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Figure 10 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 10 Lupaeus longisetus (Corpuz-Raros), male legs: a – I; b – II; c – III; d – IV. Scale bar 50 µm.
Figure 9 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 9 Lupaeus longisetus (Corpuz-Raros), female legs: a – I; b – II; c – III; d – IV. Scale bar 50 µm.
Figure 8 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 8 Lupaeus longisetus (Corpuz-Raros), female idiosoma: a – dorsum; b – venter. Scale bar 100 µm.
Figure 6 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 6 Dactyloscirus trifidus Corpuz-Raros, male: a – palp; b – chelicera; c – leg I, genu to tarsus; d – leg II, genu to tarsus. Female, genu to tarsus of all legs: e – leg I; f – leg II; g – leg III, h – leg IV. Scale bar 50 µm.
Figure 5 in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 5 Dactyloscirus trifidus Corpuz-Raros, male idiosoma – a. dorsum; b. venter. Scale bar 100 µm.
Figure 4 Cunaxa minidiscondyla n in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 4 Cunaxa minidiscondyla n. sp., tritonymph: a – venter of idiosoma; b – leg I (trochanter to base of tarsus); c – leg II; d – leg III; e – leg IV. Scale bar 100 µm.
Figure 3 Cunaxa minidiscondyla n in Additional contributions to the knowledge of Philippine predatory mites mainly of the subfamilies Cunaxinae and Cunaxoidinae (Acari: Prostigmata: Cunaxidae)
Figure 3 Cunaxa minidiscondyla n. sp., female: a – Leg I; b – Leg II; c – Leg III; d – Leg IV. Scale bar 100 µm.
Figure 4 Hardybodes minutus n in Contribution to the knowledge of the oribatid mite genus Hardybodes (Acari, Oribatida, Carabodidae) with description of a new species from the Philippines
Figure 4 Hardybodes minutus n. sp.: A – trochanter, femur and genu of leg I, right, antiaxial view; B – leg II (trochanter partially covered by pedotectum II), right, antiaxial view; C – leg III except tarsus, left, antiaxial view; D – leg IV, left, antiaxial view. Scale bar 15 μm.
Figure 1 Hardybodes minutus n in Contribution to the knowledge of the oribatid mite genus Hardybodes (Acari, Oribatida, Carabodidae) with description of a new species from the Philippines
Figure 1 Hardybodes minutus n. sp.: A – dorsal view; B – ventral view (legs except trochanters III, IV not illustrated). Scale bar 45 μm.
Figure 3 Hardybodes minutus n in Contribution to the knowledge of the oribatid mite genus Hardybodes (Acari, Oribatida, Carabodidae) with description of a new species from the Philippines
Figure 3 Hardybodes minutus n. sp.: A – prodorsum, frontal view; B – posterior view; C – subcapitulum, ventral view; D – palp, right, antiaxial view; E – chelicera, left, paraxial view. Scale bar 45 μm (A, B), scale bar 15 μm (C–E).
Figure 2 Hardybodes minutus n in Contribution to the knowledge of the oribatid mite genus Hardybodes (Acari, Oribatida, Carabodidae) with description of a new species from the Philippines
Figure 2 Hardybodes minutus n. sp.: A – anterior part of body, lateral view (legs except trochanter III not illustrated); B – posterior part of body, lateral view (legs except trochanter IV not illustrated). Scale bar 45 μm.
Data for Applying Old Knowledge in Primary Study Selection
<p>Three data sets (Hall, Wahono, Danijel) generated from existing systematic literature review publications.</p> <p>Hall.csv is generated from Hall, et al., "A systematic literature review on fault prediction performance in software engineering", 2012.</p> <p>Wahono.csv is generated from Wahono, et al., "A systematic literature review of software defect prediction: research trends, datasets, methods and frameworks", 2015.</p> <p>Danijel.csv is generated from Radjenović, et al., "Software fault prediction metrics: A systematic literature review", 2013.</p> <p>Each data set has two subset, which are derived from the original set by publication years. E.g. Hall2007-.csv contains examples in Hall.csv published before and in 2007, while Hall2007+.csv contains examples published after 2007.</p> <p>These data sets are used in our the newly submitted paper: "Testing Reading Tactics for Automated Reading Assistance: Is it Useful to Apply Old Knowledge?".</p>
Traditional Chinese Medicine Multidimensional Knowledge Graph
<h3>Overview of the Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</h3> <p>The <strong>Traditional Chinese Medicine Multi-dimensional Knowledge Graph (TCM-MKG)</strong> is a comprehensive, open-source data platform developed by Jingqi Zeng in November 2024. This platform aims to integrate and standardize a vast array of data from multiple sources, encompassing both traditional Chinese medicine (TCM) and modern biomedical sciences. By organizing and linking this diverse information, TCM-MKG acts as a bridge that connects the ancient wisdom of TCM with contemporary medical research and applications.</p> <h3>Key Features and Objectives:</h3> <ul> <li> <p><strong>Multi-source Data Integration</strong>: TCM-MKG consolidates data from over 30 authoritative resources, covering a broad spectrum of topics, including TCM terminology, Chinese patent medicines (CPM), Chinese herbal pieces (CHP), natural products (NP), chemical components, disease targets, and more. These data sources are carefully curated and interlinked, ensuring a rich, multi-dimensional view of TCM in relation to modern biomedical research. The platform incorporates data from reputable databases such as DrugBank, BioGRID, DisGeNET, STRING, and many others, ensuring that the TCM knowledge is not only expansive but also scientifically robust and cross-referenced with global biomedical standards.</p> </li> <li> <p><strong>Standardized Design for Global Interoperability</strong>: TCM-MKG adheres to international data standards and integrates with widely-used global medical classification systems such as ICD-11, UMLS, MeSH, and DOID. This ensures that the platform’s data is globally comparable and facilitates easy integration with international research efforts, promoting collaboration and knowledge exchange across the fields of TCM and modern medicine.</p> </li> <li> <p><strong>Open Source and Collaborative</strong>: In line with its mission to enhance transparency and accessibility, TCM-MKG is open-sourced in a structured tabular format. This allows researchers worldwide to freely access, contribute to, and expand upon the data, fostering interdisciplinary collaboration and accelerating innovation in both TCM research and modern medicine.</p> </li> <li> <p><strong>Advanced Analytical Capabilities</strong>: By leveraging the power of knowledge graph technology and graph-based intelligence algorithms, TCM-MKG supports deep data mining and relational reasoning. Researchers can uncover hidden associations between TCM components, diseases, and targets, providing insights into the mechanisms of herbal interactions and offering new pathways for drug discovery and therapeutic research.</p> </li> </ul> <h3>Personal Research Application:</h3> <p>Using the TCM-MKG platform, I conducted a study titled <strong>"Graph Neural Networks for Quantifying Compatibility Mechanisms in Traditional Chinese Medicine."</strong> This research applied advanced graph intelligence algorithms to quantitatively assess the compatibility mechanisms of Chinese herbal formulas (CHF). The study provides fresh insights into the underlying principles of TCM herbal combinations.</p> <p>This research has been published:</p> <p><strong>Zeng, J., & Jia, X. (2025). Quantifying compatibility mechanisms in traditional Chinese medicine with interpretable graph neural networks. <em>Journal of Pharmaceutical Analysis</em>, 101342. <a href="https://doi.org/10.1016/j.jpha.2025.101342">https://doi.org/10.1016/j.jpha.2025.101342</a></strong></p> <p>The code and methodology for this research have been open-sourced and are available on <a href="https://github.com/ZENGJingqi/GraphAI-for-TCM" target="_new" rel="noopener">GitHub</a>.</p> <h3>Acknowledgments:</h3> <p>This work benefited from the integration of data from numerous open-access and authoritative databases. We acknowledge the valuable contributions of resources such as DrugBank, BindingDB, BioGRID, DisGeNET, and many others. These datasets provided essential insights into TCM, modern drug chemistry, genetics, diseases, and related fields, forming the foundation for the traditional Chinese medicine multi-dimensional knowledge graph (TCM-MKG) used in this study. Furthermore, we utilized the PSICHIC model (https://github. com/huankoh/PSICHIC) to analyze the binding interactions between components and targets. Full citations for these resources are included.</p> <h3>Contact Information:</h3> <p>For further inquiries or more detailed information, please feel free to contact:<br><strong>Email</strong>: <a rel="noopener">zjingqi@163.com</a></p> <p> </p>
Supplementary material to: Russian verbal aspect and the activation of event knowledge: Processing typical and atypical location adverbials in perfective and imperfective sentences
<p>Supplementary material for a self-paced reading experiment:</p> <ol> <li>Material: contains the verbal stimuli (experimental and filler sentences)</li> <li>raw_data.zip: E-Prime output for all 50 participants (tab-separated txt-files)</li> <li>spr_aspect_respinf.txt: Basic information on the participants; tab-separated txt-file</li> <li>spr_aspect_preprocessing_subm2_fin.R: data preprocessing and calculation of correct responses per speaker in R; R script<br>Input: <br>- Files from the "raw_data"-folder<br>- spr_aspect_respinf.txt<br>Output: <br>- spr_aspect_respinf_corr_resp.txt: same as spr_aspect_respinf.txt + number of correct responses per participant; tab-separated txt-file<br>- spr_aspect_all.txt: relevant data from all participants in one file; tab-separated txt-file<br>- spr_aspect_all_without-outlier.txt: same as spr_aspect_all.txt, but outliers set to NA; tab-separated txt-file</li> <li>spr_aspect_figures-stats_subm2_fin.R: plots figures and calculates statistics (descriptive statistics and GLMM) in R, R script<br>Input: <br>- spr_aspect_all_without-outlier.txt<br>Output:<br>- spr_aspect_avg.txt: Mean RT and SD per condition; tab-separated txt-file<br>- Figures</li> </ol>
ParliamentSampo Knowledge Graph
<p>The ParliamentSampo Knowledge Graph includes data regarding Finnish Parliamentary debates and actors. The RDF data has been converted using data from the Parliament of Finland's open data services and Wikidata.</p> <p>The Knowledge Graph contains harmonized data of</p> <ol> <li>speeches from the plenary sessions of the Finnish Parliament 1907–2024, and</li> <li>members and organizations of the Parliament.</li> </ol> <p>The data model is designed for representing speeches, interruptions, items (on agenda), documents and other aspects related to plenary session speeches and minutes as well as member of the parliament and biographical information about them focusing on their political career.</p> <p>This dataset is available on a public SPARQL endpoint (<a href="http://ldf.fi/semparl/sparql"><em>http://ldf.fi/semparl/sparql</em></a>).</p> <p>To test and demonstrate its usefulness, this Knowledge Graph is in use in the semantic portal <a href="https://parlamenttisampo.fi/">ParliamentSampo</a>, explained in more detail in the <a href="https://seco.cs.aalto.fi/projects/semparl/en/">project page</a>.</p> <p>The Knowledge Graph can be downloaded also as CSV and XML files. See the dataset page on <a href="https://www.ldf.fi/dataset/semparl">LDF.fi</a> for more details.</p> <div> <div><strong>Version history</strong></div> <ul> <li>1.0.0, February 2023: Initial public release</li> <li>1.0.1, April 2024: README.md addition</li> <li>1.1.0, December 2024: added speeches from end of the year 2022, parliamentary session 2023, plenary sessions 112/1999, 120/1999 and 54/2016; changes to speeches in the plenary session 118/1999; fixes to "group of speaker" of speeches</li> <li>1.2.0, February 2025: added speeches from the parliamentary session 2024</li> <li>1.2.1, June 2025: fixes to speeches in plenary sessions 86–132/1999 (In dataset versions 1.1.0 and 1.2.0 some speeches were erroneously mixed: the same speech id had content of two speeches. This was due to processing the source data of the plenary sessions both from PDF files and HTML files. The current data on these plenary sessions is based only on the HTML source data.)</li> </ul> </div>
The Yelp Collaborative Knowledge Graph
<p>This is the The Yelp Collaborative Knowledge Graph (YCKG) - a transformation of the Yelp Open Dataset into RDF format using Y2KG. </p> <p>The full YCKG dataset can be found in <code>yelp.ttl.gz</code> and <code>yckg.tar.xz</code></p> <p><strong>Paper Abstract</strong></p> <p>The Yelp Open Dataset (YOD) contains data about businesses, reviews, and users from the Yelp website and is available for research purposes. This dataset has been widely used to develop and test Recommender Systems (RS), especially those using Knowledge Graphs (KGs), e.g., integrating taxonomies, product categories, business locations, and social network information. Unfortunately, researchers applied naive or wrong mappings while converting YOD in KGs, consequently obtaining unrealistic results. Among the various issues, the conversion processes usually do not follow state-of-the-art methodologies, fail to properly link to other KGs and reuse existing vocabularies. In this work, we overcome these issues by introducing Y2KG, a utility to convert the Yelp dataset into a KG. Y2KG consists of two components. The first is a dataset including (1) a vocabulary that extends Schema.org with properties to describe the concepts in YOD and (2) mappings between the Yelp entities and Wikidata. The second component is a set of scripts to transform YOD in RDF and obtain the Yelp Collaborative Knowledge Graph (YCKG). The design of Y2KG was driven by 16 core competency questions. YCKG includes 150k businesses and 16.9M reviews from 1.9M distinct real users, resulting in over 244 million triples (with 144 distinct predicates) for about 72 million resources, with an average in-degree and out-degree of 3.3 and 12.2, respectively.</p> <p><strong>Links</strong></p> <p>Latest GitHub release: <a href="https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph">https://github.com/MadsCorfixen/The-Yelp-Collaborative-Knowledge-Graph/releases/latest</a></p> <p>PURL domain: <a href="https://purl.prod.archive.org/domain/yckg">https://purl.archive.org/domain/yckg</a></p> <p><strong>Files</strong></p> <ul> <li>Graph Data Triple Files <ul> <li><code>yelp.ttl.gz</code> full dataset</li> <li><code>yckg.tar.xz</code> full dataset</li> </ul> </li> <li>One sample file for each of the Yelp domains (Businesses, Users, Reviews, Tips and Checkins), each containing 20 entities. <ul> <li><code>yelp_schema_mappings.nt.gz</code> containing the mappings from Yelp categories to Schema things.</li> <li><code>schema_hierarchy.nt.gz</code> containing the full hierarchy of the mapped Schema things.</li> <li><code>yelp_wiki_mappings.nt.gz</code> containing the mappings from Yelp categories to Wikidata entities.</li> <li><code>wikidata_location_mappings.nt.gz</code> containing the mappings from Yelp locations to Wikidata entities.</li> </ul> </li> <li>Graph Metadata Triple Files <ul> <li><code>yelp_categories.ttl</code> contains metadata for all Yelp categories.</li> <li><code>yelp_entities.ttl</code> contains metadata regarding the dataset</li> <li><code>yelp_vocabulary.ttl</code> contains metadata on the created Yelp vocabulary and properties.</li> </ul> </li> <li>Utility Files <ul> <li><code>yelp_category_schema_mappings.csv</code>. This file contains the 310 mappings from Yelp categories to Schema types. These mappings have been manually verified to be correct.</li> <li><code>yelp_predicate_schema_mappings.csv</code>. This file contains the 14 mappings from Yelp attributes to Schema properties. These mappings are manually found.</li> <li><code>ground_truth_yelp_category_schema_mappings.csv</code>. This file contains the ground truth, based on 200 manually verified mappings from Yelp categories to Schema things. The ground truth mappings were used to calculate precision and recall for the semantic mappings.</li> <li><code>manually_split_categories.csv</code>. This file contains all Yelp categories containing either a & or /, and their manually split versions. The split versions have been used in the semantic mappings to Schema things.</li> </ul> </li> </ul>
Video tutorial for creating uploads on EFSA's Knowledge Junction community of Zenodo
<p>The Knowledge Junction is a curated, open repository for the exchange of evidence and supporting materials used in food and feed safety risk assessments, with the goal of improving transparency, reproducibility and evidence reuse. The content of this repository can be used by EFSA's panels and working groups and any other interested parties when preparing for new risk assessments. </p> <p>The video tutorial describes the process of creating a new upload along with filling out the metadata based on the requirements for publishing objects in Knowledge Junction. </p> <p><strong>Objects suitable for submission to the repository</strong></p> <ul> <li>Objects which already have a DOI should <strong>not</strong> be published in the repository</li> <li>Objects which are subject to copy right restrictions should <strong>not</strong> be published in the repository</li> <li>All other evidence and supporting materials of relevant for food and feed safety will be accepted if the metadata provided is completed according to the instructions below</li> </ul>
Data from: foraging and the importance of knowledge in Pemba, Tanzania: implications for childhood evolution
<p>Childhood is a period of life unique to humans. Childhood may have evolved through the need to acquire knowledge and subsistence skills. In an effort to understand the functional significance of childhood, previous research examined increases with age in returns to foraging across food resources. Such increases could be due to changes in knowledge, or other factors such as body size or strength. Here we attempt to unpack these age-related changes. First we estimate age-specific foraging returns for two resources. We then develop non-linear structural equation models to evaluate the relative importance of ecological knowledge, grip strength, and height in a population of part-time children foragers on Pemba island, Tanzania. We use anthropometric measures, estimates of ecological knowledge, and behavioral observations for 63 individuals across 370 foraging trips. We find slower increases in foraging returns with age for trap hunting than for shellfish collection. We do not detect any effect of individual knowledge on foraging returns, potentially linked to information-sharing within foraging parties. Producing accurate estimates of the distinct contribution of specific traits to an individual's foraging performance constitutes a key step in evaluating different hypotheses for the emergence of childhood.</p>
Figs 45-50 in New data on the Oriental Xantholinini. XXIV. Nine new taxa of Paratesba from Danum Valley, Sabah, and dichotomic key of the bornean species (Coleoptera, Staphylinidae). 221° contribution to the knowledge of the Staphylinidae
Figs 45-50: Aedeagus, tergite and sternite of male genital segment of Paratesba fluvialis nov.sp. (45-47); P. lithocarpi nov.sp. (48-50).
Figs 11-18 in New data on the Oriental Xantholinini. XXIV. Nine new taxa of Paratesba from Danum Valley, Sabah, and dichotomic key of the bornean species (Coleoptera, Staphylinidae). 221° contribution to the knowledge of the Staphylinidae
Figs 11-18: Head and pronotum (right half omitted) of Paratesba minuta nov.sp. (11-12); head (right half omitted) and anterior angle of pronotum of Paratesba rougemonti nov.sp. (13-14); P. rubescens nov.sp. (15-16); P. silvestris nov.sp. (17-18).
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.