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5,526 results for “information”
USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset
<p>USPTO-LLM is an <strong>information-enriched chemical reaction dataset</strong> that provides more side information (reaction conditions and reaction steps division) for developing new reaction prediction and retrosynthesis methods and inspires new problems, such as reaction condition prediction. It comprises over <strong>247K chemical reactions</strong> extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. </p> <p>We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. The extracted chemical reactions are organized as <strong>heterogeneous directed graphs</strong>, allowing us to formulate a series of prediction tasks, such as reaction prediction, retrosynthesis, and reaction condition prediction, in a unified graph-filling framework.</p>
A Dataset of Information (DNS, IP, WHOIS/RDAP, TLS, GeoIP) for a Large Corpus of Benign, Phishing, and Malware Domain Names 2024
<p>The dataset contains DNS records, IP-related features, WHOIS/RDAP information, information from TLS handshakes and certificates, and GeoIP information for 368,956 benign domains from Cisco Umbrella, 461,338 benign domains from the actual CESNET network traffic, 164,425 phishing domains from PhishTank and OpenPhish services, and 100,809 malware domains from various sources like ThreatFox, The Firebog, MISP threat intelligence platform, and other sources. The ground truth for the phishing dataset was double-check with the VirusTotal (VT) service. Domain names not considered malicious by VT have been removed from phishing and malware datasets. Similarly, benign domain names that were considered risky by VT have been removed from the benign datasets. The data was collected between March 2023 and July 2024. The final assessment of the data was conducted in August 2024.</p> <p>The dataset is useful for cybersecurity research, e.g. statistical analysis of domain data or feature extraction for training machine learning-based classifiers, e.g. for phishing and malware website detection.</p> <p>The dataset was created using software available in the associated GitHub repository <a href="https://github.com/nesfit/domainradar-dib" target="_blank" rel="noopener">nesfit/domainradar-dib</a>.</p> <h1>Data Files</h1> <ul> <li> <p>The data is located in the following individual files:</p> <ul> <li><strong>benign_umbrella.json</strong> - data for 368,956 benign domains from Cisco Umbrella,</li> <li><strong>benign_cesnet.json </strong>- data for 461,338 benign domains from the CESNET network,</li> <li><strong>phishing.json</strong> - data for 164,425 phishing domains, and</li> <li><strong>malware.json</strong> - data for 100,809 malware domains.</li> </ul> </li> <li>The <strong>schema.json</strong> file contains a JSON Schema with detailed description of the data entries.</li> </ul> <h1>Data Structure</h1> <div> <p>Both files contain a JSON array of records generated using mongoexport (in the MongoDB Extended JSON (v2) format in Relaxed Mode). 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> </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>malware_type</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 malware type/family or “unknown” (only present in malware.json) </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p><strong>DNS data (dns field) </strong></p> </div> </div> </td> <td> </td> <td> </td> <td> </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> <td> </td> <td> </td> <td> </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 (see RFC 7483, section 10.2.2)</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> <td> </td> <td> </td> <td> </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 <a href="https://www.iana.org/assignments/tls-parameters/tls-parameters.xhtml#tls-parameters-4" target="_blank" rel="noopener">IANA</a></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> <td> </td> <td> </td> <td> </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.</p> <p>This dataset includes GeoLite2 data created by MaxMind, available from <a href="https://www.maxmind.com" target="_blank" rel="noopener">https://www.maxmind.com</a>.</p> <p>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>
Paper information in the topic of large language models
<p>This dataset supports the findings in the preprint 'Academic collaboration on large language model studies increases overall but varies across disciplines.' The study aims to explore the application of large language models (LLMs) in scientific disciplines and their implications for interdisciplinary collaboration.</p> <p>To build LLM paper group, we start with a broad search using general terms related to LLMs and popular models based on the MMLU benchmark spanning from October 2018 to September 2024. We apply this search to the title and abstract to avoid excessive noise in the dataset and then undergo a series of filtering steps<br>to enhance relevance and remove duplicates. The resulting dataset contains 59,293 papers.</p> <p>In addition to the paper group in the topic of LLMs, we establish two control groups. The first control group focuses on machine learning (ML) papers. We select ML as a control because it is a well-established field from which LLM emerged as a subfield. To construct this group, we collect a random sampling of 70,945 papers containing the phrase ''machine learning'' in either their title or abstract. To provide an even broader perspective beyond AI-related fields, we create a second control group consisting of a random sample of 73,110 papers from all other research categories---specifically, papers that belong neither to the ML nor LLM categories. </p> <p>The three files below contain the cleaned samples collected from OpenAlex, which are derived from the original files. </p> <ul> <li>LLM: llm-cleaned-samples.csv</li> <li>ML: ml-cleaned-samples.csv</li> <li>Non-LLM/ML: non-llm-cleaned-samples.csv</li> </ul> <p>The three zip files below contain author affiliation information (including departmental discipline) extracted by GPT-4o-mini to support the departmental analysis in the paper:</p> <ul> <li>LLM: llm-author-affiliations.zip</li> <li>ML: ml-author-affiliations.zip</li> <li>Non-LLM/ML: non-llm-author-affiliations.zip</li> </ul> <p>The three files below contain the paper information used to support all the analysis in our paper:</p> <ul> <li>LLM: llm-information-entropy.csv</li> <li>ML: ml-information-entropy.csv</li> <li>Non-LLM/ML: non-llm-information-entropy.csv</li> </ul> <p>If you have any additional questions, please feel free to contact <a rel="noreferrer">lingyaol@umich.edu or lydinh@usf.edu.</a></p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Sensory-Informed Architectural Design Qualities in Autism
<p><span>This dataset provides a collection of design qualities for autism-friendly designs. The collected data relies on the current literature, including various guidelines and research papers.</span></p>
ExpertSegmentation: Sample datasets for segmentation of microscopy with domain-informed targets via custom loss
<p>Sample datasets for submission to NeurIPS 2024 titled ExpertSegmentation: Segmentation for microscopy with domain-informed targets via custom loss. Hand-labels were generated using Ilastik. Files include:</p> <ol> <li><strong>NMC_3D</strong>: 3D, 2-phase, microCT image of a Lithium-ion battery LiNiMnCoO2 electrode</li> <li><strong>NMC_3D_Labels: </strong>Hand labels for NMC_3D volume.</li> <li><strong>NMC_2D</strong>: 2D, 4-phase, SEM image of a Lithium-ion battery LiNiMnCoO2 electrode cross-section</li> <li><strong>NMC_2D_Labels: </strong>Hand labels for NMC_2D image.</li> <li><strong>Graphite_3D</strong>: 3D, 3-phase, microCT image of a Laser-ablated lithium-ion battery graphite electrode</li> <li><strong>Graphite_3D_Labels: </strong>Hand labels for Graphite_3D volume.</li> <li><strong>PEMFC_3D</strong>: 3D, 5-phase, microCT image of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)</li> <li><strong>PEMFC_3D_Labels: </strong>Hand labels for PEMFC_3D volume.</li> </ol> <p>See references for dataset sources.</p>
Designing a Training Journey for Privacy and Information Security Practitioners in the Federal Public Administration
<p> Context : The Ministry of Management and Innovation in Public Services (MGI) leads the formulation and coordination of the Digital Government Strategy (EGD). DEPSI, under the Secretariat of Digital Government (SGD), is responsible for the Privacy and Information Security Program (PPSI), which aims at data privacy, compliance, and institutional resilience. Problem: The culture of privacy and information security in the Federal Public Administration faces development challenges. Despite the PPSI, there is a lack of awareness initiatives, training, a clear strategy, best practices, and performance indicators. Proposed Solution: The proposal aims to develop a training journey for Practitioners working in roles related to privacy and information security, with the goal of identifying and promoting the best practices, skills, and competencies required for these roles. IS Theory: This study aligns with Organizational Information Processing Theory, providing mechanisms to help organizations adapt to regulatory uncertainties in privacy and security. Method: We employed a mixed approach, combining document analysis, a literature review, and a survey. Guidelines and standards were analyzed to map competencies and responsibilities, while the survey gathered practitioners' perceptions of the proposed training journey. Summary of Results: We identified the key profiles and their corresponding responsibilities, and proposed a personalized training journey. Survey results indicated that the journey meets Practitioners’ expectations, being well-evaluated in terms of criteria and assigned weights. Contributions and Impact in IS: This work contributes by presenting a proposal for a Training Journey to assess the knowledge of Federal Public Administration employees and guide them on the best paths for professional development. </p>
Supporting information for: An assessment of monazite fission-track thermochronology as a proxy for low-magnitude cooling, Catalina-Rincon Metamorphic Core Complex, AZ, U.S.A.
<p><span>The following supporting information contains: The detailed location and age data for the geochronological, isotopic, and geochemical data used in this study, and their associated publications. Detailed thermochronometric data and associated thermal history modelling information for all thermochronology and modelling presented in the study.</span></p>
RESICE - Reusability-targeted Enriched Sea Ice Core Database - General Information
<div> <div>RESICE is described in detail in the article <em>Reusability-targeted enrichment of sea ice core data</em> published on 2025-03-20 in Scientific Data (DOI: <a href="https://doi.org/10.1038/s41597-025-04665-x" target="_blank" rel="noopener">10.1038/s41597-025-04665-x</a>).</div> <div> </div> <div>A large number of sea ice core data sets are available that have been acquired by research groups around the world and published in different data repositories. The structure of sea ice core data differs substantially across repositories and entries regarding combinations of content, level and quality of description, label names, formats, units, etc. Here, we have compiled sea ice core data and metadata available in data sets (DS) into a tabular database. Additionally, we have added data and metadata from articles (A) and expedition reports (ER). We have enriched the database with metadata from instrument manuals (IM) and controlled terminologies (CT) such as the <a title="SIN" href="https://library.wmo.int/idurl/4/41953" target="_blank" rel="noopener"><em>Sea Ice Nomenclature</em></a> (SIN) from the World Meteorological Organization (WMO) and the <a title="SeaVoX Polygons" href="https://doi.org/10.14284/590" target="_blank" rel="noopener"><em>Polygon data set of water body extent from the SeaVoX Salt and Fresh Water Body Gazetteer</em></a> by the British Oceanographic Data Centre (BODC). We grouped the type of sources into primary sources (DS), secondary sources (A, ER), and tertiary sources (IM, CT). RESICE enhances reusability of the included sea ice core data through enrichment. RESICE provides a comprehensive resource for sea ice modeling applications that aim at using information from compiled sea ice core data. Some examples are calibration and validation of physics-based process models addressing the generation and evolution of sea ice or the training of data-driven models that rely on harmonized training data. As data and metadata are combined from many sources, each entry in the data set needs to be traceable to the original source and its corresponding DOI or URL. Where appropriate, we refer to the original excerpt, figure or table of the original source or comment on inconsistencies or required changes to transparently communicate the entries origin. This is the general information on the database. Please find <a title="RESICE Part A" href="https://www.doi.org/10.5281/zenodo.14745035" target="_blank" rel="noopener">Part A</a> of the database that can be reused under license CC-BY, and <a href="https://www.doi.org/10.5281/zenodo.14744942">Part B</a> of the database that can be reused under license CC-BY-SA. RESICE can be interactively viewed, analyzed and plotted in the <a title="MOSAiC webODV" href="https://mvre.webodv.cloud.awi.de/DataExploration/id/DVevtE7c">MOSAiC webODV</a> instance. RESICE can be reproduced and extended with the <a title="pyresice Python package" href="https://doi.org/10.5281/zenodo.11198658" target="_blank" rel="noopener">pyresice</a> Python package available on <a title="pyresice gitLab" href="https://git.rwth-aachen.de/mbd/pyresice/" target="_blank" rel="noopener">gitLab</a>.</div> </div>
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles - English and Spanish neighborhood sub-graphs
<p>Two language-specific sub-graphs (English and Spanish) based on the ConceptNet Knowledge Graph. These two files are required to run the code for reproducing the results reported in the paper <a href="https://aclanthology.org/2021.latechclfl-1.16/">"Zero-Shot Information Extraction to Enhancea Knowledge Graph Describing Silk Textiles"</a> at the <a href="https://sighum.wordpress.com/events/latech-clfl-2021/">LaTeCH-CLfL 2021</a> workshop co-located with <a href="https://2021.emnlp.org/">EMNLP 2021</a>.</p>
Information literacy in the area of Library and Information Science. A bibliometric analysis in Latin America, from the Lens database (2001-2020).
<p>The results of scientific production on ALFIN (2001-2020) in the areas of Library and Information Science are shown. All BIC journals were identified from Latindex. Then it was verified whether these journals were contained in the following databases: Web of Science (Core Collection and Scielo Citation Index), Scopus, Lens and Dimensions. The Lens database was chosen for retrieving records on ALFIN and performing the bibliometric analysis, as it has the highest coverage of BIC journals in Latindex. The trend and growth of scientific production were evaluated according to authors and year of publication; the productivity of authors was analyzed using Lotka's Law and the dispersion of the literature according to Bradford's Law. The degree, index and coefficient of collaboration were determined and collaboration networks were identified according to authors. The results show that scientific production on ALFIN in Latin America, reached a peak between 2017 and 2018, presenting a decrease from 2019 onwards. It was also observed that the production, collaboration between authors and the number of journals is predominantly Brazilian.</p>
Information and results obtained from IoT Platform in Alba Iulia Pilot
<p> </p> <p> The IoT platform allows the geolocation of the container, as well as manage the information of the filling level and traceability of trucks. The eco driving application on board of the truck sends the data from the truck (location, speed, RPM and engine load) t o the IoT platform where it is stored. At the same time sound alarms are emitted when driver excess the ecodriving parameters in the Android application. Besides this, the application receives from the IoT platform the position of the containers that must be collected.</p> <p>In this way the driver can see the position, order of collection of the containers and pathway to collect them, as explained above.<br> The IoT platform receives the information of the labels dispensed from the smart containers. The score of the characterized bags were registered in the platform as explained in the next section. </p> <p>Attached the different data sets generated during the project.</p> <p>For general info, follow the link: https://plasticircle.eu/home/</p>
Information and results obtained from IoT Platform in Valencia Pilot
<p> The IoT platform allows the geolocation of the container, as well as manage the information of the filling level and traceability of trucks. The eco driving application on board of the truck sends the data from the truck (location, speed, RPM and engine load) t o the IoT platform where it is stored. At the same time sound alarms are emitted when driver excess the ecodriving parameters in the Android application. Besides this, the application receives from the IoT platform the position of the containers that must be collected.</p> <p>In this way the driver can see the position, order of collection of the containers and pathway to collect them, as explained above.<br> The IoT platform receives the information of the labels dispensed from the smart containers. The score of the characterized bags were registered in the platform as explained in the next section. </p> <p>Attached the different data sets generated during the project.</p> <p>For general info, follow the link: https://plasticircle.eu/home/</p> <p> </p>
Eastern bettong (Bettongia gaimardi) reintroduced to Mulligan's Flat Woodland Sanctuary and Tidbinbilla Nature Reserve: DArT SNPs + individual information
<p>Incorporating genetic data into conservation programmes improves management outcomes, but the impact of different sample-grouping methods on genetic diversity analyses is poorly understood. To this end, the multi-source reintroduction of the eastern bettong (<em>Bettongia gaimardi</em>) was used as a long-term case study to investigate how sampling regimes may affect common genetic metrics, and hence management decisions. The dataset comprised 5307 SNPs sequenced across 263 individuals. Samples included 45 founders from five genetically distinct Tasmanian source regions, and 218 of their descendants captured during annual monitoring at Mulligan's Flat Woodland Sanctuary (MFWS; 121 samples across eight generations), and Tidbinbilla Nature Reserve (TNR; 97 samples across nine generations). The most management-informative sampling regime was found to be generational cohorts, providing detailed long-term trends in genetic diversity. When these generation-specific trends were not investigated, recent changes in population genetics were masked, and it became apparent that management recommendations would be less appropriate. The results also illuminated the importance of considering establishment and persistence as separate phases of a multi-source reintroduction. The establishment phase (useful for informing early adaptive management) should consist of no less than two generations, and continue until admixture is achieved (admixture defined here as >80% of individuals possessing >60% of source genotypes, with no one source composing >70% of >20% individuals' genotype) is achieved. This ensures that the persistence phase analyses of population trends remain minimally biased. Based on this case study, we recommend that emphasis be given to the value of generationally specific analyses, and that conservation programmes collect DNA samples throughout the establishment and persistence phases, and avoid collecting genetic samples only when analysis is imminent. We also recommend that population genetic analyses for multi-source reintroductions consider whether admixture has been achieved when calculating descriptive genetic metrics. </p>
Supplementary Information for "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module"
<p>This is the external Supplementary Information for our publication "UV-Spectroscopic Detection of (Pyro-)Phosphate with the PUB module".</p> <p>The .pdf file contains the Supplementary Information: author contributions, accessibility statement, experimental procedures, additional discussions and supplementary items, among others.</p> <p>The .zip file contains the raw data and metadata for all items (supplementary and main text) as well as the calculation results.</p> <p>To some extent, this work builds on and borrows from our previous publications on spectral unmixing (https://doi.org/10.3390/mps2030060, https://doi.org/10.1002/cbic.202000204) and thermodynamic reaction control (https://doi.org/10.1002/adsc.201901230, https://doi.org/10.1002/cphc.202000901, https://doi.org/10.1021/acscatal.1c02589).</p>
Simulated treatment effects on bird communities inform landscape‐scale dry conifer forest management
<p>Human land use and climate change have increased forest density and wildfire risk in dry conifer forests of western North America, threatening various ecosystem services, including habitat for wildlife. Government policy supports active management to restore historical structure and ecological function. Information on potential contributions of restoration to wildlife habitat can allow assessment of tradeoffs with other ecological benefits when prioritizing treatments. We predicted avian responses to simulated treatments representing alternative scenarios to inform landscape‐scale forest management planning along the Colorado Front Range. We used data from the Integrated Monitoring in Bird Conservation Regions program to inform a hierarchical multispecies occupancy model relating species occupancy and richness with canopy cover at two spatial scales. We then simulated changes in canopy cover (remotely sensed in 2018) under three alternative scenarios, (1) a "fuels reduction" scenario representing landscape‐wide 30% reduction in canopy cover, (2) a "restoration" scenario representing more nuanced, spatially variable treatments targeting historical conditions, and (3) a reference, no‐change scenario. Model predictions showed areas of potential gains and losses for species richness, richness of ponderosa pine forest habitat specialists, and the ratio of specialists to generalists at two (1 km<sup>2</sup> and 250 m<sup>2</sup>) spatial scales. Under both fuels reduction and restoration scenarios, we projected greater gains than losses for species richness. Surprisingly, despite restoration more explicitly targeting ecologically relevant historical conditions, fuels reduction benefited bird species richness over a greater spatial extent than restoration, particularly in the lower montane life zone. These benefits reflected generally positive species associations with moderate canopy cover promoted more consistently under the fuels reduction scenario. In practice, contemporary forest management is likely to lie somewhere between the fuels reduction and restoration scenarios represented here. Therefore, our results inform where and how active forest management can best support avian diversity. Although our study raises questions regarding the value of including landscape‐scale heterogeneity as a management objective, we do not question the value of targeting finer-scale heterogeneity (i.e., stand and treatment level). Rather, our results combined with those from previous work clarify the scale at which targeting structural heterogeneity and historical reference conditions can promote particular ecosystem services.</p>
Improved Bathymetric Prediction using Geological Information: SYNBATH
<p>Manuscript in revision: <em>Earth and Space Science, </em>December 20, 2021</p> <p><em>Abstract</em></p> <p>To date, approximately 20% of the ocean floor has been surveyed by ships at a spatial resolution of 400 m or better. The remaining 80% has depth predicted from satellite altimeter-derived gravity measurements at a relatively low resolution. There are many remote ocean areas in the southern hemisphere that will not be completely mapped at 400 m resolution during this decade. This study is focused on the development of synthetic bathymetry to fill the gaps. There are two types of seafloor features that are not typically well resolved by satellite gravity: abyssal hills and small seamounts (< 2.5 km tall). We generate synthetic realizations of abyssal hills by combining the measured statistical properties of mapped abyssal hills with regional geology including fossil spreading rate/orientation, rms height from satellite gravity, and sediment thickness. With recent improvements in accuracy and resolution, It is now possible to detect all seamounts taller than about 800 m in satellite-derived gravity and their location can be determined to an accuracy of better than 1 km. However, the width of the gravity anomaly is much greater than the actual width of the seamount so the seamount predicted from gravity will underestimate the true seamount height and overestimate its base dimension. In this study we use the amplitude of the vertical gravity gradient (VGG) to estimate the mass of the seamount and then use their characteristic shape, based on well surveyed seamounts, to replace the smooth predicted seamount with a seamount having a more realistic shape. </p> <p>SYNBATH_V1.2 September 20, 2021</p> <p>This version of SYNBATH has abyssal hills as described below. Superimposed on that are 30,000 gaussian seamounts with sigma to height ratios of 2.4. The heights were determined by fitting a uncompensated model VGG for a seamount of a particular height to the observed VGG in a 33 by 33 km area using a density of 2800 kg m^-3. Any seamount taller than 2600 m or less than 700 m was not used.</p> <p>SYNBATH_V1.1 July 6, 2021</p> <p>A refined version of the SYNBAPS with better blending</p> <p>SYNBATH_V1.0 July 1, 2021</p> <p>This is the first version of SYNthetic BATHymetry (SYNBATH) that is a merge of the latest SRTM15 global bathymetry/topography grid and synthetic abyssal hill fabric based on an anisotropic power spectral model published by Goff and others [2010, 2020]. The synthetic abyssal fabric fills the voids in the real bathymetry coverage that used to be filled by predicted depth.</p> <p>These are global grids with 86400 columns and 43200 rows in NETCDF format.</p> <p>Seamount Heights used in SYNBATH_V1.2 December 15, 2021</p> <p>This directory contains the locations and heights of the seamounts in the combined New and Kim Wessel (KW) catalogues. There are three categories of seamounts.</p> <p>1) good.nxybh - contains 34295 with heights successfully modeled using the VGG as described in the Sandwell 2022 publication. The file has 5 columns:</p> <p>name longitude latitude base_depth height_VGG<br> KW-00001 0.191666666667 -6.44166666667 -4060.15673828 2600<br> KW-00002 -0.425 -6.84166666667 -4125.54345703 2600<br> KW-00003 -0.075 -6.875 -4221.48730469 2600<br> .<br> .<br> .</p> <p><br> 2) uncharted.nxybh - contains 19732 seamounts that are more than 3 km from a depth sounding. The file has 5 columns:</p> <p>name longitude latitude base_depth height_VGG<br> New-00001 3.60833333333 2.74166666667 -3990.33374023 2000<br> New-00002 3.375 2.59166666667 -4113.46435547 1500<br> New-00003 3.19166666667 2.475 -4209.29638672 1200<br> .<br> .<br> .</p> <p>3) well_charted.nxybh - contains 739 seamounts that are well charted by more than 50% sounding coverage over the seamount and good coverage at the summit so the summit depth is known. The file has 6 columns:</p> <p>name longitude latitude base_depth height_VGG summit_depth<br> New-00707 -8.525 71.4916666667 -2094.12524414 1000 -1044.107788<br> New-00786 -4.775 70.0083333333 -2978.39868164 1200 -2427.73095683<br> New-00808 -4.375 66.2583333333 -3378.35644531 1100 -2656.7897947<br> .<br> .<br> .</p> <p>In addition, there are three matching kmz-files so the locations of the seamounts can be viewed in Google Earth.<br> good.kmz - yellow dots<br> uncharted.kmz - red dots<br> well_charted.kmz - green dots</p> <p> </p> <p> </p>
Annotation dataset for the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind"
<p>This is the resulting dataset from the text annotation exercise in the article titled "<strong>On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind</strong>" that will appear in the journal Open Archeology in a special issue titled Archaeological Practice on Shifting Grounds (edited by Åsa Berggren and Antonia Davidovic-Walther). The article is accepted for publication and the annotations are final. CIDOC CRM is used for text annotations.</p>
Dataset for "Evaluation of Publicly Available Information on Sex-related Differences in the Efficacy and Safety of New Molecular Entities and Therapeutic Biological Products"
<p>Contains our extraction sheets with additional documents/notes on methods used in our study.</p>
Sequential and efficient neural-population coding of complex task information
<p>Condensed neural and behavioral data, included secondary results from model fits and analyses. This dataset contains all information required to recreate figures from the paper.</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.