145425</p> <p>Colin, Y., Bouchali, R., Marjolet, L., Marti, R., Vautrin, F., Voisin, J., Bourgeois, E., Rodriguez-Nava, V., Blaha, D., Winiarski, T., Mermillod-Blondin, F., Cournoyer, B., 2020. Coalescence of bacterial groups originating from urban runoffs and artificial infiltration systems among aquifer microbiomes. Hydrol. Earth Syst. Sci. 24, 4257–4273. https://doi.org/10.5194/hess-24-4257-2020</p> <p>Cournoyer, B., Watanabe, S., Vivian, A., 1998. A tellurite-resistance genetic determinant from phytopathogenic pseudomonads encodes a thiopurine methyltransferase: evidence of a widely-conserved family of methyltransferases1The International Collaboration (IC) accession number of the DNA sequence is L49178.1. Biochim. Biophys. Acta BBA - Gene Struct. Expr. 1397, 161–168. https://doi.org/10.1016/S0167-4781(98)00020-7</p> <p>Favre‐Bonté, S., Ranjard, L., Colinon, C., Prigent‐Combaret, C., Nazaret, S., Cournoyer, B., 2005. Freshwater selenium-methylating bacterial thiopurine methyltransferases: diversity and molecular phylogeny. Environ. Microbiol. 7, 153–164. https://doi.org/10.1111/j.1462-2920.2004.00670.x</p>
Robustness assessment of a C++ implementation of a quantized (int8) version of the LeNet-5 convolutional neural network
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid, and 8-bit integers for weights and activations instead of floating-point.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><i><strong>relu2</strong></i>: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li><i><strong>fc1</strong></i>: Fully connected (294, 147)</li><li><i><strong>fc2</strong></i>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>BF</strong>: single, double-adjacent and triple-adjacent bit-flip faults</li><li><strong>S0</strong>: single, double-adjacent and triple-adjacent stuck-at-0 faults</li><li><strong>S1</strong>: single, double-adjacent and triple-adjacent stuck-at-1 faults</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (int8)</li><li><strong>zw</strong>: zero point of the weights (int8)</li><li><strong>b</strong>: biases (int32)</li><li><strong>z</strong>: zero point (int8)</li><li><strong>m</strong>: m (int32)</li></ul><p>Images 200 to 249 from the MNIST dataset have been used as workload.</p><p>This dataset contains the raw data obtained from running exhaustive fault injection campaigns for all considered fault models, targeting all considered locations and for all the images in the workload.</p><p>In addition, the raw data have been lightly processed to obtain global data related to the particular bits and parameters affected by the faults, and the obtained failure modes.</p><h3>Files information</h3><ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. </li><li><i>single_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of single bit-flip faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.</p><p>After that, one fault injection experiment was executed for each bit of each element of each parameter of the CNN.</p><p>Each experiment consisted in:</p><ul><li>Affecting the bits (inverting it in case of bit-flip faults, setting it to 0 or 1 in case of stuck-at-0 or atuck-at-1 faults) identified by the mask.</li><li>Classifying all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (200-249).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.zw, 3 - conv1.m, 4 - conv1.b, 5 - conv1.z, 6 - conv2.w, 7 - conv2.zw, 8 - conv2.m, 9 - conv2.b, 10 - conv2.z, 11 - fc1.w, 12 - fc1.zw, 13 - fc1.m, 14 - fc.b, 15 - fc1.z, 16 - fc2.w, 17 - fc2.zw, 18 - fc2.m, 19 - fc2.b, 20 - fc2.z)</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - {conv1.b, conv1.m, conv1.zw}, [0-74] - conv1.w, 0 - conv1.z, [0-5] - {conv2.b, conv2.m, conv2.zw}, [0-149] - conv2.w, 0 - {conv1.z, conv2.z, fc1.z, fc2.z}, [0-146] - {fc1.b, fc1.m, fc1.zw}, [0-43217] - fc1.w, [0-9] - {fc2.b, fc2.m, fc2.zw}, [0-1469] - fc2.w)</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty])</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1)</li><li><strong>OUTPUT</strong>: 10 integer numbers provided by the CNN as output after processing the image. The highest value identifies the selected category for classification.</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
Additional Data: Poised PABP-RNA hubs implement signal-dependent mRNA decay in development
<p>This repository contains processed data resulting from iCLIP experiments that were analysed in the following paper:"<strong>Poised PABP-RNA hubs implement signal-dependent mRNA decay in development</strong>"<br>The paper is published at Nature Structural and Molecular BIology.</p> <h2><br>Archived data</h2> <p>Data archived in this repository include:</p> <ol> <li>Data derived from iCLIP experiments targeting LIN28A, PABPC1, and PABPC4, that were analysed in the manuscript (see iCLIP.zip). Raw data is available from ENA, with the accession code PRJEB60519. <ol> <li>Sample descriptions are given in iCLIP-SampleAnnotation.csv</li> <li>Crosslink files in BED6 format (individual replicates and merged replicates)</li> <li>Peak files generated with the Clippy peak caller in BED6 format</li> <li>K-mer enrichment around high-confidence crosslink sites in the 3'-UTRs, calculated by the PEKA software</li> </ol> </li> <li>Expression values (salmon quantfiles) for 3'-seq experiments, specified in "QuantseqExperimentsAnnotation.tsv", are available in "SalmonQuantfiles.zip". Raw data is available from ENA, with the accession code PRJEB60519.</li> <li>Source code of the nextflow pipeline, which was used on the iMaps webserver to analyse iCLIP data and produce the files archived here (see imaps-nf-0.30.zip).</li> <li>A list of naive genes, that were analysed in the manuscript (see NaiveGeneIds.csv).</li> </ol> <h2>Details on iCLIP data generation</h2> <p>iCLIP data for LIN28A-WT (in 2iL and FGF2 treated cells), LIN28A-S200A (in FGF2 treated cells) as well as for PABPC1 and PABPC4 (in LIN28A KO cells with and without LIN28A overexpression), were analysed on iMaps Goodwright server (<a href="https://imaps.goodwright.com/">https://imaps.goodwright.com/</a>). The LIN28A iCLIPs were analysed on 18th of July, 2022; the PABPC iCLIPs were analysed on 26th of December, 2022. The code and settings used in the pipeline (release v0.30) can be viewed at <a href="https://github.com/goodwright/imaps-nf">https://github.com/goodwright/imaps-nf </a>, and is also archived here - (imaps-nf-0.30.zip)<br> </p> <ul> <li>First, reads were demultiplexed using Ultraplex and barcodes were trimmed from the reads. The default Ultraplex settings were applied, as denoted below:</li> </ul> <blockquote> <p>adapter='AGATCGGAAGAGCGGTTCAG'<br>adapter2='AGATCGGAAGAGCGTCGTG'<br>barcodes='barcode.csv',<br>final_min_length=20<br>fiveprimemismatches=1<br>ignore_no_match=False<br>ignore_space_warning=False<br>inputfastq='MOD4878A1-merged.fastq.gz',<br>keep_barcode=False,<br>min_trim=3,<br>outputprefix='demux',<br>phredquality=30,<br>phredquality_5_prime=0,<br>sbatchcompression=False,<br>threads=10,<br>threeprimemismatches=0,<br>ultra=False</p> </blockquote> <p> </p> <ul> <li>TrimGalore was used to run FASTQC and quality trim the reads and remove reads with length less than 10 nt:</li> </ul> <blockquote> <p>trim_galore --fastqc --length 10 -q 20 --cores 8 --gzip file.fastq.gz</p> </blockquote> <p> </p> <ul> <li>Reads were then premapped to rRNA, tRNA sequences referred to as small RNA, smRNA, using mouse genome build (GRCm39 GENCODE M28 annotation) with Bowtie v1.3.0 (Langmead et al., 2009)</li> </ul> <blockquote> <p>bowtie --threads 12 --sam -x $INDEX -q --un file.unmapped.fastq -v 2 -m 100 --norc --best --strata file.fq.gz 2</p> </blockquote> <p> </p> <ul> <li>Reads that did not map with Bowtie were then aligned with STAR v2.7.9a (Dobin et al., 2013) to mouse genome build (GRCm39 GENCODE M28 annotation).</li> </ul> <blockquote> <p>STAR \<br>--genomeDir star \<br>--readFilesIn file.unmapped.fastq.gz \<br>--runThreadN 12 \<br>--outFileNamePrefix 1_R1. \<br>\<br>--sjdbGTFfile Homo_sapiens_filtered.gtf \<br>--outSAMattrRGline 'ID:1_R1' 'SM:1_R1' \<br> --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outSAMattributes All --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 1000000 --outFilterScoreMin 10 --alignEndsType Extend5pOfRead1 --twopassMode Basic</p> </blockquote> <p> </p> <ul> <li>PCR-duplicates were removed using UMI-tools (Smith, Heger and Sudbery, 2017)</li> </ul> <blockquote> <p>java -jar /UMICollapse/umicollapse.jar \<br> bam \<br> -i file.Aligned.sortedByCoord.out.bam \<br> -o file.dedup.bam \<br> --umi-sep rbc:</p> </blockquote> <p> </p> <ul> <li>The nucleotide preceding each sequencing read was assigned as the crosslink event.</li> </ul> <p> </p> <ul> <li>Peaks of crosslinking signal were identified with Clippy v1.4.1, using the default settings.</li> </ul> <p> </p> <ul> <li>Obtained peaks and crosslink sites were used to run PEKA v1.0.0 (Kuret et al., 2022), using the default settings.</li> </ul> <p> </p> <ul> <li>For Clippy and PEKA, the GENCODE primary assembly annotation M28 was filtered to retain only entries with transcript support level 1 or 2, in genes where such transcripts were available, and used to produce a segmentation file with the <em>get_segments</em> function from the iCount tool (Curk, 2019).</li> </ul> <p> </p> <ul> <li>All files generated during data processing are available from the iMaps Goodwright webserver for analysis of CLIP data (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively).</li> </ul> <h2>Source data</h2> <p>Raw sequencing reads, from which the data enclosed here were derived, are accessible at ENA (PRJEB60519).<br>The raw sequencing reads and all data produced by the analysis pipeline is also available at the iMaps webserver (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively); and on the updated Flow webserver (see <a href="https://app.flow.bio/projects/882635250203/">https://app.flow.bio/projects/882635250203/</a> and <a href="https://app.flow.bio/projects/340215254997/">https://app.flow.bio/projects/340215254997/ </a>for LIN28A and PABPC1/4 iCLIPs, respectively).</p> <h2>Downstream computational analysis of enclosed data</h2> <p>The code, used to analyse the data enclosed here and train the CNN to predict transcript stability in naive-to-primed transition based on 3'UTR nucleotide sequence, is available at GitHub (<a href="https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics">https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics</a>) and archived on Zenodo (<a href="../doi/10.5281/zenodo.10054297">https://zenodo.org/doi/10.5281/zenodo.10054297</a><strong>).</strong></p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Dataset for "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b"
<p>This is the supplemental materials for the Astronomy & Astrophysics publication "Implementation of disequilibrium chemistry to spectral retrieval code ARCiS and application to 16 exoplanet transmission spectra. Indication of disequilibrium chemistry for HD 209458b and WASP-39b". Please refer to "README.md" for details.</p>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Figure 3: Scheme of the procedure adopted for implementing the Sand Dune Acts of 1903/1908, to reclaim the lands affected by sand drifting
<p>Figure 3 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore
<p>Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore; see https://github.com/crisprVerse/crisprScore for more detail.</p> <p> </p>
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
implementation of an in-line Kerr active cavity equipped with a loop mirror
<p>This dataset includes the measurements of the resonances collected at the through port of an active fiber cavity (in in-line configuration) of 5 meters of length based on a step index silica fiber and including a loop mirror and a fiberized mirror at the two ends of the cavity. The dataset includes also the measurement of the effective losses of the in-line active cavity vs. the intracavity power. </p>
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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.