Find research datasets worth reusing

Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans dataset

This dataset is connected to the protocol article titled: Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans Abstract: Candida albicans is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of C. albicans to reversibly change morphology and to grow as yeast, pseudohyphal or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help fight C. albicans infections. While numerous studies investigated gene expression changes occurring during C. albicans morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single molecule fluorescent in situ hybridization (smFISH) protocol for C. albicans, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae or hyphal cells. We quantified the expression of two mRNAs, cell cycle-controlled mRNA (CLB2) and a transcription regulator (EFG1), which show differential expression in the different morphological cell types and in different nutrient conditions. In this protocol we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell-segmentation and mRNA spot analysis. Raw data is provided with the protocol to favour reproducibility. This approach could benefit the molecular characterization of C. albicans and other filamentous fungi, pathogenic or non-pathogenic. Data description: This dataset consists of a FISH experiment spanning two different mRNAs, EFG1 and CLB2, and two different nutrient condition, being SPIDER37 and TSB37 in Candida albicans. For culturing, the C. albicans wildtype strain SC5314 was inoculated at 30 degrees overnight (~15 hours) in 10 mL of TSB medium in a 30 degree (celsius) shaking incubator. Next, samples were diluted to a density of 10^5 cells/ mL and inoculated for 6 hours in either 30 mL TSB medium or Spider medium at 37 degrees in falcon tubes on an orbital microplate shaker. Then, samples were fixated by adding PFA to a final concentration of 4% to the medium. For hybridization, both mRNAs were hybridized independently by specific DNA oligo labelled with a Quasar670 dye to enable the visualisation of single mRNA molecules. As both genes are labelled by the same dye, these oligos were not co-applied to the same sample but to independent samples. Microscopy For smFISH imaging we use an Olympus BX-63 epifluorescence microscope equipped with Ultrasonic stage and UPlanApo 100x 1.35NA oil-immersion objective (Olympus). Lumencore SOLA FISH light source, a Hamamatsu ORCA-Fusion sCMOS camera (6.5 µm-pixel size) mounted using U-CMT C-Mount Adapter, and zero-pixel shift filter sets: F36-500 DAPI HC Brightline Bandpass Filter, F36-502 FITC HC BrightLine Filter, F36-542 Cy3 HC BrightLine Filter, and F36-523 Cy5 HC BrightLine Filter. Images are acquired across 61-81 optical sections (depending on the sample thickness) with a z-step size of 0.2 μm. The CellSens software (Olympus) is used for instrument control and image acquisition. For the DAPI  channel 10-50 ms of exposure was used. Whilst, for the CY5 channel, used imaging the FISH probes, 750 ms was applied.

opencc-by-4.0Jun 2023View details →

Fluxes of the protonated masses from the soil samples collected from two temperate ecosystems detected by PTR-ToF-MS

Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs. In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic vs. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms. This dataset therefore is collected from these experiments. It includes the raw flux data and figure source data associated with a peer-reviewed publication in Soil Biology & Biochemistry at <a href="https://doi.org/10.1016/j.soilbio.2023.109153">https://doi.org/10.1016/j.soilbio.2023.109153</a>. Overall, our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil biogeochemical properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18-68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems. Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or Prof. Rinnan at riikkar@bio.ku.dk

Irish Potato Imagery Dataset for Early Detection of Crop Diseases

The annotated dataset consists of irish potatoes leaf imagery for early diseases detection. The irish potato crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 22nd November 2022 and 08th April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Early blight, and (ii) Late blight diseases in irish potatoes. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones.  All images are in the .zip files; “lateblt.zip” has 20,499 images, “healthy.zip” has 20,438 images, and “earlyblt.zip” has 17,772 images. A total of 58,709 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.

Common Beans Imagery Dataset for Early Detection of Crop Diseases

The annotated dataset consists of common beans leaf imagery for early diseases detection. The common beans crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 20th   October 2022 and 10th April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Bean anthracnose, and (ii) Bean rust diseases in common beans. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones.  All images are in the .zip files; “anthra.zip” has 13,531 images, “healthy.zip” has 24,973 images, and “rust.zip” has 20,568 images. A total of 59,072 image files are labelled. This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.

opencc-by-4.0Jan 2022View details →

CDSet: Crosswalk DataSet for Zebra Crossing Detection

This dataset is a collection of crosswalk (zebra crossing) images collected on vehicle-mounted cameras, used to study advanced neural network algorithms for crosswalk detection and analysis of car crossing behavior. The dataset contains 3434 images, including real scenes such as daytime, rainy days, occlusion, deformation, truncation, night, damaged, dazzling, etc. The targets includes two categories: crosswalk and guide arrows. The dataset is divided into 3080 training sets and 354 test sets. In addition, this dataset also provides additional 1770 images labeled with or without crosswalk for testing. The correspoding paper is CDNet: a real-time and robust crosstalk detection network on Jetson nano based on YOLOv5, which can be found in <a href="https://doi.org/10.1007/s00521-022-07007-9">doi: 10.1007/s00521-022-07007-9</a> (<a href="https://rdcu.be/cHuc8">Read Paper</a>). The benchmark model is based on the previous SOTA model YOLOv5 and has undergone multiple improvements such as ROI, SE, NST, SSVM, Synthetic Fog, etc. The open source code can be found on <a href="https://github.com/zhangzhengde0225/CDNet">Github-CDNet</a>.

Supplementary materials of "Calibrated weighted permutation test detects ancient language connections in the Circumpolar area (Chukotian-Nivkh and Yukaghir-Samoyedic)"

Supplementary materials of the paper: Alexei S. Kassian, George Starostin, Mikhail Zhivlov, Sergey A. Spirin. Calibrated weighted permutation test detects ancient language connections in the Circumpolar area (Chukotian-Nivkh and Yukaghir-Samoyedic). Journal of Historical Linguistics, 2024, <a href="https://doi.org/10.1075/jhl.00014.kas">https://doi.org/10.1075/jhl.00014.kas</a>.  The Supplement includes the datasets, descriptions, comments and all technical files which makes our linguistic experiment fully reproducible: <ul> <li>Supplement text (pdf): 1. Consonant classes; 2. Weighted permutation test; 3. Negative control and calibration of permutation test outcomes; 4. Pairwise comparison between Yeniseian, Burushaski and all the Na-Dene wordlists; 5. CC-matches in statistically significant pairs (Chukotian-Itelmen, Nivkh, Samoyed, Yukaghir, Yeniseian, Na-Dene, Burushaski); 6. Basic information on the language families and groups; 7. Linguistic comments on individual Swadesh forms; 8. Transcription system.</li> <li>Supplement tables (xls): 1. Stability of the Swadesh concepts; 2. 110-item wordlists for the languages of the Circumpolar area; 3. Weighted permutation test for the negative control group; 4. Consonant classes.</li> <li>Technical files of the permutation test: 1. Circumpolar languages; 2. Negative control group (incl. dataset); 3. Positive control group; 4. Yeniseian-Dene-Burushaski hypothesis</li> </ul> The software package is available at <a href="https://github.com/dmkrylov/starling-permutation-test">https://github.com/dmkrylov/starling-permutation-test</a>

Toxic Memes Detection Dataset

The dataset for training and evaluating toxic memes detection models. Images are collected from popular Russian Telegram channels and labelled with respect to <a href="https://transparency.fb.com/policies/community-standards">Facebook Community Standards</a>.

Multimodal Toxic Memes Detection Dataset

The dataset for training and evaluating multimodal toxic memes detection models. Contains images, extracted texts and toxicity labels. Images are collected from popular Russian Telegram channels and labelled with respect to <a href="https://transparency.fb.com/policies/community-standards">Facebook Community Standards</a>.

Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans small example dataset

This small example dataset is connected to the protocol article titled: Single-molecule Fluorescent In Situ Hybridization (smFISH) for RNA detection in the fungal pathogen Candida albicans Abstract: Candida albicans is the most prevalent human fungal pathogen. Its pathogenicity is linked to the ability of C. albicans to reversibly change morphology and to grow as yeast, pseudohyphal or hyphal cells in response to environmental stimuli. Understanding the molecular regulation controlling those morphological switches remains a challenge that, if solved, could help fight C. albicans infections. While numerous studies investigated gene expression changes occurring during C. albicans morphological switches using bulk approaches (e.g., RNA sequencing), here we describe a single-cell and single-molecule RNA imaging and analysis protocol to measure absolute mRNA counts in morphologically intact cells. To detect endogenous mRNAs in single fixed cells, we optimized a single molecule fluorescent in situ hybridization (smFISH) protocol for C. albicans, which allows one to quantify the differential expression of mRNAs in yeast, pseudohyphae or hyphal cells. We quantified the expression of two mRNAs, cell cycle-controlled mRNA (CLB2) and a transcription regulator (EFG1), which show differential expression in the different morphological cell types and in different nutrient conditions. In this protocol, we described in detail the major steps of this approach: growth and fixation, hybridization, imaging, cell-segmentation and mRNA spot analysis. Raw data is provided with the protocol to favour reproducibility. This approach could benefit the molecular characterization of C. albicans and other filamentous fungi, pathogenic or non-pathogenic. Data description: This dataset consists of a FISH experiment spanning two different mRNAs, EFG1 and CLB2, and one nutrient condition, SPIDER37, in Candida albicans. For culturing, the C. albicans wildtype strain SC5314 was inoculated at 30 degrees overnight (~15 hours) in 10 mL of TSB medium in a 30 °C shaking incubator. Next, samples were diluted to a density of 10^5 cells/ mL and inoculated for 6 hours in 30 mL Spider medium at 37 °C in falcon tubes on an orbital microplate shaker. Then, samples were fixated by adding PFA to a final concentration of 4% to the medium. For hybridization, both mRNAs were hybridized independently by specific DNA oligo labelled with a Quasar670 dye to enable the visualisation of single mRNA molecules. As both genes are labelled by the same dye, these oligos were not co-applied to the same sample but to independent samples. Microscopy For smFISH imaging we use an Olympus BX-63 epifluorescence microscope equipped with Ultrasonic stage and UPlanApo 100x 1.35NA oil-immersion objective (Olympus). Lumencore SOLA FISH light source, a Hamamatsu ORCA-Fusion sCMOS camera (6.5 µm-pixel size) mounted using U-CMT C-Mount Adapter, and zero-pixel shift filter sets: F36-500 DAPI HC Brightline Bandpass Filter, F36-502 FITC HC BrightLine Filter, F36-542 Cy3 HC BrightLine Filter, and F36-523 Cy5 HC BrightLine Filter. Images are acquired across 61-81 optical sections (depending on the sample thickness) with a z-step size of 0.2 μm. The CellSens software (Olympus) is used for instrument control and image acquisition. For the DAPI  channel 10-50 ms of exposure was used. Whilst, for the CY5 channel, used for imaging the FISH probes, 750 ms was applied.

opencc-by-4.0Sep 2023View details →

tomato-leaf-diseases-detection

This dataset is collected from https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf . we are saving this dataset for future use purpose. All copyright goes to this kaggel website link.  There are tomato leaf diseases in this dataset. There are almost 9 classes of tomato leaf diseases and 1 class for healthy tomato leaf diseases. This ten classes are : <ol> <li>Mosaic Virus                            </li> <li>Target Spot</li> <li>Bacterial Spot</li> <li>Yellow Leaf Curl Virus</li> <li>Late Blight</li> <li>Leaf Mold</li> <li>Early Blight</li> <li>Spider Mites Two-Spotted Spider Mite</li> <li>Tomato Healthy</li> <li>Septoria Leaf Spot.</li> </ol>

opencc-by-4.0Mar 2022View details →

pLitterStreet - Street Level Plastic Litter Detection Dataset

pLitterStreet dataset comprises of more than 13,000 images. These images were captured using vehicle-mounted cameras that were strategically positioned to focus on the sides of streets. The primary objective of this dataset is to facilitate research related to street litter and its impact on the environment. Annotations for the images are provided in the widely-used Microsoft COCO JSON format. Image in the dataset is fully annotated, enabling the identification and categorization of various types of litter found along urban and rural streets. These annotations include precise labeling of litter items, making the dataset an invaluable resource for developing and evaluating object detection and image recognition models.

opencc-by-4.0May 2023View details →

Data and code for 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes'

This repository contains the code and files for reproducing the analyses and results reported in 'Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes' by Gavin M. Douglas and B. Jesse Shapiro (<a href="https://doi.org/10.1038/s41559-023-02268-6">https://doi.org/10.1038/s41559-023-02268-6</a>). File organization and descriptions: <ul> <li>code/ - Contains GitHub repository releases of code used in manuscript (the other folders contain datafiles only). This code is provided here as well as on GitHub to ensure long-term access. <ul> <li>handy_pop_gen-1.1.0/ - release v1.1.0 of the convenience repository (used for specific data processing and analysis steps referred to in the manuscript).</li> <li>pangenome_pseudogene_null-1.1.0/ - Main code repository for manuscript.</li> </ul> </li> </ul>   <ul> <li>broad_pangenome_analysis/ <ul> <li>element_info/element_counts.tsv.gz - Counts of (filtered) pseudogenes and intact genes called per genome accession.</li> <li>element_info/gene_sizes.tsv.gz - Gene sizes in base-pairs.</li> <li>element_info/pseudogene_sizes.tsv.gz - Filtered pseudogene sizes in base-pairs.</li> <li>element_info/element_percent_coverage/*tsv.gz - Tables containing the percent genome coverage of genes and pseudogenes, by accession and averaged over accessions per species separately.</li> <li>example_Mycoplasmopsis_bovis_panaroo_output.csv.gz - Panaroo output table for Mycoplasmopsis bovis, which was used for an example. Corresponds to the gene_presence_absence.csv file in the raw Panaroo output.</li> <li>focal_and_non.focal_full_to_short.tsv.gz - Mapfile of full to short (and unique) species ids used in analysis. Primarily to include species ids in cluster names without making them unnecessarily long.</li> <li>genome_info/accessions.tsv.gz - Genome accessions used for broad pangenome analysis (note that not all genome accessions could be downloaded [and were ignored], which is indicated in the "could_download" column).</li> <li>genome_info/genome_sizes.tsv.gz - Sizes of all genomes used for the broad pangenome analysis.</li> <li>metrics_additional_subsamples.tsv.gz - Contains columns also found in the pangenome_and_related_metrics.tsv.gz file below, but based on genome subsamplings of 3 and 20, rather than 9.</li> <li>model_output/pangenome_linear_models.rds - R Data Serialization files containing the output of R linear model objects (generated by lm and provided as an R list object). There are separate elements in the list for the mean number of genes, genomic fluidity, percentage singletons (si), and si/sp.</li> <li>model_output/linear_model_coef.tsv.gz - Coefficient summary table for all linear models.</li> <li>pangenome_and_related_metrics.tsv.gz - Metrics used for broad pangenome analysis across 670 prokaryotic species. Note that this table was filtered down to 668 species after excluding those with < 9 genomes.</li> <li>pangenome_and_related_metrics_filt.tsv.gz - Filtered table, as described above.</li> <li>taxonomy.tsv.gz - Taxonomy for all species used for this analysis, taken from GTDB. Row names are species names.</li> </ul> </li> </ul>   <ul> <li>indepth_10_species_analysis/ <ul> <li>cluster_breakdown_tables/ - Folder containing tables providing breakdown of how clusters are distributed by element type, pangenome partition, and species. Provided for easy plotting.</li> <li>cluster_COG_annot.tsv.gz - Mapping of cluster IDs to COG annotations.</li> <li>cluster_filt_lengths_and_additional.tsv.gz - Metadata on clusters, most pertinently the length of the representative sequence in the cluster (which was used to filter out some clusters, below the cut-off which pseudogenes could not be called).</li> <li>cluster_member_breakdown.tsv.gz - Table providing information on each element (called pseudogenes and intact genes) and provides information such as what cluster they are part of, what species and genome accession they are found in, etc.</li> <li>cluster_types.rds - R Data Serialization file containing R list providing breakdown of all clusters into categories (intact/pseudogene/mixed, where mixed means containing both pseudogene and intact elements).</li> <li>COG_enrichment_results/ultra.cloud-COG-gene-enrichments.tsv.gz - Output file with enrichment test summaries for COG IDs in significant COG categories, which was run for the ultra-cloud pangenome partition model only.</li> <li>element_glmm_input.tsv.gz - Table containing all information used for fitting generalized linear mixed models.</li> <li>focal_species.txt - Names of species used for the in-depth analysis.</li> <li>genome_info/ - Folder containing the genome accessions (and the corresponding genome sizes) for all ten analyzed species.</li> <li>glmm_output/ - Folder containing R Data Serialization files containing output R objects after fitting generalized linear mixed models (only ultra-rare files are present, due to file size constraints).</li> <li>per_genome_element.type_percent_coverages.rds - R Data Serialization file containing R list providing the percent coverage by intact genes vs pseudogenes per accession (nested by species)</li> </ul> </li> </ul>

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Environmental DNA-based detection of Batrachochytrium salamandrivorans in captive settings

Detecting pathogens in the live animal trade is critical for tracking and preventing their movement, introduction, and spillover into susceptible fauna. However, the scale of the live animal trade makes individually testing animals infeasible for all but the most economically important taxa. For instance, while the fungal pathogen, Batrachochytrium salamandrivorans (Bsal), threatens amphibian, particularly caudate diversity, in Europe and the Americas, screening even a fraction of the millions of live amphibians imported into the United States, alone, is impractically laborious and expensive. A promising alternative to individual-level sampling (e.g., swabbing the skin of salamanders) is to instead collect DNA from the animals' environment (e.g., housing container or water) which allows us to screen a whole group of animals at a time. We used a series of experiments with Bsal-spiked water and substrates and experimentally infected rough-skinned newts (Taricha granulosa) to determine how best to collect Bsal environmental DNA (eDNA) samples, that is, which methods yield the greatest recovery of Bsal eDNA, and evaluate the capacity of these methods to detect Bsal-infected animals in conditions that might be found in captive settings and trade. We found that filtering water housing infected animals for even an hour can consistently recover detectable levels of Bsal eDNA, that there is little evidence of Bsal eDNA being clumped in housing containers or being swamped or inhibited under realistically dirty conditions, and that eDNA-based methods achieves an equivalent or higher chance of detecting Bsal infections in a group of co-housed newts with fewer samples than traditional methods of individually swabbing. By sampling the genetic materials shed or produced by a whole group of animals, eDNA-based methods are a powerful means of detecting pathogens, such as Bsal, in shipment and captive population. These methods bring routine pathogen surveillance into reach in many more contexts and can thus be an important tool in conservation and disease control.

opencc-zeroSep 2023View details →

opencc-by-nc-4.0Sep 2023View details →

Dataset and Models for Detection of News Agency Releases in Historical Newspapers

This record contains the annotated datasets and models used and produced for the work reported in the Master Thesis "Where Did the News come from? Detection of News Agency Releases in Historical Newspapers " (<a href="https://infoscience.epfl.ch/record/305129?&ln=en">link</a>). Please cite this report if you are using the models/datasets or find it relevant to your research: <pre><code>@article{Marxen:305129, title = {Where Did the News Come From? Detection of News Agency Releases in Historical Newspapers}, author = {Marxen, Lea}, pages = {114p}, year = {2023}, url = {http://infoscience.epfl.ch/record/305129}, }</code></pre> 1. DATA The newsagency-dataset contains historical newspaper articles with annotations of news agency mentions. The articles are divided into French (fr) and German (de) subsets and a train, dev and test set respectively. The data is annotated at token-level in the CoNLL format with IOB tagging format. The distribution of articles in the different sets is as follows: <table> <caption>Dataset Statistics</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">Lg.</th> <th scope="col">Docs</th> <th scope="col">Agency Mentions</th> </tr> </thead> <tbody> <tr> <th scope="row">Train</th> <td>de</td> <td>333</td> <td>493</td> </tr> <tr> <th scope="row"> </th> <td>fr</td> <td>903</td> <td>1,122</td> </tr> <tr> <th scope="row">Dev</th> <td>de</td> <td>32</td> <td>26</td> </tr> <tr> <th scope="row"> </th> <td>fr</td> <td>110</td> <td>114</td> </tr> <tr> <th scope="row">Test</th> <td>de</td> <td>32</td> <td>58</td> </tr> <tr> <th scope="row"> </th> <td>fr</td> <td>120</td> <td>163</td> </tr> </tbody> </table> Due to an error, there are seven duplicated articles in the French test set (article IDs: courriergdl-1847-10-02-a-i0002, courriergdl-1852-02-14-a-i0002, courriergdl-1860-10-31-a-i0016, courriergdl-1864-12-15-a-i0005, lunion-1860-11-27-a-i0004, lunion-1865-02-05-a-i0012, lunion-1866-02-16-a-i0009).   2. MODELS The two agency detection and classification models used for the inference on the <a href="https://impresso-project.ch/">impresso</a> Corpus are released as well: <ul> <li>newsagency-model-de: based on <a href="https://www.deepset.ai/german-bert">German BERT</a> (with maximum sequence length 128), fine-tuned with the German training set of the newsagency-dataset</li> <li>newsagency-model-fr: based on <a href="https://huggingface.co/dbmdz/bert-base-french-europeana-cased">French Europeana BERT</a> (with maximum sequence length 128), fine-tuned with the French training set of the newsagency-dataset</li> </ul> The models perform multitask classification with two prediction heads, one for token-level agency entity classification and one for sentence-level (has_agency: yes/no). They can be run with TorchServe, for details see the <a href="https://github.com/impresso/newsagency-classification/tree/main/lib/bert_classification">newsagency-classification</a> repository.   Please refer to the report for further information or contact us.   3. CODE <a href="https://github.com/impresso/newsagency-classification">https://github.com/impresso/newsagency-classification</a>   4. CONTACT Maud Ehrmann (EPFL-DHLAB) Emanuela Boros (EPFL-DHLAB)

opencc-by-4.0Sep 2023View details →

ASSIST-IoT Multimodal Fall Detection Dataset

Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in <code>notes.md</code>. Contents The repository contains: <ul> <li><code>data/location_data.csv</code> and <code>data/full_acceleration</code> – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified</li> <li><code>data/subsampled_acceleration_data.csv</code> – subsampled acceleration dataset used for training the AI model</li> <li><code>notes.md</code> – description of activities performed and notes from data collection</li> <li><code>videos</code> – reference videos for performed activities</li> </ul> Authors <ul> <li><a href="https://orcid.org/0000-0002-2543-9461">Piotr Sowiński</a> – research methodology, data collection and processing</li> <li><a href="https://orcid.org/0000-0003-3217-1050">Monika Kobus</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0003-4295-3005">Anna Dąbrowska</a> – research methodology, methodological supervision</li> <li><a href="https://orcid.org/0000-0003-1524-7877">Kajetan Rachwał</a> – data collection</li> <li><a href="https://orcid.org/0000-0002-7109-891X">Karolina Bogacka</a> – research methodology</li> <li><a href="https://orcid.org/0000-0002-9572-2705">Krzysztof Baszczyński</a> – research methodology, data collection</li> <li><a href="https://orcid.org/0000-0002-3080-0303">Anastasiya Danilenka</a> – research methodology, data collection and processing</li> </ul> Acknowledgements This work is part of the <a href="https://assist-iot.eu/">ASSIST-IoT project</a> that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258. The <a href="https://www.ciop.pl/en">Central Institute for Labour Protection – National Research Institute</a> provided facilities and equipment for data collection. License The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.

Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography

Introduction The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces. Data Description Data and detector models were generated using GEANT4. The simulations include: <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> Contents This record includes: <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> Disclaimer The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.

Fig. 3 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 3. Phylogenetic tree based on D1/D2 LSU rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.

Fig. 5. A in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 5. A comparison of the line drawings in Stein (1883) and micrographs of Tripos eugrammus (A) and Dinophysis tripos (B). The arrows point the Stein's drawing style with acuter appendices than in the real morphology of the cells.

Fig. 2 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 2. Phylogenetic tree based on SSU rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.

Fig. 4 in Detection of Prorocentrum shikokuense in the Mediterranean Sea and evidence that P. dentatum, P. obtusidens and P. shikokuense are three different species (Prorocentrales, Dinophyceae)

Fig. 4. Phylogenetic tree based on ITS rRNA gene sequences, showing the position of the sequence of Prorocentrum shikokuense by Maximum Likelihood (ML). Numbers near branches denote ML bootstrap (BS) and Bayesian posterior probability (PP) values. BS and PP values <70 and 0.8, respectively, are not shown.