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18,621 results for “development;”
Asaia spp. accelerate development of the yellow fever mosquito, Aedes aegypti, via interactions with the vertically transmitted larval microbiome
<p><strong><span>Background:</span></strong><em> Aedes aegypti</em> mosquitoes are the primary vectors of yellow fever, dengue, chikungunya and Zika virus. Control programs primarily rely on insecticide application, which encounter challenges related to efficacy and resistance evolution. Alternative strategies, such as the sterile insect technique, highly depend on efficient mass-rearing of healthy insects prior to mass release. Based on effects seen in other mosquito species, we tested the hypothesis that acetic acid bacteria <span>of the </span><em>Asaia</em> <span>genus are</span> mutualist<span>s</span> for developing <em>Ae. aegypti</em> larvae. We tested for beneficial interactions across three <em>Asaia </em>species and whether <em>Asaia</em> inoculation benefited both axenic and conventionally reared larvae. To better understand the underlying mechanisms, we characterized the larval microbiome<span> </span>using culture-based methods and 16S rRNA gene amplicon sequencing.</p> <p><strong>Results:</strong><span> <span>Even</span></span> though <em>Asaia </em>bacteria were transient members of the gut community in conventionally reared insects<span>, t</span>wo <em>Asaia </em>species accelerated larval development relative to controls.<span> Despite their transient nature, </span>the two mutualist <em>Asaia</em> species had lasting impacts on the larval microbiome, mostly by altering the relative abundance of the most dominant bacteria genera <em>Klebsiella</em> and <em>Pseudomonas</em> and other minor components<span>.</span> Axenic larvae that were inoculated with <em>Asaia </em>were dominated by this group, but always exhibited slower development than conventionally reared insects.</p> <p><strong>Conclusions:</strong> These results reveal <em>Asaia</em> as a poor mutualist for <em>Ae. aegypti</em>, with its<em> </em>positive effect on the host mediated by interactions with other bacteria. A practical application of <em>Asaia </em>for improving mass-rearing efficiency results from the acceleration of development time to pupation by a day.</p>
Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Journal of Climate and Community Development (JOCCD)
<p>cover of JOCCD</p>
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>
DCASE 2024 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2024 "Few-shot Bioacoustic Event Detection" consists of 217 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB24/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___RD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_set_annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br>TRAINING SET<br>-----------------------------------------------------<br>Number of audio recordings | 174<br>Total duration | 21 hours<br>Total classes | 47<br>Total events | 14229<br>-----------------------------------------------------<br>TRAINING SET/BV<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 10 hours<br>Total classes | 11<br>Total events | 9026<br>Sampling rate | 24000 Hz<br>-----------------------------------------------------<br>TRAINING SET/HT<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 5 hours<br>Total classes | 5<br>Total events | 611<br>Sampling rate | 6000 Hz<br>-----------------------------------------------------<br>TRAINING SET/JD<br>-----------------------------------------------------<br>Number of audio recordings | 1<br>Total duration | 10 mins<br>Total classes | 1<br>Total events | 357<br>Sampling rate | 22050 Hz<br>-----------------------------------------------------<br>TRAINING SET/MT<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 1 hour and 10 mins<br>Total classes | 4<br>Total events | 1294<br>Sampling rate | 8000 Hz<br>-----------------------------------------------------<br>TRAINING SET/WMW<br>-----------------------------------------------------<br>Number of audio recordings | 161<br>Total duration | 4 hours and 40 mins<br>Total classes | 26<br>Total events | 2941<br>Sampling rate | various sampling rates<br>-----------------------------------------------------</p> <p>-----------------------------------------------------<br>VALIDATION SET<br>-----------------------------------------------------<br>Number of audio recordings | 43<br>Total duration | 49 hours and 57 minutes<br>Total classes | 7<br>Total events | 3504<br>-----------------------------------------------------<br>VALIDATION SET/HB<br>-----------------------------------------------------<br>Number of audio recordings | 10<br>Total duration | 2 hours and 38 minutes<br>Total classes | 1<br>Total events | 712<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 3 hours<br>Total classes | 2<br>Total events | 292<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/ME<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 20 minutes<br>Total classes | 2<br>Total events | 73<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB24<br>-----------------------------------------------------<br>Number of audio recordings | 4<br>Total duration | 2 hours<br>Total classes | 2<br>Total events | 350<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/RD<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 18 hours<br>Total classes | 1<br>Total events | 1372<br>Sampling rate | 48000 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PW<br>-----------------------------------------------------<br>Number of audio recordings | 15<br>Total duration | 24 hours<br>Total classes | 1<br>Total events | 705<br>Sampling rate | 96000 Hz<br>-----------------------------------------------------</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br>---------------------<br>Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br>---------------------<br>Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2024_task5_training_set_classes.csv and DCASE2024_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. Additionally, DCASE2024_task5_validation_set_classes.csv also provides a recording names column.</p> <p>DCASE2024_task5_training_set_classes.csv<br>---------------------<br>dataset, class_code, class_name</p> <p>DCASE2024_task5_validation_set_classes.csv<br>---------------------<br>dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1 June 2024</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Burooj Ghani - burooj.ghani@naturalis.nl | Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p> <p>Alternately, join us on Slack: <a href="https://join.slack.com/t/dcase/shared_invite/zt-12zfa5kw0-dD41gVaPU3EZTCAw1mHTCA">task-fewshot-bio-sed</a></p> <p> </p>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Raw data for Development of Germline Progenitors in Larval Queen Honeybee ovaries
<p>This repository contains raw files for images relating to a publication of honeybee ovary development. That work is Cullen, Delargy and Dearden 2024, <strong><span>Development of Germline Progenitors in Larval Queen Honeybee ovaries. </span></strong><span>The data is organised in folders relate to each figure, and is in .oir format, a raw data format produced by Olympus confocal systems. This data file format is able to be read by FIJI.</span></p>
Database of Participatory Practices and Social Innovations in Wind Energy Developments
<p>Inês Campos was responsible for designing the database, collecting data, and analyzing data. Flávio Oliveira also collaborated in the design of the database and data collection. </p>
Sample of Developer Contributions by Gender
<p>Two sets of 100k anonymized software developers from World of Code after applying the name-to-gender inference tool Wiki-Gendersort and quantifying each developer's number of contributions by commit, project, files; files are classified into 116 sub-categories according to language or development file type (e.g., programming, scripting, or markdown languages such as C, C++, Python, JavaScript, Assembly, HTML; build files such as Dockerfiles, CMake, etc; configuration files; documentation files such as README and Markdown; and libraries. One sample is stratified (50/50 split between male and female developers), and one sample is representative of the population. Wiki-Gendersort outputs M (male), F (female), UNK (unknown), INI (initialis), or UNI (unisex). The scripts used to generate the data set can be found on GitHub. </p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals
<p>This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.</p>
Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)
<p>This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.</p> <p>The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.</p> <p>The deposited data includes:</p> <ul> <li>krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.</li> <li>krkCMd_images.zip: archive containing source image files in folders by test series: <br>- stacked images of cracks in subsequent stages of self-healing (.tif files),<br>- zip archives assigned to image stacks and containing sets of ImageJ data files .roi,<br>- ImageJ .roi files specifying the locations of grid lines in the images.</li> <li>krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.</li> </ul> <p><span>For details please see the <a href="https://doi.org/10.1038/s41597-025-04485-z">data descriptor [1]</a>. When referring to the data in publications please cite [1].</span></p> <p>[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. <em>Sci Data</em> <strong>12</strong>, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z</p> <p>[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. <em>Constr. Build. Mater.</em> <strong>422</strong>, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Dataset for Gelatinous fibers develop asymmetrically to support bends and coils in common bean vines (Phaseolus vulgaris L., Fabaceae)
<ol> <li>Internode_lengths_AveragePerGroup_Stage5.csv = averaged internode lengths for each treatment groups at stage 5 (plastochron 9). Data represented in Appendix S2.</li> <li>Internode_lengths_Stage5.csv = data of all internodes lengths per individual plant at stage 5 (plastochron 9). Data represented in Appendix S2.</li> <li>InternodeLengths_allStages.csv = internode lengths through 5 stages (0-9 plastochon). Data represented in Appendix S1.</li> </ol> <p>Associated scripts: https://github.com/angelique-acevedo/Common-Bean-Analysis</p>
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
Roger Powell and the Development of the Non-Adhesive Binding
<p>This is the recording and transcript of a lecture given by Anthony Cains in 1988 at the Fifth Anniversary Conference of the Parker Library Conservation Project. A publication based upon the conference paper, supplemented with new material and updated, was released in 1994:</p> <p>Cains, Anthony G. 1994. ‘Roger Powell and His Early Irish Manuscripts in Dublin’. In <em>Conservation and Preservation in Small Libraries</em>, edited by Nicholas Hadgraft and Katherine Swift, 151–56. Cambridge: Parker Library Publications.</p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Development of MeV TOF-SIMS capillary microprobe at the Ruđer Bošković Institute in Zagreb
<p>New Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) setup using MeV heavy ions for the excitation is developed at the Ruđer Bošković Institute accelerator facility. To focus heavy MeV ions to micron dimensions, conical glass capillary is used instead of quadrupole magnetic lenses. The setup uses a continuous primary beam where START signal for TOF is obtained from the PIN diode placed behind the thin transmission sample. Results showing measured energy spectra for several primary heavy ions are presented and compared with theoretical simulations. . The first mass spectra obtained with the new setup using reflectron-type TOF analyzer are given together with the mass and spatial resolution values of the new setup. </p>
ScienceDex guides
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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.