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11,684 results for “relationships”
Concentration–discharge relationships of chlorophyll describe the origin and fluxes of river algae across ecoregions
Since 2017, the National Ecological Observatory Network (NEON) program provides an extensive network of automated instruments and field sampling at 34 different aquatic sites across the U.S. NEON data provides a rare opportunity to combine high-frequency measurements from sensors with physical samples collected at multiples sites from different biomes. Such combination of data is frequently available only for 1, or few, sites. NEON provides the necessary data to translate some of the methods and research previously applied to single sites to a large spatial and temporal scale. Our approach in this study was to obtain all the available and reliable data from NEON aquatic sites, and analyze high frequency measurements of Chlorophyl a (Chl a) concentration and turbidity during high flows with a common analytical procedure. Then, we relate these high flow patterns to watershed conditions and algal communities to elucidate the controls on river phytoplankton communities and fluxes. The data provided here summarizes multiple storm characteristics and C-Q metrics (hysteresis index) for Chl a and turbidity for several high flow events at 26 different NEON stream and river sites.
Precipitation-productivity relationships in desert grassland: a test of the double asymmetry hypothesis.
The purpose of this data package is to provide the derived data and R code for analyses presented in the manuscript by Collins et al. Knowing the relationship between precipitation (PPT) and aboveground net primary productivity (ANPP) is essential for understanding and modeling the global carbon cycle. Across grassland to forest gradients, the PPT-ANPP relationship is well-defined and non-linear. Temporal patterns within a site over time, however, are more variable than spatial patterns and nearly always linear. Linear relationships, however, are inconsistent with positive asymmetry occurring when the increase in ANPP in a wet year is greater than the decline in a dry year. The double asymmetry model predicts that concave down non-linearities will occur when extreme high and low PPT years are included in a time series. We used long-term ANPP data from ambient plots, plus rainfall addition and reduction experiments to test the predictions of the double asymmetry model. By combining experimental drought, plus water and nitrogen addition experiments we found some support for the double asymmetry model. However, the response was concave up not down under high precipitation coupled with nitrogen addition. By experimentally extending the range of monsoon precipitation we generated a significant although weak, non-linear PPT-ANPP relationship, but only when nutrient limitation was alleviated. Our results demonstrate that multiple interacting factors govern the PPT-ANPP relationship within a site over time.
Dataset of "Activity-stability relationship in magnetron co-sputtered bimetallic catalysts for proton exchange membrane fuel cells"
<p>In the present study, magnetron sputtered PtxM100-x (M = Co, Cu, Y; x = 25, 50, 75 and 100) bimetallic alloys were investigated as PEMFC cathodes. Accurate composition control enabled a systematic study of the correlation between alloy composition, activity, and stability. The catalysts underwent thorough characterization, employing a diverse portfolio of characterization techniques such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy and cyclic voltammetry. The activity of all investigated alloys was tested directly in a fuel cell device, while stability was assessed through potentiodynamic cycling in a half-cell. <br>The activity-stability index, considering experimental results for both activity and stability, was calculated and compared for all investigated catalysts. All alloys exhibited a volcano-type trend in activity-stability index as a function of the concentration of alloying element with peaks observed at Pt50Co50, Pt50Cu50 and Pt75Y25 for respective alloys, surpassing that of monometallic platinum. Overall, Pt50Co50 emerged as a catalyst with the highest activity-stability ratio.</p>
Dataset of the article "Bayesian phylogenetics illuminate shallower relationships in Trans-Himalayan languages in Tibet-Arunachal area"
<p>This repository archives the dataset of the article "Bayesian phylogenetics illuminate shallower relationships in Trans-Himalayan languages in Tibet-Arunachal area". The cognate annotation of Tshangla, Kho-Bwa, Hrusish, Mishmic, and Tani languages were done by us. The cognate decision on the other languages was annotated by Sagart et al. (2019). Please use the following information to cite our work: <br> Wu, M.-S, Bodt, T. A, Tresoldi, T. (2022). Bayesian phylogenetics illuminate shallower relationships Trans-Himalayan languages in the Tibet-Arunachal area. Linguistics of the Tibeto-Burman Area. [forthcoming]</p>
An Empirical Evaluation of the Relationship between Technical Debt and Software Security
<p>This dataset contains the static analysis results of 50 open source software applications retrieved from Github. The results were produced by using SonarQube, PMD, CKJM Extended and Findbugs tools. Further details can be found in the relevant publication: </p> <ul> <li>Siavvas, M., Tsoukalas, D., Janković, M., Kehagias, D., Chatzigeorgiou, A., Tzovaras, D., Aničić, N., Gelenbe, E. <em>An Empirical Evaluation of the Relationship between Technical Debt and Software Security</em>. In: Konjović, Z., Zdravković, M., Trajanović, M. (Eds.) ICIST 2019 Proceedings Vol.1, pp.199-203, 2019</li> </ul>
LAGOS-NE Shallow Lakes: a dataset of lake variables and multi-scaled ecological context variables used to predict and compare trophic status and TP:CHLa relationships between shallow and non-shallow lakes in the Upper Midwest and Northeastern United States.
We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes.
Sample data for evaluating Scholix relationship SubTypes for linked data publications
<p>Scholix links provide a standardized framework for establishing connections between research publications and their associated datasets or related data publications, thereby fostering improved discoverability, reusability, and reproducibility of research data.<br><br>This dataset aims to facilitate the evaluation of the degree of relatedness between literature publications and their associated linked data publications. It comprises 3,600 tuples, each representing a pair of a literature publication (A) and a linked data publication (B) connected through Scholix links.</p> <p><strong>Dataset Contents</strong></p> <p>1. <em>Scholix Links</em>: The dataset includes 450 Scholix links for each of the eight most frequently observed relationship types between literature and linked data publications, as expressed in the "RelationshipType - SubType" field of Scholix metadata:</p> <ul> <li>IsSupplementedBy</li> <li>IsReferencedBy</li> <li>IsRelatedTo</li> <li>References</li> <li>Documents</li> <li>Cites</li> <li>IsSupplementTo</li> <li>IsCitedBy</li> </ul> <p>2. <em>Publication Metadata</em>: In addition to the Scholix links, the dataset is augmented with metadata for each publication, including titles and author names. This metadata was harvested from the Crossref and DataCite APIs.</p> <p>3. <em>Relatedness Measures</em>: To estimate the degree of relatedness between literature and linked data publications, the dataset includes numeric measures for the similarity of authors' lists and publication titles for each tuple.</p> <p><strong>Data Sources</strong></p> <ul> <li>Scholix links were harvested from the Scholexplorer API.</li> <li>Publication metadata (titles and author names) were obtained from the Crossref and DataCite APIs.</li> </ul> <p>This dataset can be valuable for researchers and practitioners working on linked literature and data publications, evaluating the quality of existing links, or developing algorithms to identify related publications across different domains.</p>
Inter-Chemical Correlation results for the study: HHEARx2016-1449 (Environmental phenols and pesticide levels in relationship to autism)
Title: Environmental phenols and pesticide levels in relationship to autism <br>Species: Homo sapiens <br>Number of samples: 842 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=7 <br>
Sex-specific relationships between urbanization, parasitism, and plumage coloration in house finches
Historically, studies of condition-dependent signals in animals have been male-centric, but recent work suggests that female ornaments can also communicate individual quality (e.g., disease state, fecundity). There also has been a surge of interest in how urbanization alters signaling traits, but we know little about if and how cities affect signal expression in female animals. We present data of carotenoid-based plumage coloration and coccidian (Isospora spp.) parasite burden in desert and city populations of house finches Haemorhous mexicanus to examine links between urbanization, health state, and feather pigmentation in males and females. In earlier work, we showed that male house finches are less colorful and more parasitized in the city, and we again detected such patterns in this study for males; however, urban females were less colorful, but not more parasitized, than rural females. Moreover, contrary to rural populations, we found that urban birds (regardless of sex) with larger patches of carotenoid coloration were also more heavily infected with coccidia. These results show that urban environments can disrupt condition-dependent color expression and highlight the need for more studies on how cities affect disease and signaling traits in both male and female animals.
SBC LTER: Reef: Feeding relationships for kelp forest species
These data are an assembled food web for all species sampled in the SBC LTER annual kelp forest community surveys. Relationships are taken from literature review on diets and consumption habits of target species, plus searches of theses, grey literature, and student report databases at major west coast libraries. Only feeding observations from studies that showed direct consumption or provided strong evidence of a direct feeding relationship between two taxa were used. Our final food web was revised and verified after consultation with several experts in the field of kelp forest community ecology. See Methods section for complete information on cited literature and contributing experts. These data represent Table S1 for Appendix S1 for a published paper: <ulink url="http://dx.doi.org/10.1111/j.1365-2486.2011.02409.x">Byrnes, J. E., D. C. Reed, B. J. Cardinale, K. C. Cavanaugh, S. J. Holbrook and R. J. Schmitt. 2011. Climate-driven increases in storm frequency simplify kelp forest food webs. Global Change Biology, 17: 2513â24. doi: 10.1111/j.1365-2486.2011.02409.x</ulink>.
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</p>
Empirical relationship between calcium triplet equivalent widths and [Fe/H] using Gaia photometry
<p>I present a new empirical relationship for red giant branch stars between the overall metallicity of the star and the sum of equivalent widths of the near-infrared calcium triplet (CaT) spectral lines. This method takes advantage of the all-sky photometry and astrometry of the Gaia mission, and the archival spectra from 2050 red giant branch stars from 18 globular clusters (-0.69>[Fe/H]>-2.44) acquired with the Anglo-Australian Telescope's AAOmega spectrograph.</p> <ul> <li><cluster_name>.tar.gz <ul> <li>Raw and reduced spectra for 18 globular clusters (NGC104, NGC6752, NGC6809, NGC288, NGC7099, NGC362, NGC6218, NGC4590, IC4499, NGC1904, ESO452-SC06, ESO280-SC12, NGC1851, NGC6624, NGC2298, Pal 5, NGC5024, Terzan 8, NGC5053)</li> <li>Lists of likely members</li> <li>Measured equivalent widths and radial velocities</li> </ul> </li> <li>code.tar.gz <ul> <li>running.py – calculates the equivalent widths and radial velocities of the stars from the spectra</li> <li>cat_new_params.py – calculates the best fitting empirical relationship</li> </ul> </li> <li>paper.tar.gz <ul> <li>The LaTeX source for the submitted RNAAS paper.</li> </ul> </li> </ul> <p> </p>
Data from: Consistent trait-environment relationships within and across tundra plant communities
<p>A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.</p> <p>Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (2021). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution</p> <p>These are the data and codes from Kemppinen et al. (2021).</p>
Genetic Relationships Between Terminal Shoot Length, Number of Flushes and Height in a Four-Year-old Progeny Test of Pinus brutia Ten.
<p><strong>Description of the data</strong></p> <p>A total of 188 plus trees were selected from eight natural seed stands of <em>Pinus brutia</em> in the Aegean region of Turkey. The number of trees selected per seed stand (provenance) varied between 7 to 53 trees. Open-pollinated seeds were collected from plus trees in 1998 and in 1999. In addition, six checklots consisting of bulk seeds from natural seed stands were included in the study to estimate genetic gain and link the progeny tests across different breeding zones in the Aegean region.</p> <p>Open-pollinated progeny tests were established at three locations in the Aegean region of Turkey (Hisaronu, Izmir, and Kinik) in March 2000. One-year-old bare-root seedlings were used in the study. Randomized complete block design with four-tree row plots was used in all sites. For each plus tree (female parent), about 72 half-sib progenies were planted across three test sites. The Hisaronu site had four blocks, while two other sites had seven blocks each. Each parent tree was represented by 16 half-sib progenies at the Hisaronu site and 28 progenies in the other two sites when the trials were planted. The spacing among seedlings was 2 x 3 m at each site. Each block was split into four sets (sets in replications) to accommodate a large number of trees, with checklots included in every set. In total, about 166 half-sib progenies and checklots were planted in each block.</p> <p>At the end of the first growing year after planting, survival was assessed. It was about 52% at the Hisaronu site. Dead seedlings at the Hisaronu site were replaced with 1083 two-year-old seedlings of the same families, which were grown in a nursery near Marmaris in the Aegean region. The other two sites had 91% (İzmir) and 94% (Kinik) survival. At age four after planting (2004), tree height (cm), terminal shoot length (cm), and the number of flushes were measured. In total, approximately 12100 trees were assessed across the three locations. </p>
Parental Engagement and Relationships (PEAR) in Early Childhood (EC). Impact study: Parents' responses
<p>Dataset with parents' quantitative responses collected within the impact study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC).</p> <p>The file (.sav) can be opened using IBM SPSS Software. The file is named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.</p>
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </p>
Great Ape Cerebral Aging - Expansion Relationship
<p>This contains data associated with the manuscript "The Uniqueness of Human Vulnerability to Brain Aging in Great Ape Evolution" https://www.biorxiv.org/content/10.1101/2022.09.27.509685v1</p> <p> </p> <p>The chimp.zip and IXI.zip contain data for the chimpanzee and human samples respectively. This includes the input matrix (subject x gray matter voxels), OPNMF bootstrap outputs, and OPNMF parcellaitons (2-40).</p> <p> </p> <p>The outputs.zip contains outputs from the analyses conducted for the manuscript using the code from https://github.com/viko18/GreatApe_Aging.</p> <p> </p> <p>The expansion_map.zip contains the modulated jacobians and templates following cross-species registration for the chimpanzee, baboon, and macaque templates.</p> <p> </p> <p>y_JunaChimp_brain.nii.gz is the deformtion field map from chimpanzee template space to human and iy_*.nii.gz is the inverse, so human to chimpanzee.</p> <p> </p> <p>Davi130_MNI_3mm_cortex.nii.gz is the avi130 chimpanzee parcellation in human MNI sapce.</p>
Dataset of the Marital and Parental Relationships of Western- and Central Europe between 1350-1550
<p>All the data is collected from WikiData manually, allowing for a critically reflection of the available data. In case of missing data this has been reconstructed when possible. The dataset includes the rulers and consorts of the following territories:</p><p>the Duchy of Anjou, the Kingdom of Aragon, the Archduchy of Austria, the Duchy of Auvergne, the Duchy of Bar, the Duchy/Electorate of Bavaria, the Duchy of Berg, the Kingdom of Bohemia, the Duchy of Bouillon, the Duchy of Bourbon, the Duchy of Brabant, the Margraviate of Brandenburg, the Duchy of Brittany, the Duchy of Burgundy, the Kingdom of Castile, the Duchy of Cleves, the Kingdom of Denmark, the Kingdom of England, the Duchy of Florence / Grand Duchy of Tuscany, the Kingdom of France, the Duchy/Archduchy of Further Austria, the Duchy of Guelders, the Duchy of Holstein-Gottorp, the Holy Roman Empire, the Kingdom of Hungary, the Duchy/Archduchy of Inner Austria, the Duchy of Jülich (Cleves-Berg), the Duchy of Limburg, the Grand Duchy of Lithuania, the Duchy of Lorraine, the Duchy/Archduchy of Lower Austria, the Duchy of Luxembourg, the Kingdom of Majorca, the Duchy of Milan, the Kingdom of Naples, the Kingdom of Navarre, the Kingdom of Norway, the Principality of Orange, the Electorate Palatinate, the Kingdom of Poland, the Duchy of Pomerania, the Kingdom of Portugal, the Kingdom of Sardinia, the Duchy of Savoy, the Electorate of Saxony, the Kingdom of Scotland, the Principality of Sedan, the Kingdom of Sicily, the Kingdom of Spain, the Kingdom of Sweden, the Duchy/Archduchy of Tyrol and the Duchy of Württemberg.</p><p> </p><p>1. Node and Edge lists of the <strong>marital</strong> relationships between rulers and consorts of Western- and Central Europe. This dataset also includes biographical and spatial information suitable for GIS.</p><p>2. Node and Edge list of the <strong>parental </strong>relationships to be used for Network Analysis.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
The phylogeny of ceutorhynchine weevils (Ceutorhynchinae, Curculionidae): mitogenome data improve the resolution of tribal relationships.
Open the record for dataset details and reuse information.
Disco Time: The Relationship Between Perceived Duration and Tempo in Music
<p>Data set from a study published in Music & Science.</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)
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DANDI Archive for NWB datasets
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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
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