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Network traffic datasets with novel extended IP flow called NetTiSA flow
<p><strong>Network traffic datasets with novel extended IP flow called NetTiSA flow</strong></p> <p>Datasets were created for the paper: NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification -- Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka -- which is published in The International Journal of Computer and Telecommunications Networking <a href="https://doi.org/10.1016/j.comnet.2023.110147" rel="nofollow">https://doi.org/10.1016/j.comnet.2023.110147</a><br><br>Please cite the usage of our datasets as:</p> <blockquote> <p>Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka, "NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification", Computer Networks, Volume 240, 2024, 110147, ISSN 1389-1286<br><br></p> <pre><code>@article{KOUMAR2024110147, title = {NetTiSA: Extended IP flow with time-series features for universal bandwidth-constrained high-speed network traffic classification}, journal = {Computer Networks}, volume = {240}, pages = {110147}, year = {2024}, issn = {1389-1286}, doi = {https://doi.org/10.1016/j.comnet.2023.110147}, url = {https://www.sciencedirect.com/science/article/pii/S1389128623005923}, author = {Josef Koumar and Karel Hynek and Jaroslav Pešek and Tomáš Čejka} } </code></pre> </blockquote> <p>This Zenodo repository contains 23 datasets created from 15 well-known published datasets, which are cited in the table below. Each dataset contains the NetTiSA flow feature vector.<br><br> </p> <p><strong>NetTiSA flow feature vector</strong></p> <p><br>The novel extended IP flow called NetTiSA (Network Time Series Analysed) flow contains a universal bandwidth-constrained feature vector consisting of 20 features. We divide the NetTiSA flow classification features into three groups by computation. The first group of features is based on classical bidirectional flow information---a number of transferred bytes, and packets. The second group contains statistical and time-based features calculated using the time-series analysis of the packet sequences. The third type of features can be computed from the previous groups (i.e., on the flow collector) and improve the classification performance without any impact on the telemetry bandwidth.</p> <p> </p> <p><strong>Flow features</strong></p> <p>The flow features are:</p> <ul> <li><strong><em>Packets</em></strong> is the number of packets in the direction from the source to the destination IP address.</li> <li><em><strong>Packets in reverse order</strong></em> is the number of packets in the direction from the destination to the source IP address.</li> <li><strong><em>Bytes</em> </strong>is the size of the payload in bytes transferred in the direction from the source to the destination IP address.</li> <li><strong><em>Bytes in reverse order</em></strong> is the size of the payload in bytes transferred in the direction from the destination to the source IP address.</li> </ul> <p> </p> <p><strong>Statistical and Time-based features</strong></p> <p>The features that are exported in the extended part of the flow. All of them can be computed (exactly or in approximative) by stream-wise computation, which is necessary for keeping memory requirements low. The second type of feature set contains the following features:</p> <ul> <li><strong><em>Mean</em></strong> represents mean of the payload lengths of packets</li> <li><strong><em>Min</em></strong> is the minimal value from payload lengths of all packets in a flow</li> <li><strong><em>Max</em></strong> is the maximum value from payload lengths of all packets in a flow</li> <li><strong><em>Standard deviation</em></strong> is a measure of the variation of payload lengths from the mean payload length</li> <li><strong><em>Root mean square</em></strong> is the measure of the magnitude of payload lengths of packets</li> <li><strong><em>Average dispersion</em></strong> is the average absolute difference between each payload length of the packet and the mean value</li> <li><strong><em>Kurtosis</em></strong> is the measure describing the extent to which the tails of a distribution differ from the tails of a normal distribution</li> <li><em><strong>Mean of relative times</strong></em> is the mean of the relative times which is a sequence defined as <span>\(st = \{t_1 - t_1, t_2 - t_1, ..., t_n - t_1\} \)</span></li> <li><em><strong>Mean of time differences</strong></em> is the mean of the time differences which is a sequence defined as <span>\(dt = \{ t_j - t_i | j = i + 1, i \in \{1, 2, \dots, n - 1\} \}.\)</span></li> <li><em><strong>Min from time differences</strong></em> is the minimal value from all time differences, i.e., min space between packets.</li> <li><em><strong>Max from time differences</strong></em> is the maximum value from all time differences, i.e., max space between packets.</li> <li><em><strong>Time distribution</strong></em> describes the deviation of time differences between individual packets within the time series. The feature is computed by the following equation:<br><span>\(tdist = \frac{ \frac{1}{n-1} \sum_{i=1}^{n-1} \left| \mu_{\{dt_{n-1}\}} - dt_i \right| }{ \frac{1}{2} \left(max\left(\{dt_{n-1}\}\right) - min\left(\{dt_{n-1}\}\right) \right) }\)</span></li> <li><em><strong>Switching ratio</strong></em> represents a value change ratio (switching) between payload lengths. The switching ratio is computed by equation:<br><span>\(sr = \frac{s_n}{\frac{1}{2} (n - 1)}\)</span></li> </ul> <p> where <span>\(s_n\)</span> is number of switches.</p> <p> </p> <p><strong>Features computed at the collector</strong><br>The third set contains features that are computed from the previous two groups prior to classification. Therefore, they do not influence the network telemetry size and their computation does not put additional load to resource-constrained flow monitoring probes. The NetTiSA flow combined with this feature set is called the Enhanced NetTiSA flow and contains the following features:</p> <ul> <li><em><strong>Max minus min</strong></em> is the difference between minimum and maximum payload lengths</li> <li><em><strong>Percent deviation</strong></em> is the dispersion of the average absolute difference to the mean value</li> <li><em><strong>Variance</strong></em> is the spread measure of the data from its mean</li> <li><em><strong>Burstiness</strong></em> is the degree of peakedness in the central part of the distribution</li> <li><em><strong>Coefficient of variation</strong></em> is a dimensionless quantity that compares the dispersion of a time series to its mean value and is often used to compare the variability of different time series that have different units of measurement</li> <li><em><strong>Directions</strong></em> describe a percentage ratio of packet direction computed as <span>\(\frac{d_1}{ d_1 + d_0}\)</span>, where <span>\(d_1\)</span> is a number of packets in a direction from source to destination IP address and <span>\(d_0\)</span> the opposite direction. Both <span>\(d_1\)</span> and <span>\(d_0\)</span> are inside the classical bidirectional flow.</li> <li><em><strong>Duration</strong></em> is the duration of the flow</li> </ul> <p> </p> <p>The NetTiSA flow is implemented into IP flow exporter <a href="https://github.com/CESNET/ipfixprobe">ipfixprobe</a>.</p> <p> </p> <p><strong>Description of dataset files</strong></p> <p>In the following table is a description of each dataset file:</p> <table> <tbody> <tr> <td> <p><strong>File name</strong></p> </td> <td> <p><strong>Detection problem</strong></p> </td> <td> <p><strong>Citation of the original raw dataset</strong></p> </td> </tr> <tr> <td>botnet_binary.csv </td> <td>Binary detection of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>botnet_multiclass.csv </td> <td>Multi-class classification of botnet </td> <td>S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014. </td> </tr> <tr> <td>cryptomining_design.csv </td> <td>Binary detection of cryptomining; the design part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>cryptomining_evaluation.csv </td> <td>Binary detection of cryptomining; the evaluation part </td> <td>Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022 </td> </tr> <tr> <td>dns_malware.csv </td> <td>Binary detection of malware DNS </td> <td>Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021. </td> </tr> <tr> <td>doh_cic.csv </td> <td>Binary detection of DoH </td> <td>Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020 </td> </tr> <tr> <td>doh_real_world.csv </td> <td>Binary detection of DoH </td> <td>Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022 </td> </tr> <tr> <td>dos.csv </td> <td>Binary detection of DoS </td> <td>Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019. </td> </tr> <tr> <td>edge_iiot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>edge_iiot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022. </td> </tr> <tr> <td>https_brute_force.csv </td> <td>Binary detection of HTTPS Brute Force </td> <td>Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020 </td> </tr> <tr> <td>ids_cic_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>ids_cic_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018. </td> </tr> <tr> <td>unsw_binary.csv </td> <td>Binary detection of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>unsw_multiclass.csv </td> <td>Multi-class classification of intrusion in IDS </td> <td>Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015. </td> </tr> <tr> <td>iot_23.csv </td> <td>Binary detection of IoT malware </td> <td>Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23 </td> </tr> <tr> <td>ton_iot_binary.csv </td> <td>Binary detection of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>ton_iot_multiclass.csv </td> <td>Multi-class classification of IoT malware </td> <td>Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021 </td> </tr> <tr> <td>tor_binary.csv </td> <td>Binary detection of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>tor_multiclass.csv </td> <td>Multi-class classification of TOR </td> <td>Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017. </td> </tr> <tr> <td>vpn_iscx_binary.csv </td> <td>Binary detection of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_iscx_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016. </td> </tr> <tr> <td>vpn_vnat_binary.csv </td> <td>Binary detection of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> <tr> <td>vpn_vnat_multiclass.csv </td> <td>Multi-class classification of VPN </td> <td>Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022 </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p>
Phishing and Benign Domain Dataset (DNS, IP, WHOIS/RDAP, TLS, GeoIP)
<p>The dataset contains DNS records, IP-related features, WHOIS/RDAP information, information from TLS certificate fields, and GeoIP information for 432,572 verified benign domains from Cisco Umbrella and 36,993 verified phishing domains from PhishTank and OpenPhish services. The dataset is useful for statistical analysis of domain data or feature extraction for training machine learning-based classifiers, e.g. for phishing detection. The data was collected between March and July 2023.The final assessment of the data was conducted in July 2023 (this is why the names are suffixed with _2307).</p> <p>The upload contains: a) <strong>data</strong> files, b) the description of the data <strong>structure</strong>, and c) the <strong>veature vector</strong> we used for ML-based phishing domain detection.</p> <h1>Data Files</h1> <p>The data is located in two individual files:</p> <ul> <li><strong>benign_2307.json</strong> - data about 432,572 benign domains, and</li> <li><strong>phishing_2307.json</strong> - data about 36,993 phishing domains.</li> </ul> <h1>Data Structure</h1> <p>Both files are in the JSON Array format. The structure is as follows:</p> <pre><code>[ { "_id" : "A unique ID of the data record", "domain_name" : "Name of the domain (e.g., zenodo.com)", "dns" : { "//": "Data obtained from DNS records" }, "evaluated_on" : "// ISO Timestamp of data collection ", "ip_data" : [ "// Data for each related IP adddress ", { "//": "IP-related data, including RTT from ICMP echo attempts (from Brno, Czechia)", "//": "WHOIS/RDAP data for the given IP address", "//": "GeoIP data for the given IP address", "//": "NERD system reputation score (if available)", "//": "ASN info", "//": "remarks: ISO timestamps of collection of the individual data pieces" }, ], "label" : "benign_2307 for benign OR misp_2307 for phishing", "rdap" : { "//": "WHOIS/RDAP information for the domain name" }, "remarks" : { "dns_evaluated_on" : "ISO Timestamp of DNS data collection", "rdap_evaluated_on" : "ISO Timestamp of WHOIS/RDAP data collection", "tls_evaluated_on" : "ISO Timestamp of TLS certificate information collection", "dns_had_no_ips" : "true if no IPs were found in DNS records" }, "sourced_on" : "ISO Timestamp of the moment the domain was found", "tls" : { "cipher" : "Identifier of the TLS cipher suite", "count" : "Number of certificates in chain", "protocol" : "Version of the TLS protocol", "certificates" : [ "//": "Information from TLS certificate fields: issuer, extensions, etc." ] }, "category" : "Category of the record (could be ignored)", "source" : "Name of the file that we used to save the domain list" } ]</code></pre> <h1>Feature Vector</h1> <p>This section describes the veature vector used in the "Unmasking the Phishermen: Phishing Domain Detection with Machine Learning and Multi-Source Intelligence" paper that was accepted to the IEEE NOMS 2024 conference.</p> <h2>Lexical Features</h2> <p>The following features were extracted from the sole domain name:</p> <ul> <li><strong>lex_name_len</strong> - length of the domain name,</li> <li><strong>lex_begins_with_digit</strong> - true if the domain name begins with a digit,</li> <li><strong>lex_www_flag</strong> - true if the domain name begins with "www.",</li> <li><strong>lex_phishing_keyword_count</strong> - occurence count of 47 phishing-related keywords,</li> <li><strong>lex_consecutive_chars</strong> - length of the longest consecutive character sequence,</li> <li><strong>lex_tld_len</strong> - length of the top-level domain (TLD),</li> <li><strong>lex_tld_hash</strong> - hash of the TLD,</li> <li><strong>lex_sld_len </strong>- length of the second-level domain (SLD),</li> <li><strong>lex_sld_norm_entropy </strong>- normalized entropy of the SLD,</li> <li><strong>lex_stld_unique_char_count </strong>- number of unique characters in the TLD and the SLD,</li> <li><strong>lex_sub_count </strong>- number of subdomains,</li> <li><strong>lex_sub_digit_ratio </strong>- ratio of digits in subdomains,</li> <li><strong>lex_sub_hex_ratio </strong>- ratio of hex symbols in subdomains,</li> <li><strong>lex_sub_non_alpanum_ratio </strong>- ratio of non-alphanumeric symbols in subdomains,</li> <li><strong>lex_sub_vowel_ratio </strong>- ratio of vowels in subdomains,</li> <li><strong>lex_sub_consonant_ratio </strong>- ratio of consonants in subdomains,</li> <li><strong>lex_sub_max_consonant_len </strong>- length of the longest consonant sequence in subdomains,</li> <li><strong>lex_sub_norm_entropy </strong>- normalized entropy of a string made from all subdomains,</li> <li><strong>lex_phishing_bigram_matches</strong> - occurrence count of the top 300 phishing domain bigrams,</li> <li><strong>lex_phishing_trigram_matches</strong> - occurrence count of the top 2000 phishing domain trigrams,</li> <li><strong>lex_phishing_tetragram_matches</strong> - occurrence count of the top 5000 phishing domain tetragrams,</li> <li><strong>lex_phishing_pentagram_matches</strong> - occurrence count of the top 10000 phishing domain pentagrams.</li> </ul> <h2>DNS-based Features</h2> <p>The following features were extracted from DNS responses when querying about the domain:</p> <ul> <li><strong>dns_A_count</strong> - number of A records for the domain,</li> <li><strong>dns_AAAA_count</strong> - number of AAAA records for the domain,</li> <li><strong>dns_CNAME_count</strong> - number of CNAME records for the domain,</li> <li><strong>dns_MX_count</strong> - number of MX records for the domain,</li> <li><strong>dns_NS_count</strong> - number of nameserver (NS) records for the domain,</li> <li><strong>dns_TXT_count</strong> - number of TXT records for the domain,</li> <li><strong>dns_soa_primary_ns_len</strong> - number of characters in the primary NS's domain name,</li> <li><strong>dns_soa_primary_ns_level</strong> - number of subdomain in the primary NS's domain name,</li> <li><strong>dns_soa_primary_ns_digit_count</strong> - number of digits in the primary NS's domain name,</li> <li><strong>dns_soa_primary_ns_entropy </strong>- normalized entropy of the primary NS's domain name,</li> <li><strong>dns_soa_email_len</strong> - number of characters in the admin's email domain name part,</li> <li><strong>dns_soa_email_level</strong> - number of subdomains in the admin's email domain name part,</li> <li><strong>dns_soa_email_digit_count</strong> - number of digits in the admin's email domain name part,</li> <li><strong>dns_soa_email_entropy</strong> - normalized entropy of the admin's email domain name part,</li> <li><strong>dns_soa_refresh</strong> - SOA refresh parameter,</li> <li><strong>dns_soa_retry</strong> - SOA retry parameter,</li> <li><strong>dns_soa_expire</strong> - SOA expire parameter,</li> <li><strong>dns_mx_avg_len</strong> - average number of characters of the domain names in MX records,</li> <li><strong>dns_mx_avg_entropy</strong> - average normalized entropy of the domain names in MX records,</li> <li><strong>dns_domain_name_in_mx </strong>- true if the domain name is contained in the MX record's domains,</li> <li><strong>dns_txt_spf_exists</strong> - true if an SPF record is in the TXT RRs,</li> <li><strong>dns_txt_avg_entropy</strong> - average normalized entropy of the TXT records</li> <li><strong>dns_ttl_low</strong> - number of RRsets with TTL in [0,100],</li> <li><strong>dns_ttl_mid </strong>- number of RRsets with TTL in [101,500],</li> <li><strong>dns_zone_entropy</strong> - normalized entropy of the zone's domain name.</li> </ul> <h2>IP-based Features</h2> <p>These features were derived from IP addresses and ICMP echo replies:</p> <ul> <li><strong>ip_mean_average_rtt</strong> - average RTT of all ICMP echo attempts,</li> <li><strong>ip_entropy</strong> - total entropy of all /16 (/64 for v6) IP prefixes,</li> <li><strong>ip_count</strong> - total number of IP addresses for the domain,</li> <li><strong>ip_v4_count</strong> - total number of IPv4 addresses for the domain,</li> <li><strong>ip_v6_count</strong> - total number of IPv6 addresses for the domain,</li> </ul> <h2>TLS-based Features</h2> <p>The following features were extracted from TLS certificate chains and TLS handshakes:</p> <ul> <li><strong>tls_chain_len</strong> - length of the TLS certificate chain,</li> <li><strong>tls_broken_chain</strong> - true if there is a certificate that has never been valid,</li> <li><strong>tls_expired_chain </strong>- true if there is an expired certificate in the chain,</li> <li><strong>tls_total_extension_count</strong> - total extensions in all certificates in the chain,</li> <li><strong>tls_critical_extensions</strong> - total extensions flagged as "critical" in all certificates,</li> <li><strong>tls_with_policies_crt_count</strong> - number of certificates that include the "policies" extension,</li> <li><strong>tls_percentage_crt_with_policies</strong> - percentage of certificates that include the "policies" extension,</li> <li><strong>tls_x509_anypolicy_crt_count</strong> - number of certificates not enforcing any security policy,</li> <li><strong>tls_iso_policy_crt_count</strong> - total discovered policies from the 1.* OID space,</li> <li><strong>tls_joint_isoitu_policy_crt_count</strong> - total discovered policies from from the 2.* OID space,</li> <li><strong>tls_subject_count</strong> - number of subject alternative names (SANs) in the leaf certificate,</li> <li><strong>tls_server_auth_crt_count</strong> - number of certificates with the "Web Server Authentication",</li> <li><strong>tls_client_auth_crt_count</strong> - number of certificates with the "Web Client Authentication",</li> <li><strong>tls_CA_certs_in_chain_ratio</strong> - ratio of CA certificates in the chain,</li> <li><strong>tls_unique_SLD_count</strong> -number of unique second-level domains (SLD) in domain name SANs, </li> <li><strong>tls_common_name_count</strong> - number of common names in the chains,</li> <li><strong>tls_root_cert_validity_len</strong> - length of the validity period of the root certificate,</li> <li><strong>tls_leaf_cert_validity_len</strong> - length of the validity period of the leaf certificate.</li> </ul> <h2>WHOIS/RDAP-based Features</h2> <p>These features are based on the information gathered from WHOIS/RDAP when asking about: a) the domain name and b) domain-related IP addresses:</p> <ul> <li><strong>rdap_registration_period</strong> - difference between domain expiration and registration date,</li> <li><strong>rdap_has_dnssec</strong> - true if DNSSEC is used for the domain,</li> <li><strong>rdap_domain_age</strong> - days elapsed from the domain registration,</li> <li><strong>rdap_time_from_last_change</strong> - days elapsed from the last change of records,</li> <li><strong>rdap_domain_active_time</strong> - min(today, expiration) - registration date,</li> <li><strong>rdap_registrar_name_hash</strong> - hash of the domain's registrar,</li> <li><strong>rdap_ip_avg_admin_name_len</strong> - average length of the admin's name for IP addresses.</li> </ul> <h2>Geolocation Features</h2> <p>This set of features is based on information gathered from the GeoIP service when asked about domain-related IP addresses:</p> <ul> <li><strong>geo_countries_count</strong> - number of distinct countries where servers of domain-related IPs are located,</li> <li><strong>geo_countries_hash</strong> - a unique hash for each combination of countries amongst domain-related IPs,</li> <li><strong>geo_continent_hash</strong> - a uniuque hash for each combinations of continents where the countries are situated.</li> </ul>
RNA-Seq read counts from monocyte- and ips-derived macrophages
<p>This file contains raw RNA-Seq read counts from monocyte-derived macrophages (MDMs) and induced pluripotent stem cell-derived macrophages (IPSDMs) before and after 6 hours stimulation after LPS.</p> <p>The file was created using featureCounts. The first two columns are:</p> <ul> <li>gene_id: Ensembl 74 gene id.</li> <li>length: Length of the gene in bp.</li> </ul> <p>The MDM samples are:</p> <ul> <li>B1_ctrl</li> <li>B1_LPS</li> <li>B4_ctrl</li> <li>B4_LPS</li> <li>B5_ctrl</li> <li>B5_LPS</li> <li>B2_ctrl</li> <li>B2_LPS</li> <li>B3_ctrl</li> <li>B3_LPS</li> </ul> <p>The IPSDM samples are:</p> <ul> <li>CRL1_ctrl</li> <li>CRL1_LPS</li> <li>S7RE_ctrl</li> <li>S7RE_LPS</li> <li>FSPS10C_ctrl</li> <li>FSPS10C_LPS</li> <li>FSPS11B_ctrl</li> <li>FSPS11B_LPS</li> </ul> <p> </p>
Parsayarya/Scraping-ATCM: ATCM WP and IP paper dataset
<p><strong>Dataset descriptions</strong>: This dataset contains a collection of information and working papers from the Antarctic Treaty Consultative Meeting (ATCM) held since 1961. Each record in the dataset offers a comprehensive view of individual documents presented during the meeting, including:</p> <ul> <li><strong>ID</strong>: A unique identifier for each document, typically including the ATCM session number, document number, and file extension (e.g., ATCM45_ip001_e.docx).</li> <li><strong>DocumentID</strong>: A numerical identifier assigned to each document for easy reference and categorization.</li> <li><strong>Type</strong>: The classification of the document, such as 'ip' (Information Paper) or 'wp' (Working Paper), indicating the nature of the content.</li> <li><strong>Year</strong>: The year of the ATCM session, signifying the temporal context of the document (e.g., 2023).</li> <li><strong>Title</strong>: The official title of the document, providing a concise summary of its subject or focus.</li> <li><strong>Submitted By</strong>: The country or organization that submitted the document, reflecting the diverse international participation in the ATCM.</li> <li><strong>Category</strong>: The thematic category assigned to the document, such as 'Cooperation with other organizations' or 'Operation of the Antarctic Treaty System', helps in understanding the focus areas of the meeting.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>Research funded by Australian Research Council SRIEAS Grant SR200100005 Securing Antarctica’s Environmental Future.</p>
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.
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>
Fig. 2 in Occurrence of pathogens in populations of Ips typographus, Ips sexdentatus (Coleoptera, Curculionidae, Scolytinae) and Hylobius spp. (Coleoptera, Curculionidae, Curculioninae) from Austria, Poland and France
Fig. 2. Pathogens in I. typographus from different bio-geographic regions in Europe: n Northern continental – Baltic, o North-eastern Alps (transitional Atlantic – continental), p South-eastern Alps (transitional Alpine – Illyric), Q Western Alps and R Atlantic.
Рис. 2. Процесс поеΔания гусеницами искусственной питатеΛьной среΔы Fig. 2. Caterpillars feeding on artificial nutrient medium in Development dynamics of Kavkaz-2 and Sovetskaya-14 NGL silkworm caterpillars on artificial nutrient medium IPS 7.2-G
Рис. 2. Процесс поеΔания гусеницами искусственной питатеΛьной среΔы Fig. 2. Caterpillars feeding on artificial nutrient medium
Fig. 2 in Insecticidal activity of three plant extracts against adult Ips typograpgus L. under laboratory conditions
Fig. 2. Concentration–mortality response curves from the probit analyses testing insecticide effects of plant extracts against adults ofIps typographus in laboratory bioassays.
Fig. 1 in Insecticidal activity of three plant extracts against adult Ips typograpgus L. under laboratory conditions
Fig. 1. Mean cumulative mortality* of Ips typographus adults four days after treatments with different concentrations of three plant extracts in laboratory bioassays. The error bars represent the standard errors of means. (*corrected for a natural mortality in the control)
Fig. 3 in Insecticidal activity of three plant extracts against adult Ips typograpgus L. under laboratory conditions
Fig. 3. Kaplan-Meier estimates of the survival curves for Ips typographus adults treated with the plant extracts (2.5%) in laboratory bioassays.
Fig. 1. IPS 31102, a left maxillary fragment with the upper incisor and P4–M2 in The Never-Ending Problem of Miocene Beaver Taxonomy
Fig. 1. IPS 31102, a left maxillary fragment with the upper incisor and P4–M2 of the castorid Chalicomys catalaunicus (Bataller, 1838) from Sant Quirze (MN7+8 from the Vallès−Penedès Basin, Catalonia, Spain). Note the abundant cement infilling all synclines.
FIGURE 2. Agerinia roselli from Les Saleres. IPS-2543, left mandible fragment with P3 and P4 in New dental material and redescription of Agerinia roselli (Primates, Adapiformes) from Les Saleres (early Eocene, NE Iberian Peninsula)
FIGURE 2. Agerinia roselli from Les Saleres. IPS-2543, left mandible fragment with P3 and P4 in occlusal (1), buccal (3), lingual (5), and mesial (7) views; enlarged images of mesial roots of the same specimen in occlusal (2), buccal (4), lingual (6), and mesial (8) views; white arrows indicate the position of the most mesial root; black arrows indicate the position of the root immediately mesial with respect to the P3. Scale bar represents 3 mm in both cases.
FIGURE 3. Agerinia roselli from Les Saleres. IPS-82793, isolated right M1 in New dental material and redescription of Agerinia roselli (Primates, Adapiformes) from Les Saleres (early Eocene, NE Iberian Peninsula)
FIGURE 3. Agerinia roselli from Les Saleres. IPS-82793, isolated right M1 in occlusal (1), buccal (2) and lingual (3) views. IPS-82816, isolated right M1 in occlusal (4), buccal (5) and lingual (6) views. IPS-2542, left mandible fragment with M2 in occlusal (7), buccal (8) and lingual (9) views. IPS-82794, right mandible fragment with M2 in occlusal (10), buccal (11) and lingual (12) views. IPS-1981, holotype, left mandible fragment with M2 and M3 in occlusal (13), buccal (14) and lingual (15) views. IPS-2541, right mandible fragment with M2 and M3 in occlusal (16), buccal (17) and lingual (18) views. IPS-82795, right mandible fragment with M3 in occlusal (19), buccal (20) and lingual (21) views. IPS- 82790, left mandible fragment preserving part of the ramus mandibularis and a fragment of the M3 in occlusal (22), buccal (23) and lingual (24) views. Scale bar represents 3 mm.
Fig. 3. Sinohippus sampelayoi, IPS 2001 in Presence of the Asian horse Sinohippus in the Miocene of Europe
Fig. 3. Sinohippus sampelayoi, IPS 2001. Fragment of left hemimandible with p4, m1 and m2. In occlusal (A), lingual (B), and buccal (C) views.
Raw image files in Fig. S1E associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains raw ND2 files shown in Fig. S1E associated with: https://doi.org/10.1101/2024.09.24.614704</p>
Raw image files of uncropped western blots associated with "Endo-IP and Lyso-IP Toolkit for Endolysosomal Profiling of Human Induced Neurons"
<p>This entry contains uncropped blots for Fig. 1C, S1C, S1D, 2I, and S3B for: https://doi.org/10.1101/2024.09.24.614704</p>
Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 2. Metacheiromys marshii, AMNH 131777, drawing of basicranium in ventral view with isosurface from CT scans of left petrosal inserted (compare with Simpson 1931: fig. 7). Much of the mastoid exposure on the specimen's left side is damaged. Numbers 1 to 4 indicate depressions that based on the right side include a thin layer of entotympanic; 1 to 3 are between petrosal and basioccipital and 4 is petrosal only. The white arrow in the lower left passes through a canal between the petrosal and exoccipital for the auricular branch of the vagus nerve. Abbreviations: abX – grooves and foramina for auricular branch of vagus nerve, as – alisphenoid, astp – alisphenoid tympanic process, bo – basioccipital, bs – basisphenoid, eam – roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, es – epitympanic sinus of squamosal, fm – foramen magnum, fo – foramen ovale, gf – glenoid fossa, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, mtc – musculotubal canal, oc – occipital condyle, pa – porus acousticus (hidden), pas – parasphenoid, pgp – postglenoid process, pr – promontorium of petrosal, ps – presphenoid, smf – stylomastoid foramen, sof – superior orbital fissure, sq – squamosal, tca – tympanic canaliculus, th – tympanohyal, tm – tubular external acoustic meatus.
Text-fig. 4. Metacheiromys marshii, AMNH 131777, basicranial isosurface from CT scans in oblique posteroventral view: left petrosal in blue and small piece of left entotympanic in red. The white arrow on the left petrosal passes through a canal traversed by the tympanic nerve. Abbreviations: abX – foramen for auricular branch of vagus nerve, aptt – anteroventral process of tegmen tympani, as – alisphenoid, bo – basioccipital, bs – basisphenoid, ctp – caudal tympanic process, eam – squamosal roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, oc – occipital condyle, pcf – posterior carotid foramen, pp – paroccipital process of petrosal, pr – promontorium of petrosal, ptp – posttympanic process of squamosal, smf – stylomastoid foramen, sq – squamosal, stf – stapedial artery foramen, tca – tympanic canaliculus, th – tympanohyal. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 4. Metacheiromys marshii, AMNH 131777, basicranial isosurface from CT scans in oblique posteroventral view: left petrosal in blue and small piece of left entotympanic in red. The white arrow on the left petrosal passes through a canal traversed by the tympanic nerve. Abbreviations: abX – foramen for auricular branch of vagus nerve, aptt – anteroventral process of tegmen tympani, as – alisphenoid, bo – basioccipital, bs – basisphenoid, ctp – caudal tympanic process, eam – squamosal roof of external acoustic meatus, ec – ectotympanic, en – entotympanic, eo – exoccipital, hf – hypoglossal foramen, ips – foramen for inferior petrosal sinus, ljf – lateral jugular foramen, me – mastoid exposure of petrosal, mjf – medial jugular foramen, mt – muscular tubercle, oc – occipital condyle, pcf – posterior carotid foramen, pp – paroccipital process of petrosal, pr – promontorium of petrosal, ptp – posttympanic process of squamosal, smf – stylomastoid foramen, sq – squamosal, stf – stapedial artery foramen, tca – tympanic canaliculus, th – tympanohyal.
Post-remediation evaluation of contaminated site using geophysical methods: IP
<p>Five IP profiles were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.