Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,302
datasets available to search
ShareScore release 0.9.0
Dataset results
9,302 results for “DETECT”
Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3
<p>Light curves and spectra of SPECULOOS-3 used in the research work presented in the Nature Astronomy article "Detection of an Earth-sized exoplanet orbiting the nearby ultracool dwarf star SPECULOOS-3"</p>
Annotated Dataset for Bilingual Code-Mixed English-Malay Sentiment Analysis and Sarcasm Detection in Public Security Domain
<p>Tweets from X, and post with comment from TikTok was acquired <span>from 11 September until 21 September 2022</span>. Data from both platforms was merged and selected. Three annotators manually labelling the selected data for sentiment and sarcasm. Sentiment labels are ‘positive’, ‘negative’, and ‘neutral’. Sarcasm label is ‘sarcastic’ and ‘not sarcastic’. Majority voting is considered for each label. Language identification label produced for each data. </p>
Dataset for: Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, Linbing Wang, and Imad L. Al-Qadi. "Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning." Automation in Construction 161 (2024): 105355. https://doi.org/10.1016/j.autcon.2024.105355.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(25IRT) images: this folder includes fusion images (25% infrared + 75% visible)</li> <li>04-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>05-Fusion(75IRT) images: this folder includes fusion images (75% infrared + 25% visible)</li> <li>06-Annotations: this folder includes annotations (xml files) based on PASCAL VOC (PASCAL Visual Object Classes Challenge) styles.</li> </ul>
Magnetic Hair Tactile Sensor for Directional Pressure Detection
<p><span>Tactile sensing in the human body is achieved via the skin. This has inspired the fabrication of synthetic skins with pressure sensors for potential applications in robotics, bio-medicine, and human-machine interfaces. Tactile sensors based on magnetic elements are promising as they provide high sensitivity and a wide dynamic range. However, current magnetic tactile sensors mostly detect pressures of solid objects and operate at relatively high forces about 100 mN. Here, we address these limitations by manufacturing soft, stretchable, and hair-like structures that are permanently magnetized to achieve high-resolution, cost-effective, and high-resolution pressure sensing. Combining these hair-like structures with advances in 3D magnetic-field measurements allows us to monitor directional tactile pressures without solid contact. To prove the concept of this technology, we built a bio-inspired soft device with a hairy structure that senses and reports environmental mechanical stresses, similar to that of human skin. Simple self-assembly of the soft magnetic hair structure makes our approach easy to scale for large-area applications.</span></p>
Investigating the Effects of Laser Wavelengths and Other Ablation Parameters on the Detection of Biogenic Elements and Contaminants in Hydroxyapatite - JAAS - Dataset
<p>The dataset provided contains measurements for an article in The Journal of Analytical Atomic Spectrometry (JAAS). Further details are available in the .txt file included in the folder.</p>
Figure 1 in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 1. Examples of images collected for soybean in the VE-VC (A) and R2 (B) growth stages.
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
<p>This dataset consists of 615 images of <em>Scaphoideus titanus</em> (ST) and <em>Orientus ishidae</em> (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods:</p> <ul> <li>Photos from the field;</li> <li>Images of stored YST (T = 5±1°C) and reared insects within a controlled greenhouse environment;</li> <li>Digital scans of YST collected during regular monitoring activities in the fields;</li> <li>Photos from a smart trap prototype installed in our experimental vineyard.</li> </ul> Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. <table><tbody> <tr> <td>Image source</td> <td>Number of images</td> <td>ST annotations</td> <td>OI annotations</td> <td>Number of background images</td> </tr> <tr> <td> <p>Field</p> </td> <td>18</td> <td>3</td> <td>101</td> <td>8</td> </tr> <tr> <td>Laboratory</td> <td>157</td> <td>473</td> <td>863</td> <td>8</td> </tr> <tr> <td>scanned</td> <td>390</td> <td>853</td> <td>542</td> <td>84</td> </tr> <tr> <td>smart-trap</td> <td>50</td> <td>0</td> <td>0</td> <td>50</td> </tr> </tbody> </table> <p>We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing on <em>brightness & contrast</em>, <em>sharpness</em>, and a combination of both. Finally the annotations exported in YOLO format.</p> <h3>Dataset structure</h3> <ol> <li><em>crop/</em></li> <li><em>bright/</em></li> <li><em>sharp/</em></li> <li><em>bright_and_sharp/</em> </li> <li><em>labels/</em></li> </ol> <p>At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission).</p> <h3>Deployment</h3> <p>You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our <a href="https://github.com/checolag/insect-detection-scripts/tree/main">Github</a> repository.</p>
Strong lensing of tidal disruption events: Detection rates in imaging surveys
<p>The unlensed and strongly lensed tidal disruption event (TDE) catalogs at the fiducial temperature of T = 20,000 K for the L1 and L2 luminosity models separately. The L1 luminosity model is an observationally driven upper limit defined by 1% of stellar fallback material (produced in the disruption of a 0.1 Msun main-sequence star) turning into radiation. The L2 luminosity model is defined by self-crossing shocks of stellar debris. Each line of a file represents a TDE (unlensed or lensed), so to compute the TDE detection rate one would need to iterate through the entire file, sum over the TDE weighting factors, and apply magnitude cuts according to the desired survey limit. For the lensed catalogs, further normalization by any oversampling factors is needed. These catalogs assume a survey area of 20,000 deg^2; the observational limiting magnitude is 27.0 (AB) for each band, so one can choose a brighter limiting survey magnitude with mag (AB) < 27.0. </p>
ICESat-2 Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: Coastal Alaska
<p>This dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, <a href="https://nsidc.org/data/atl03/versions/6" target="_blank" rel="noopener">Neumann et al., 2023</a>), release 006, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, <a href="https://doi.org/10.1029/2022GL100272" target="_blank" rel="noopener">Duncan & Farrell, 2022</a>). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.</p> <p><strong>Data Bounds (lon,lat): </strong></p> <p><em>LL corner: (-169.71, 68.32), UR corner: (-137.1, 72.18)</em></p> <p> </p> <p><strong>File Format</strong></p> <p>Coastal Alaska data are provided in .xz compressed format and decompress to ascii text. The columns are as follows:</p> <ol> <li>Longitude (degrees)</li> <li>Latitude (degrees)</li> <li>Time (seconds since 2018-01-01)</li> <li>Elevation (meters, relative to DTU18 Mean Sea Surface)</li> </ol> <p>Data are provided monthly for the period December 2021 (202112) to May 2022 (202205).</p> <p> </p> <p><strong>Users of this dataset are asked to cite the following publication:</strong></p> <p><em>Duncan, K. and Farrell, S. L. (2022). Determining Variability in Arctic Sea Ice Pressure Ridge Topography with ICESat-2. Geophys. Res. Lett., 49, e2022GL100272. https://doi.org/10.1029/2022GL100272</em></p> <p> </p> <p><em>This dataset is supported by NASA Cryosphere Grant 80NSSC20K0966</em></p> <p> </p> <p>For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu</p>
Dataset for paper Spotting the Hook: Leveraging Domain Data for Advanced Phishing Detection
<p>The dataset contains DNS records, IP-related features, WHOIS/RDAP information, information from TLS certificate fields, and GeoIP information for 432,572 benign domains from Cisco Umbrella and 68,353 phishing domains from PhishTank and OpenPhish services. The ground truth for the phishing dataset was double-check with the VirusTotal (VT) service. Domain names not considered as phishing by VT have been removed. The data was collected between March and November 2023.The final assessment of the data was conducted in December 2023.</p> <p>The dataset is useful for statistical analysis of domain data or feature extraction for training machine learning-based classifiers, e.g. for phishing detection. </p> <h1>Data Files</h1> <p>The data is located in two individual files:</p> <ul> <li><strong>benign.json</strong> - data for 432,572 benign domains, and</li> <li><strong>phishing.json</strong> - data for 68,353 phishing domains.</li> </ul> <h1>Data Structure</h1> <div> <p>Both files contain a JSON array of records generated using mongoexport. The following table documents the structure of a record. Please note that:</p> <ul> <li>some fields may be missing (they should be interpreted as nulls), </li> <li>extra fields may be present (they should be ignored), </li> <li>due to a processing error, the common_name field of the certificate objects always contains trailing symbols: <code>‘></code> .</li> </ul> </div> <table> <tbody> <tr> <td> <div> <div> <p><strong>Field name </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Field type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Nullable </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>domain_name </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The evaluated domain name </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>url </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The source URL for the domain name </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>evaluated_on </p> </div> </div> </td> <td> <div> <div> <p>Date </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Date of last collection attempt </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>source </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>An identifier of the source </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>sourced_on </p> </div> </div> </td> <td> <div> <div> <p>Date </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Date of ingestion of the domain name </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>dns </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Data from DNS scan </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>rdap </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Data from RDAP or WHOIS </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>tls </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Data from TLS handshake </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ip_data </p> </div> </div> </td> <td> <div> <div> <p>Array of Objects </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Array of data objects capturing the IP addresses related to the domain name </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p><strong>DNS data (dns field) </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>A </p> </div> </div> </td> <td> <div> <div> <p>Array of Strings </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of IPv4 addresses </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>AAAA </p> </div> </div> </td> <td> <div> <div> <p>Array of Strings </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of IPv6 addresses </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>TXT </p> </div> </div> </td> <td> <div> <div> <p>Array of Strings </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of raw TXT values </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>CNAME </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The CNAME target and related IPs </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>MX </p> </div> </div> </td> <td> <div> <div> <p>Array of Objects </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of objects with the MX target hostname, priority and related IPs </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>NS </p> </div> </div> </td> <td> <div> <div> <p>Array of Objects </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of objects with the NS target hostname and related IPs </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>SOA </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>All the SOA fields, present if found at the target domain name </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>zone_SOA </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The SOA fields of the target’s zone (closest point of delegation), present if found and not a record in the target domain directly </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>dnssec </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Flags describing the DNSSEC validation result for each record type </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ttls </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The TTL values for each record type </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>remarks </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The zone domain name and DNSSEC flags </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p><strong>RDAP data (rdap field) </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>copyright_notice </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>RDAP/WHOIS data usage copyright notice </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>dnssec </p> </div> </div> </td> <td> <div> <div> <p>Bool </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>DNSSEC presence flag </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>entitites </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>An object with various arrays representing the found related entity types (e.g. abuse, admin, registrant). The arrays contain objects describing the individual entities. </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>expiration_date </p> </div> </div> </td> <td> <div> <div> <p>Date </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>The current date of expiration </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>handle </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>RDAP handle </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>last_changed_date </p> </div> </div> </td> <td> <div> <div> <p>Date </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>The date when the domain was last changed </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>name </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The target domain name for which the data in this object are stored </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>nameservers </p> </div> </div> </td> <td> <div> <div> <p>Array of Strings </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Nameserver hostnames provided by RDAP or WHOIS </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>registration_date </p> </div> </div> </td> <td> <div> <div> <p>Date </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>First registration date </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>status </p> </div> </div> </td> <td> <div> <div> <p>Array of Strings </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The state of the registered object [TODO] </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>terms_of_service_url </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>URL of the RDAP usage ToS </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>url </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>URL of the RDAP entity </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>whois_server </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>WHOIS server address </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p><strong>TLS data (tls field) </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>cipher </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>TLS cipher suite description according to [TODO] </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>protocol </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>One of “TLS”, ”TLSv1.2”, ”TLSv1.3” </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>certificates </p> </div> </div> </td> <td> <div> <div> <p>Array of Objects </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Array of objects representing the certificate chain, the first element is the root certificate </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p><strong>IP data (elements in the ip_data array) </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>ip </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The IP address </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_record </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>The type of the DNS record the address was captured from </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>remarks </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>No </p> </div> </div> </td> <td> <div> <div> <p>Ping round-trip time, “is alive” flag and rdap/geo/asn evaluation dates </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>rdap </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>RDAP data, similar to DNS RDAP, see the JSON Schema for details </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geo </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Geolocation data from the GeoLite2 City database (e.g. latitude, longitude, city, country, etc.) </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>asn </p> </div> </div> </td> <td> <div> <div> <p>Object </p> </div> </div> </td> <td> <div> <div> <p>Yes </p> </div> </div> </td> <td> <div> <div> <p>Autonomous system data from the GeoLite2 ASN database (ASN, organization, network) </p> </div> </div> </td> </tr> </tbody> </table> <h1>Acknowledgements</h1> <p>We would like to thank the OpenPhish Team for grating permission to use and publish their dataset. We also thank VirusTotal for providing us access to the API for research purposes. The research has been supported by the <strong>Flow-based Encrypted Traffic Analysis</strong> project, no. VJ02010024, granted by the Ministry of the Interior of the Czech Republic and the <strong>Smart Information Technology for a Resilient Society </strong>project, no. FIT-S-23-8209, granted by Brno University of Technology.</p>
Optimizing the detection of biological signals through a semi-automated feature selection tool
<p>This repository contains the .xml files generated by the MZmine (Schmid et al., 2023) software version 2.53 during the preprocessing of the raw data (.raw) according to the recommendations of the 2007 Metabolomics Standards Initiative (Summer et al., 2007).</p> <p> </p> <p>Schmid R et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol. 2023 Apr;41(4):447-449.</p> <p>Sumner et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics. 2007 Sep;3(3):211-221. </p>
Application of CoLD-CoP to Detecting Competitively and Cooperatively Binding Ligands
<p>CompetitiveCooperative_CoLD_CoP_for_archiving.zip will unzip into a directory with two sub-directories. The directory LysozymeNAG has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + lysozyme sample and a ligands + lysozyme + NAG (N-Acetyl-Glucosamine) sample. This directory also has the key scripts used for DOSY processing and analysis of this dataset. The directory TyrosinaseHCCA similarly has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + (mushroom) tyrosinase sample and a ligands + tyrosinase + HCCA (4-Hydroxy-a-Cyano-Cinnamic Acid) sample. The TyrosinaseHCCA directory has not only the key scripts used for DOSY processing and analysis of this dataset, but it also has 1D proton spectra acquired on each solution used in this arm of the project. </p> <p>The file das_dosy_nm.m is a customized version of the DOSY processing file used in DOSY processing instead of the dosy_nm.m file used in the DOSY Toolbox. Use of the scripts found in the directories described in the above paragraph requires das_dosy_nm.m as well as NMRPipe, the DOSY Toolbox (https://nmr.chemistry.manchester.ac.uk/?q=node/8), CoLD-CoP toolbox and Covariance Toolbox (both available via MATLAB Central File Exchange).</p> <p>The file lysozyme_complexes_for_docking.zip contains lysozyme structures as minimized in various complexes as well as the ligands from those complexes. These can be used for rescoring or further re-docking, e.g., in FastDRH. DockingAndEnergyMinimization.zip contains some of the files used for docking and energy minimization of the Lysozyme-NAG, Lysozyme-Tris and Lysozyme-GlcNAC complexes. </p>
Detection of carbon dioxide and hydrogen peroxide on the stratified surface of Charon with JWST
<p>This repository houses the JWST/NIRSpec grand-average spectrum of Charon, along with infrared absorption spectra that detail the effects of 5 keV electron irradiation on CO2 diluted in H2O ice films.</p>
Data from: The heat is on: reduced detection of floral scents after heat waves in bumblebees
<p>Global climate change disrupts key ecological processes and biotic interactions. The recent increase in heat wave frequency and severity prompts the evaluation of physiological processes that ensure the maintenance of vital ecosystem services such as pollination. We use experimental heat waves to determine how high temperatures affect the bumblebees' ability to detect floral scents. Heat waves induced strong reductions in antennal responses to floral scents in both tested bumblebee species (<em>Bombus terrestris and Bombus pascuorum</em>). These reductions were generally stronger in workers than in males. Bumblebees showed no consistent pattern of recovery 24 hours after heat events. Our results suggest that the projected increased frequency and severity of heat waves may jeopardize bumblebee-mediated pollination services by disrupting the chemical communication between plants and pollinators. The reduced chemosensitivity can decrease the bumblebees' abilities to locate food sources and lead to declines in colonies and populations.</p>
Fig.1 in Application Of Near-Infrared Reflectance Spectroscopy For Detection Of Acrylamide Content In Potato Crisps And Bread
Fig.1. Predicted acrylamide vs reference acrylamide in bread samples.
Fig.2 in Application Of Near-Infrared Reflectance Spectroscopy For Detection Of Acrylamide Content In Potato Crisps And Bread
Fig.2. Predicted acrylamide vs reference acrylamide in potato crisp samples.
Strong toxic gas detection on Penta-BCP monolayers: insights from DFT calculations
<p>Rapid and accurate detection of toxic gases remains important for industrial safety and environmental well-being. This study examines the potential of <em>penta</em>-BCP monolayer as a high-performance toxic gas sensor utilizing the robust framework of density functional theory (DFT). <em>Penta</em>-BCP exhibits strong and selective interactions with specific toxic gas molecules, including CO, NO, and NO<sub>2</sub>. This results in the remarkable ability to readily capture these harmful gases while demonstrating minimal interaction with non-toxic counterparts such as H<sub>2</sub>, N<sub>2</sub>, CO<sub>2</sub>, and CH<sub>4</sub>, underlining its exceptional selectivity. Furthermore, <em>penta</em>-BCP has a significantly shorter recovery time for NO and NO<sub>2</sub> gases compared to <em>penta</em>-BCN, which has a similar structure. These findings collectively position <em>penta</em>-BCP as a frontrunner for the next generation of toxic gas sensors, paving the way for enhanced environmental monitoring and improved public health protection.</p>
Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024
<p>Data pertaining to the published article "Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI" by Donatelli et al., 2024. <a href="https://doi.org/10.1111/jon.13201">https://doi.org/10.1111/jon.13201</a></p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
Sound database of Industrial Machine for Audio Anomaly Detection
<p><span>Audio anomaly detection(AAD) can seamlessly determinefaults in industrial machines and improve the efficiency of predictive maintenance systems. However, the unavailability of audio sound recordings of real industrial machines operating in their actual industrial setup has limited the efficacy of detection systems. Many different audio databases exist having collections of sounds from dummy (or real) systems operating in controlled environments but a collection of audio sounds from actual industrial machines is missing. Therefore, audio sound recordings of an Air compressor machine working in its natural industrial environment are presented. Only real sounds of an actual machine are captured. Synthetic mixing of sounds is avoided. Damaging the machine to create an anomalous state is avoided. Yet fourteen different unhealthy states are identified and their audio recordings are presented. Dataset with varied values of SNRs is also presented. Spectrograms are plotted and spectral shape parameter values of the developed corpus are calculated. The findings demonstrate the divergence in the developed database and its usefulness in building an effective AAD system for a real industrial machine.</span> </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.