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635 results for “Attributes”
A compendium and evaluation of taxonomy quality attributes
<p>This dataset contains the following data and source code and results.</p> <ul> <li>The definition of taxonomy quality attributes from Szopinski et al. and Usman et al. (szopinski_usman_analysis.xls)</li> <li>The results of the data extraction validation (data_extraction_validation.xls)</li> <li>The implementations of the robustness and conciseness measures (source_code.zip). Unpack also the model files in doc2vec_model.zip.</li> <li>The results of the taxonomy analysis (results_qualitative_attributes.xls and conciseness_robustness_results.zip)</li> <li>Detailed results of intruder nodes, calculated for the robustness measure (intruder_node_details.zip)</li> </ul>
Reproducible Authorship Attribution Benchmark Tasks
<p>Reproducible Authorship Attribution Benchmark Tasks (RAABT) consists of five closed-set authorship identification experiments.</p> <p>Each task features fixed train and test sets. Four of the five tasks have a test set consisting of writing samples on fixed topics, guaranteeing that test set examples do not overlap with training set examples in terms of subject matter. Data for all tasks is available for download without any restrictions.</p> <p>The file README.md contains a full description of the data.</p>
Machine learning-based evidence and attribution mapping of 100,000 climate impact studies - Data
<p>Data for the paper Machine learning-based evidence and attribution mapping of 100,000 climate impact studies</p> <p><strong>Document Metadata</strong></p> <p>0c_doc_info.csv contains basic document metadata for each document considered in our study</p> <p><strong>Predictions</strong></p> <p>In each predictions file, 1 refers to a document hand-labelled as belonging to a category, and 0 refers to a document hand-labelled as not belonging to a category. All values in between are predicted values, where for values greater than 0.5, a document is considered likely to belong to the given category.</p> <p>1_document_relevance.csv contains the predicted relevance of a document to the study.</p> <p>1_driver_predictions.csv contains the predicted climate driver of each document.</p> <p>1_impact_predictions.csv contains the predicted impact type of each document</p> <p><strong>Geographical data</strong></p> <p>Place_df.csv contains a row for each geographical entity automatically extracted from each study</p> <p>Study_gridcell_2.5.csv contains a row matching each study with each grid cell covered by the study’s smallest mentioned geographical entity</p> <p><strong>Merged data</strong></p> <p>2_study_da.csv contains a row for each study describing the aggregated detection and attribution characteristics of the grid cells the study refers to</p> <p>2_merged_da_data.csv contains a row for each grid cell describing the attribution categories and the number of weighted grid cells for each climate driver.</p>
Authorship Attribution - Twitter - 2019
<p>Dataset of tweets used for the research of authorship attribution of small messages. It was used in the work <a href="https://ieeexplore.ieee.org/document/8683747"><em>A Needle in a Haystack? Harnessing Onomatopoeia and User-specific Stylometrics for Authorship Attribution of Micro-messages</em></a> published at <a href="https://2019.ieeeicassp.org/"><em>2019 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP</em></a></p> <p> </p> <p>Format:</p> <p>A single file with one tweet ID per line. Total of 130,141,590 tweet IDs from more than 55,000 Twitter users.</p> <p>The tweets can be recovered from Twitter (hydrated) using the Twitter API or the code at <a href="https://github.com/theocjr/twitter-reader">twitter-reader</a>.</p>
Data from: Plant attributes interact with fungal pathogens and nitrogen addition to drive soil enzymatic activities and their temporal variation
<p>Nitrogen enrichment can alter soil communities and their functioning directly, via changes in nutrient availability and stoichiometry, or indirectly, by changing plant communities or the abundance of consumers. However, most studies have only focused on one of these potential drivers and we know little about the relative importance of the different mechanisms (changes in nutrient availability, in plant diversity or functional composition, or in consumer abundance) by which nitrogen enrichment affects soil functioning. In addition, soil functions could vary dramatically between seasons, however, they are typically measured only once during the peak growing season. We therefore know little about the drivers of intra-annual stability in soil functioning.</p> <p>In this study, we measured activities of β-glucosidase and acid phosphatase, two extracellular enzymes that indicate soil functioning. We did so in a large grassland experiment which tested the effects, and relative importance, of nitrogen enrichment, plant functional composition and diversity, and foliar pathogen presence (controlled by fungicide) on soil functioning. We measured the activity of the two enzymes across seasons and years to assess the stability and temporal dynamics of soil functioning.</p> <p>Overall β-glucosidase activity was slightly increased by nitrogen enrichment over time but did not respond to the other experimental treatments. Conversely, plant functional diversity, and interactions between plant attributes and fungicide application, were important drivers of mean acid phosphatase activity. The temporal stability of both soil enzymes was differently affected by two facets of plant diversity: species richness increased temporal stability and functional diversity decreased it; however, these effects were dampened when nitrogen and fungicide were added.</p> <p>Synthesis: The fungicide effects on soil enzyme activities suggest that foliar pathogens can also affect belowground processes and the interacting effect of fungicide and plant diversity suggests that these plant enemies can modulate the relationship between plant diversity and ecosystem functioning. The contrasting effects of our treatments on the mean versus stability of soil enzyme activities clearly show the need to consider temporal dynamics in belowground processes, to better understand the responses of soil microbes to environmental changes such as nutrient enrichment.</p>
Predicting the Install, Compile, and Test Attributes of a Library
<p>The task of selecting a third-party library is a common one, but it can be challenging due to the many factors that a developer has to consider. Previous research has mainly focused on specific attributes. In this paper, we take a comprehensive approach by identifying the most desirable attributes and examining how they are related to one another. Our work is split into three parts. First, we conduct a developer survey to rank their preferences on a set of 30 attributes from existing work, grouped into four categories: build, documentation, source code, and contribution. To confirm our results, in the second part of the study, we then mined 104,364 NPM libraries to find that the build (i.e., installation, compiling, and testing) of a library is important. Finally, in the third part of the study, we find that static source code attributes (i.e., number of files, repository size, and directories) correlate and predict the success of installing, building, and testing a library. Our work lays the groundwork on what attributes of a library are useful for predicting a successful build of a library.</p>
Data for: Attributes of CloudSat identified echo objects
<p>This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection.</p> <p>An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code.</p> <p>The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011).</p>
Callithrix aurita: Occurrence, allochthonous species and landscape attributes
<p><span>The</span> <span>buffy-tufted-ear marmoset (<em>Callithrix</em> <em>aurita</em>) is a small primate endemic to the Brazilian Atlantic Forest biome, and one of the 25 most endangered primates in the world, due to fragmentation, loss of habitat and invasion by allochthonous <em>Callithrix</em> species. Using occurrence data for <em>C. aurita</em> from published data papers, we employed model selection using Akaike Information Criterion corrected for small samples and cumulative AICc weight (w<sub>+</sub>) to evaluate whether fragment size, distance to fragments with allochthonous species, altitude, connectivity, and surrounding matrices influence the occurrence of <em>C. aurita</em> within its distributional range. Distance to fragments with <em>C. jacchus</em> (w<sub>+</sub> = 0.94) and non-vegetated areas (w<sub>+</sub> = 0.59) correlated negatively with <em>C. aurita</em> occurrence. Conversely, the percentage of agriculture and pasture mosaic (w<sub>+</sub> = 0.61) and the percentage of savanna formation (w<sub>+</sub> = 0.59) in the surrounding matrix correlated positively with <em>C. aurita</em> occurrence. The findings indicate that <em>C. aurita</em> is isolated in forest fragments surrounded by potentially inhospitable matrices, along with proximity of a more generalist and invasive species, thereby increasing the possibility of introgressive hybridization. The findings also highlighted the importance of landscape elements and allochthonous congeneric species for <em>C. aurita</em> conservation, besides indicating urgency for allochthonous species management. Finally, the approach used here can be applied to improve conservation studies of other endangered species, such as <em>C. flaviceps</em>, which is also endemic to the Brazilian Atlantic Forest and faces the same challenges.</span></p>
BioKG with attributes and decoupled benchmarks
<p>BioKG is a biomedical knowledge graph containing relationships between proteins, molecules, diseases, and others. It was originally proposed by Walsh et al. (2020) in <a href="https://dl.acm.org/doi/10.1145/3340531.3412776">"BioKG: A Knowledge Graph for Relational Learning On Biological Data"</a>.</p> <p>We enrich this dataset with the aim of incorporating multimodal data associated with biomedical entities:</p> <ul> <li>Proteins: Protein embeddings computed with <a href="https://github.com/agemagician/ProtTrans">ProtTrans</a> from aminoacid sequences</li> <li>Molecules: Molecule embeddings computed with <a href="https://github.com/kexinhuang12345/MolTrans">MolTrans</a> from SMILES representations</li> <li>Diseases: Textual descriptions retrieved from <a href="https://www.nlm.nih.gov/mesh/meshhome.html">MeSH</a></li> </ul> <p>Furthermore, we decouple the benchmarks provided by Walsh et al. from the edges in the knowledge graph, which ensures that there is no direct data leakage between the benchmarks and the triples used to train link prediction models.</p>
Predicting Mathematics Anxiety and Achievement: Unveiling the Significance of Student and Teacher Attributes through Hierarchical Linear Modeling
<p>This study aimed to determine the predictive power of student and teacher characteristics on students' math anxiety and achievement.</p>
APPENDIX 13 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
APPENDIX 13. — Islands with multi-long branched appearance in the Thousand Island Lake, China.
Plot attribute table of the Kunbaja online resource
<p>Plot attribute table of the QGIS database of the Kunbaja online resource model as dataset in the .xlsx file format</p>
Data for: Low mercury concentration in a Greenland glacial fjord attributed to oceanic sources
<p>Total mercury (THg) and methyl mercury (MeHg) concentrations measured in southeast Greenland between 10 August and 20 August 2021. The majority of samples were collected in Sermilik Fjord and on the continental shelf to the west of the fjord mouth (identified by CTD cast number; see below for corresponding data citation). A small number of samples were collected by hand from surface waters in Apuseeq Fjord, in a lake and river near the village of Tiilerilaaq (formerly known as Tiniteqilaq), and in a river near Tasiilaq. All samples are identified by the date, time, and lat/lon of collection.</p> <p>For further details about the sample collection and analysis, please see the methods section of the associated publication.</p> <p>Sermilik Fjord and continental shelf CTD data are available via the NSF Arctic Data Center (doi:10.18739/A2HD7NT9K).</p>
Avian species functional diversity and habitat use the role of forest structural attributes and tree diversity in the Midlands Mistbelt forests of KwaZulu-Natal, South Africa
<p><span>Forest transformation has major impacts on biodiversity and ecosystem functioning. Identifying the influence of forest habitat structure and composition on avian functional communities is important for conserving and managing forest systems. This study investigated the effect of forest structure and composition characteristics on bird species community structure, habitat use, and functional diversity in 14 Mistbelt forest patches of the Midlands of KwaZulu-Natal in South Africa. We surveyed bird communities using point counts. We quantified bird functional diversity for each forest patch using three diversity indices: functional richness, functional evenness, and functional divergence. We further assessed species-specific responses by focusing on three avian forest specialists, orange ground-thrush </span><span><em>Geokichla</em> <em>gurneyi</em></span><span>, forest canary </span><span><em>Crithagra</em> <em>scotops</em></span><span>, and Cape parrot </span><span><em>Poicephalus</em> <em>robustus</em></span><span>. We found that bird community and forest-specialist species responses to forest structure and tree species diversity differed. Also, forest structural complexity, canopy cover, and tree species richness were the main forest characteristics better at explaining microhabitat influence on bird functional diversity. Forest patches with relatively high structural complexity and tree species richness had higher functional richness. Different structural characteristics influenced habitat use by the three forest specialists. Tree species diversity influenced </span><em><span>C. scotops</span> </em><span>and</span><em><span> G. </span><span>gurneyi</span></em><span> positively, </span><span>while </span><em><span>P. robustus</span></em><span> responded negatively to forest patches with high tree species richness. </span><span>Our study showed that site-scale forest structure and composition characteristics are important for bird species richness and functional richness. Forest patches with high tree species diversity and structural complexity should be maintained to conserve forest specialists, bird species richness, and functional richness. </span></p>
Database-for-cross-country-regional-active-and-legacy-nutrient-source-attribution
<p>This dataset contains nutrient concentration (TN/NH3-N, TP) and water discharge data from Australia, China, Sweden, and the USA.<br>Each country's data is organized into subfolders based on the respective country.</p><p>Data Sources:<br>The data were collected from water quality monitoring agencies, research institutions, and public data sources in each respective country. The data sources for each country are as follows:<br>Australia: Retrieved from <a href="https://data.water.vic.gov.au/">https://data.water.vic.gov.au/</a><br>China: Retrieved from the Ministry of Ecology and Environment of the People's Republic of China (nutrient concentration) and the Hydrological Year Book (water discharge).<br>USA: Retrieved from <a href="https://doi.org/10.5066/P948Z0VZ">https://doi.org/10.5066/P948Z0VZ</a><br>Sweden: Retrieved from <a href="https://doi.org/10.5281/zenodo.7433379">https://doi.org/10.5281/zenodo.7433379</a></p><p>Data Formats and contents:<br>The data in each subfolder are stored in TXT or Excel formats.<br>TXT Files: These files are named by monitor station ID and include monitor time, nutrient data, water discharge data, and corresponding units.<br>Excel Files: These files contain information about the location of the monitoring stations and maps of the catchment areas.</p><p>To align with the source data, please be aware that the units for nutrient concentration and water discharge data may vary for each country.</p>
Baixada Santista 2020 event analysis - Attribution and Synopsis of Landslide Impacts from Precipitation
<p>Dataset used for the Baixada Santista 2020 event analysis during the Attribution and Synopsis of Landslide Impacts from Precipitation (ASLIP) Workshop as part of the Climate Science for Service Partnership Brazil. </p> <p>Notes:</p> <p>impact_data: data from land cover change (retrieved from https://brasil.mapbiomas.org/), socioeconomic impacts (https://s2id.mi.gov.br/) and susceptibility to landslides (https://www.cprm.gov.br/)</p> <p>df_rxmax_climat: data from HadGEM3 (15 members) and CPC for the climatological period of study</p> <p>df_rxmax_event: data from HadGEM3 (525 members) and CPC for the event occurred in March 2020</p> <p>pluviometer_analysis_historical: data processing scripts and precipitation observational data</p> <p>soilMoistureCPC_data: mean integrated soil moisture data from SMAP and precipitation data from CPC for the studied area</p>
A Clinical Study Assessing Critical Errors, Training/Teaching Time, and Preference Attributes of the ELLIPTA® Dry Powder Inhaler, in Comparison to Combinations of Dry Powder Inhalers Used to Provide T
ClinicalTrials.gov study NCT02982187. IPD Sharing: NO. Countries: 2. Publications: 1.
Study of Inhaler Device Attributes Investigating Critical and Overall Errors, Ease of Use, and Preference Between a Number of Inhaler Devices
ClinicalTrials.gov study NCT02184624. IPD Sharing: YES. Countries: 2. Publications: 1.
Optimization of Hip-exoskeleton Weight Attributes
ClinicalTrials.gov study NCT05120115. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Patient Satisfaction and Sensory Attributes Allergic Rhinitis Nasal Spray
ClinicalTrials.gov study NCT05146206. IPD Sharing: NO. Countries: 1. Publications: 5.
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Allen Brain Atlas
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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
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.