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MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish
<p><strong>UPDATE 27/09/2022: </strong>A complete normalization of all mentions in the corpus to SNOMED CT has been added to the 'meddoprof-norm.tsv' file.</p> <p><strong>Description</strong></p> <p>This repository contains the complete MEDDOPROF Gold Standard, a collection of 1,844 clinical cases in Spanish with annotations for occupations, working statuses and activities. MEDDOPROF is a Shared Task celebrated in 2021 that explores the application of natural language processing to occupational health. If you'd like to learn more, please visit: <a href="https://temu.bsc.es/meddoprof">https://temu.bsc.es/meddoprof</a>.</p> <p><strong>Folder and File Structure</strong></p> <p>The corpus' files are presented in the format used by the annotation tool brat. That is, for each clinical case there is a .txt file with the text and a .ann file with its corresponding annotations.</p> <p><em>- meddoprof-ner/</em></p> <p>Clinical cases annotated with these labels: PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD).</p> <p><em>- meddoprof-class/</em></p> <p>Clinical cases with the same annotations as 'meddoprof-ner' but with these labels instead: PACIENTE (patient), FAMILIAR (family member), SANITARIO (health professional) or OTRO (other).</p> <p><em>- ner_class_joint/</em></p> <p>Clinical cases with both levels of annotation (ner and class) joint (that is, a mention classified as as PROFESOR in meddoprof-ner and as PACIENTE in meddoprof-class would be PROFESION-PACIENTE here).</p> <p><em>- meddoprof-norm.tsv</em></p> <p>Tab-separated file (.tsv) with the mapping of each mention in the corpus to ESCO and SNOMED CT. The file has five columns: filename, mention text, span, ESCO code and SNOMED code.</p> <p>Additionally, two files with the filenames of the train and test partitions are included.</p> <p> </p> <p><strong>Please cite if you use this resource:</strong></p> <p>Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021.</p> <pre><code>@article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} }</code></pre> <p><strong>Related Resources:</strong></p> <p>- <a href="http://temu.bsc.es/meddoprof">Web</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4694768">Training Data</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4889776">Test set</a></p> <p>- <a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> <p>- <a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> <p>- <a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> <p> </p> <blockquote> <p>MEDDOPROF is part of the IberLEF 2021 workshop, which is co-located with the SEPLN 2021 conference. For further information, please visit <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a> or email us at encargo-pln-life@bsc.es</p> <p>MEDDOPROF is promoted by the Plan de Impulso de las Tecnologías del Lenguaje de la Agenda Digital (Plan TL) and the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4PROFHEALTH - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> </blockquote>
Data from: Development and validation of targeted environmental DNA (eDNA) metabarcoding for early detection of 69 invasive fishes and aquatic invertebrates
<p>Invasive species are of concern due to their impacts on ecosystems and economies, but they pose significant control challenges. Environmental DNA (eDNA) is a powerful tool in the detection of aquatic organisms at low densities due to high sensitivity and ease of collection. Aquatic eDNA analyses have increased worldwide and are generally either applied to a few target species (quantitative PCR) or for broad taxonomic applications (metabarcoding). Here we describe the development and testing of a hybrid approach that utilized high sensitivity PCR primer sets and high-throughput sequencing (HTS), referred to as <em>targeted metabarcoding</em>, to detect 69 fishes and invertebrates. We identified target species based on reports of globally important invasive species and developed two independent PCR primers for each species (CO1 and a second mtDNA region). We assessed sensitivity and eDNA interference for all 138 primers (2 per species, 69 species) using standard end-point PCR and tested them on 10 eDNA samples spiked with various amounts of one or more of the target species' DNA. The sensitivity of the 138 primer sets ranged between 1.5×10<sup>-5</sup> and 2.64 ng template DNA (mean = 0.069 ng). Primers were also tested for interference effects using plankton eDNA to simulate field conditions. The inclusion of interfering plankton DNA reduced the sensitivity for most primer sets by one or more orders of magnitude (range 0 to 3). Overall, our targeted metabarcoding resulted in the detection of ~ 98% of species in the DNA spiked samples, and, perhaps more importantly, the HTS read count was positively related to the quantity of spiked DNA (P < 0.002). We envision this technique being particularly useful for the early detection of species at low population densities; however, there are diverse applications of targeted metabarcoding for monitoring aquatic community composition and quantifying ecosystem change and health.</p>
Fine-scale subpopulation detection via SNP-based unsupervised method: A case study on the 1000 Genomes Project Resources
<p>Here are the supplementary files to the paper "Fine-scale subpopulation detection via SNP-based unsupervised method:<br> A case study on the 1000 Genomes Project Resources".<br> <br> The repository is organized as:</p> <ol> <li><strong>Supplementary information</strong>: the additional detailed information for the experiments in the paper <ul> <li>Supplementary_information_IPCAPS_Chaichoompu_v1.pdf</li> </ul> </li> <li><strong>Real-life dataset</strong>: the 1000 genome dataset, which is referred to in the paper and is filtered with the parameters explained in the paper. Reference: https://www.internationalgenome.org/ <ul> <li>1000genomes_with_filtering.zip</li> </ul> </li> </ol>
BIOMETSCO progress presentation 2022: Identification of Novel Biomarkers and Drug Targets for the Detection and Elimination of Occult Metastases in Colon Cancer
<p>This is a recorded talk with head of the BIOMETSCO project - Lasse Sommer Kristensen - where he presents the latest project progress.</p> <p>The talk was given at one of ODIN's (the Open Discovery Innovation Network) Knowledge Sharing Events in May 2022.</p> <p> </p> <p> </p>
WeChat Mini-App Consistency Detection Dataset
<p>A dataset for WeChat mini-App consistency detection.</p> <p>Paper(posting): Do as You Say: Consistency Detection of Data Practice in Program Code and Privacy Policy in Mini-App</p>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </p>
Clear Ionospheric Detections of Infrasound produced by Conventional Surface Explosives
<p>These HDF5 files contain complex time series data from HF receptions of a Digisonde Portable Sounder 4D (DPS4D). Each data point is the phase and amplitude of a decoded Sky Map mode pulse. </p>
Detective Conan Gacha Box/Muscrat Skull
"The Criminal" Case Closed/Detective Conan Gacha Box with a muscrat skull balanced atop it Source: Objaverse 1.0 / Sketchfab
Automated Performance Regression Detection in Microservice Architectures - Raw Measurements
<p>These measurements contain a ZIP folder in which influxdb dumps of the several load testing runs of the corresponding bachelor's thesis "Automated Performance Regression Detection in Microservice Architectures" can be found. The dumps are named by date. The two dumps which are additionally named, contain the data sets which were used for the final evaluation.</p>
Ligands detected by CheckMyBlob in the Protein Data Bank
<p>A dataset containing numerical descriptions all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017. It is the "master" data set containing all ligands queried and detected as described in the Kowiel et al. paper "Automatic recognition of ligands in electron density by machine learning methods".</p> <p>The file is compressed using 7zip to allow for faster downloads. The compressed file weighs around 1.1 GB, whereas the uncompressed CSV will take close to 3.0 GB of disk space. The all_summary.csv file can be used to reproduce the filtered ligand data sets (CMB, TAMC, CL) described in the Kowiel et al. paper. Additionally, the data set can be used as a source to create data sets based on other filtering criteria (e.g. ligand subsets of your choice), or on its own a as source of knowledge about all ligands that CheckMyBlob was capable of detecting automatically on the entire PDB as of May 1st, 2017.</p> <p>For machine learning applications, please read the code at https://github.com/dabrze/CheckMyBlob, to see examples describing how to preprocess the data. In particular, the following attributes should not be used during model training or testing: "pdb_code", "res_id", "chain_id", "local_res_atom_count", "local_res_atom_non_h_count", "local_res_atom_non_h_occupancy_sum", "local_res_atom_non_h_electron_sum", "local_res_atom_non_h_electron_occupancy_sum", "local_res_atom_C_count", "local_res_atom_N_count", "local_res_atom_O_count", "local_res_atom_S_count", "dict_atom_non_h_count", "dict_atom_non_h_electron_sum", "dict_atom_C_count", "dict_atom_N_count", "dict_atom_O_count", "dict_atom_S_count", "fo_col", "fc_col", "weight_col", "grid_space", "solvent_radius", "solvent_opening_radius", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step", "local_volume", "res_coverage", "blob_coverage", "blob_volume_coverage", "blob_volume_coverage_second", "res_volume_coverage", "res_volume_coverage_second", "skeleton_data", "resolution_max_limit", "part_step_FoFc_std_min", "part_step_FoFc_std_max", "part_step_FoFc_std_step".</p> <p>The target attribute for classification is: <strong>res_name</strong>.</p>
Mean Birds: Detecting Aggression and Bullying on Twitter
<p>In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.</p>
Comparing mail-in self-collected specimens sent via United States Postal Service versus clinic-collected specimens for the detection of Chlamydia trachomatis and Neisseria gonorrhoeae in extra-genital sites data set
<p>This data set was used to evaluate the concordance between clinic-collected extra-genital specimens and self-collected mailed-in extra-genital specimens among participants seeking sexually transmitted infection testing at a free clinic in Hollywood, CA. The newest version of the file reflects sample adequacy control (SAC) cycle threshold values for each participant.</p>
Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset
<div> <div> <p><strong>Introduction:</strong></p> <p>The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions</p> <p><strong>Captures:</strong></p> <p>The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.</p> <p><strong>Dataset:</strong></p> <p>The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.</p> <p><strong>Zip Folder:</strong></p> <p>The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.</p> <p><strong> Article Information:</strong></p> <p>The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.</p> <blockquote> <p>Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). <em>Farm-flow dataset: Intrusion detection in smart agriculture based on network flows</em>, Computers and Electrical Engineering, Volume 121, 109892, DOI: <a href="https://doi.org/10.1016/j.compeleceng.2024.109892." target="_blank" rel="noopener"> 10.1016/j.compeleceng.2024.109892</a></p> </blockquote> </div> </div>
Data and Code: Detecting Blue Whale Calls in the Northeast Pacific Using Seismic Systems (Undergraduate Thesis)
<p><strong>SeismoData.ipynb</strong>: This Jupyter notebook is adapted from seismosocialdistancing.ipynb created by Thomas Lecocq, Fred Massin and Claudio Satriano. SeismoData.ipynb was used to retrieve seismic waveform data from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC) (https://ds.iris.edu/ds/ nodes/dmc/) and convert files from miniSEED to SAC format. It was also used to preview waveform and spectrogram plots.</p> <p><strong>BlueWhaleDetectionResults_J53A_Dec132011.mat</strong>: This .mat file summarizes whale detection results from OBS J53A on December 13th 2011. The objective was to calibrate a detection algoritm created for Northwest Atlantic blue whale A calls by Plourde and Nedimovic (2022), so that it can target Northeast Pacific blue whale B calls using seismometers off Washington and California. Three tests were performed to find optimal parameters. <em>BlueWhaleDetections_J53A_Test1</em><strong> </strong>are the detection results of a control that uses Northwest Atlantic blue whale parameters (16.25-18Hz frequency and 68-78s period ranges). <em>BlueWhaleDetections_J53A_Test2</em> <strong> </strong>are the detection results using Northeast Pacific blue whale B call parameters (14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_Test2 </em>are the detection results using Northeast Pacific blue whale B call and C call parameters (10.5-12Hz and 14-17Hz frequency and 45-55s period ranges). <em>BlueWhaleDetections_J53A_SCC</em> are the 95% probability detection results from the Wilcock and Hilmo (2021) blue whale catalogue created using spectrogram cross-correlation. </p> <p><strong>NEPBlueWhaleMATLABcodes.zip</strong>: Contains scripts to run the recurrence interval power ratio method created by Plourde and Nedimovic (2022), adjusted to detect Northeast Pacific blue whale B calls and plot waveforms/spectrograms. First run <em>DetectBlueWhales.m </em>to calculate the recurrence power ratio every 12 minutes, then run C<em>reateBlueWhaleDetectionList.m </em>to classify detections with high power ratios and likely blue whale call detections.</p> <p><strong>BlueWhaleDetectionResults_CapeMendocino_Dec15to292014.mat</strong>: This .mat file summarizes the blue whale detection results from 2 OBS (FS02D and FS07D) and 1 land seismometer (CM09A) in close proximity, using Test 2 parameters. Note if there are less than 3 BWD in a given day, these are likely false detections.</p>
Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections
<p><em><span>Data Source</span></em></p> <p>This dataset is derived from the <em><span>NCEP-NCAR Reanalysis 1</span> data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a></em>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.</p> <p> </p>
Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)
Bias detection data
<p>The dataset includes 30 interview questions to cover a variety of biases that are frequently present in interview settings and a set of 15 sampling methods that potentially yield either biased or unbiased samples.</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Datasets for "A superconducting dual-rail cavity qubit with erasure-detected logical measurements"
<p>Title of Dataset: Demonstrating a superconducting dual-rail cavity qubit with erasure-detected logical measurements<br>---</p> <p>Included are data shown in Figures 1-4 of the main text and Extended Data Figures 2 and 3 in the Methods.<br>Data was collected by running quantum programs on hardware deployed at Quantum Circuits, Inc. The results of these experiments are decoded and assigned an appropriate label (discussed below and in the manuscript). We provide various levels of processing: number of counts, fraction of counts, and the logical assignment. Full labeled shot-by-shot outcomes can be provided upon request.</p> <p>## Description of the data and file structure</p> <p>Description of column labels<br>- xs: sweep variable (if applicable)<br>- Counts:<br> - 00_counts, 01_counts, 10_counts, 11_counts: Number of runs with outcome labeled "00", "01", "10", "11", respectively<br> - A_counts: Number of runs with outcome labeled as ambiguous outcome<br> - FSP_counts: Number of runs with outcome labeled as a failed state preparation<br> - all_shots: Total number of runs<br> - total_counts: Number of runs with a successful state preparation (e.g. all_shots - B_counts)<br>- Outcome fraction<br> - 00, 01, 10, 11, A, FSP: Fraction of counts normalized by total_counts<br> - 00_err, 01_err, 10_err, 11_err, A_err, FSP_err: Standard error for all above<br>- Logical outcomes<br> - 0L, 1L, erasures, Z_L: Computed logical dual-rail outcome for "0_L", "1_L", erasures, and expectation value of logical sigma_z <Z>, respectively<br> - 0L_err, 1L_err, erasures_err, Z_L_err: Standard error for all above</p> <p>State assignment datasets:<br>- Fig2_state_assignment_1msmts.csv: Data for Figure 2<br>- ExtendedDataFig2_state_assignment_2msmts.csv: Data for Extended Data Figure 2</p> <p>Bit-flip datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in Figure 3A<br>- Logical outcomes are plotted in Figure 3B<br>- Detail on datasets:<br> - Fig3_bit_flip_0L_1ms.csv: Data for Figure 3, left panel<br> - Fig3_bit_flip_0L_20us.csv: Data for Figure 3, left panel inset<br> - Fig3_bit_flip_1L_1ms.csv: Data for Figure 3, right panel<br> - Fig3_bit_flip_1L_20us.csv: Data for Figure 3, right panel inset</p> <p>Phase error datasets: <br>- Each row corresponds to a different delay specified in the xs column in units of microseconds<br>- Counts and Outcome fraction are plotted in the top panels for Figure 4A and 4B<br>- Logical outcomes are plotted in the bottom panel for Figure 4A and 4B<br>- Short time data are shown in the inset of bottom panel for Figure 4A and 4B. <br>- We provide additional detail for the short time data in Extended Data Figure 3 for short-time Ramsey<br>- Detail on ramsey_short_time.csv: Data includes an additional dimension where the Ramsey phase angle is swept<br> - values in radians are enumerated in ExtendedDataFig3_ramsey_short_time_phases.csv<br>- Detail on datasets:<br> - Fig4_ramsey_long_time.csv: Data for Figure 4A<br> - ExtendedDataFig3_ramsey_short_time.csv, ExtendedDataFig3_ramsey_short_time_phases.csv: Data for Figure 4A, inset (bottom panel); Data for Extended Data Figure 3<br> - Fig4_echo_long_time.csv: Data for Figure 4B<br> - Fig4_echo_short_time.csv: Data for Figure 4B, inset (bottom panel)</p>
Data for: Environmental DNA storage and extraction method affects detectability for multiple aquatic invasive species
<p>Environmental DNA (eDNA) refers to genetic material released by organisms into their surrounding environment. Collecting and identifying eDNA has gained popularity for monitoring and surveillance of aquatic invasive species. Invasive species management is most successful when an invasion is identified early while population size is likely to be low, highlighting the importance of eDNA detection sensitivity. Various factors influence DNA yield recovered from environmental samples. Environmental DNA storage and extraction methods, for example, can be adjusted to maximize DNA yield, thereby improving detectability. In this study, we compared the performance of two eDNA storage and extraction methods in detecting three common aquatic invasive species (<em>Bythotrephes longimanus</em>, <em>Dreissena polymorpha</em>, and <em>Faxonius rusticus</em>) across five natural ecosystems of Minnesota, United States. One method involved storing filters in 95% ethanol (EtOH) and extracting DNA using a DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), whereas the other method used cetyl trimethylammonium bromide (CTAB) for storage and a phenol–chloroform–isoamyl (PCI) procedure for DNA extraction. We also investigated the effect of DNA extract volume (1 μL relative to 3 μL) in qPCR reactions on eDNA detections for the commercial kit method. The CTAB‐PCI method yielded significantly more positive detections, across all three species, compared to the EtOH‐Qiagen method. Moreover, we found that using 1 μL of DNA extract in qPCR reactions was equally effective as using 3 μL. To improve detections of aquatic invasive species, we recommend that researchers store eDNA sample filters in CTAB or a similar lysis buffer such as Longmire's solution and extract with PCI when feasible, but note that lower extract volumes might be used without negative effect when either increasing technical replicates or repurposing samples for the detection of multiple species.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.