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125 results for “Data attribute”
IPBES Data Management Tutorials - Session 5.2: Tools to find and attribute DOIs
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>The session on <em>tools to find and attribute DOIs </em>covers fundamental background information on digital object identifiers and how to resolve and reserve them.</p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Ancillary data for article "The general formulation for runoff components estimation and attribution at mean annual time scale"
<p>The mean annual (1960-1990) precipitation and estimated model parameters wetting potential (Wp), vaporization potential (Vp) and upper limit of <span>the portion remaining after precipitation (</span>Up) of 312 catchments over China.</p>
Input data for: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>This Zenodo archive contains essential input datasets utilized in our <a href="https://doi.org/10.5194/essd-2023-112">research study</a> titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". </p><p>This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><p><strong>Datasets Included</strong>:</p><p><strong>CoDEC (Coastal Dataset for the Evaluation of Climate Impact)</strong>:</p><ul><li>This dataset is described in<a href="https://doi.org/10.3389/fmars.2020.00263"> Muis et al. (2020)</a></li><li><strong>cf_esl folder</strong>: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point.</li><li><strong>cf_tides folder</strong>: This folder holds data related to tidal elevation.</li><li><strong>coor_coastal.nc</strong>: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.</li></ul><ol><li><strong>HR (Hybrid Reconstructions)</strong>:<ul><li><strong>HybridRec_Upd0422.mat</strong>: This file contains data from the Hybrid Reconstructions dataset (<a href="https://doi.org/10.1038/s41558-019-0531-8">Dangendorf et al 2019</a>), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our <a href="https://doi.org/10.5281/zenodo.7771501">source code</a> named split_hr_dataset_to_stations.py.</li></ul></li></ol><p>We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.</p>
source_data_flood_attribution
<p>The dataset includes source data for the journal article:</p> <p>Inga Sauer, Ronja Reese, Christian Otto, Tobias Geiger, Sven Willner, Benoit Guillod, David Bresch, and Katja Frieler. “Climate Signals in River Flood Damages Emerge under Sound Regional Disaggregation,” <a href="https://doi.org/10.21203/rs.3.rs-37259/v1">10.21203/rs.3.rs-37259/v1</a></p> <p>It provides data data required for hazard and exposure modeling required for damage modeling provided in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).</p> <p>Hazard modeling: provided are spatially explicit flooded areas and flood depth on a 150arcsec resolution and discharge on a 0.25 degree resolution the file names follow the structure: variable_resolution_ghm_climateforcing_protectionstandard.nc (for flood depth (flddph) and flooded fraction (fldfrc)) and variable_ghm_climateforcing.nc</p> <p>Exposure modeling:</p> <p>The file gdp_1850_2100_150arcsec.nc contains yearly gridded-GDP on a 150 arcsec resolution converted to PPP 2005 USD. Between 2000 and 2010 there is a transition between observed GDP and the future socio-economic development scenario SSP2.</p> <p>The application of the datasets for damage modeling and for the reproduction of the article data is described here:</p> <p>https://github.com/ingajsa/flood_attribution_paper</p> <p>More information on flood modeling can be found at: 10.5281/zenodo.1241051</p> <p>For the gridded_GDP, see also: https://doi.org/10.5880/pik.2017.003</p>
Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
<p>Symbiosis with arbuscular mycorrhizal (AM) fungi improves plant nutrient capture from the soil, yet there is limited knowledge about the diversity, structure, functioning, and assembly processes of AM fungi-related microbial communities. Here, 16S rRNA gene sequencing and metagenomic sequencing were used to detect bacteria in the rhizosphere of <em>Lotus japonicus</em> inoculated with and without AM fungi, and the <em>L. japonicus</em> mutant <em>ljcbx</em> (defective in symbiosis) inoculated with AM fungi in southern grassland soil. Our results show that AM symbiosis significantly increased bacterial diversity and promoted deterministic processes of bacterial community construction, suggesting that mycorrhizal symbiosis resulted in the directional enrichment of bacterial communities and established a stable rhizosphere bacterial community. AM fungi promoted the enrichment of nine bacteria, including <em>Ohtaekwangia</em>, <em>Niastella</em>, <em>Gemmatimonas</em>, <em>Devosia</em>, <em>Sphingomonas</em>, <em>Novosphingobium</em>, <em>Opitutus</em>, <em>Lysobacter</em>, <em>Brevundimonas</em>, which are positively correlated with NPK-related parameters. Through a functional identification experiment, we found that six of these genera, including <em>Brevundimonas</em>, <em>Lysobacter</em>, <em>Ohtaekwangia</em>, <em>Sphingomonas</em>, <em>Devosia</em>, and <em>Gemmatimonas</em>, demonstrated the ability to mineralize organophosphate and dissolve inorganic phosphorus, nitrogen, and potassium. Our study revealed that AM fungi can regulate rhizosphere bacterial community assembly and attract specific rhizosphere bacteria to promote soil nutrient turnover in southern grasslands.</p>
Data to accompany "Automatic text clustering for audio attribute elicitation experiment responses", AES 143rd Convention, New York, NY, USA, 2017
<p>This work was supported by the EPSRC Programme Grant S3A: Future Spatial Audio for an Immersive Listener Experience at Home (EP/L000539/1) and the BBC as part of the BBC Audio Research Partnership. Details about the data underlying this work, along with the terms for data access, are available from http://dx.doi.org/10.15126/surreydata.00841589.</p> <p>If you use the data, please cite the following paper:</p> <p>J. Francombe, T. Brookes, and R. Mason, “Automatic text clustering for audio attribute elicitation experiment responses”, AES 143rd Convention, New York, NY, USA, 2017</p>
BRAIN Journal-Prediction of Thyroid Disease Using Data Mining Techniques-Figure 2. Attributes of the classification models used in the experiments
<p>The authors used for their experiments a data set (UCI, 2016) containing 756 records about persons with thyroid dysfunctions. The classification model has 22 attributes; the class attribute is the target and it has three possible values: hypothyroidism, hyperthyroidism and normal. The current data set was extracted and preprocessed from the original file. A description of the attributes used in the experiments is given in Figure 2 (an extract from thyroid.arff test file). </p>
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
1.Short-term experiments cannot characterize how long-lived, invasive shrubs influence ecological properties that can be slow to change, including native diversity and soil fertility. Observational studies are thus necessary, but often suffer from methodological issues. 2.To highlight ways of improving the design and interpretation of observational studies that assess the impacts of invasive plants, we compare two studies of nutrient cycling and earthworms along two separate gradients of invasive shrub abundance. By considering the divergent sampling strategies and statistical analyses of these two studies, and interpreting their contradictory results in the context of other studies, we also aim to better describe the impacts of the focal invader, Rhamnus cathartica. 3.In a new study of a single site in Minnesota, we observed positive correlations between buckthorn abundance and soil pH, soil nutrient pools, nutrient fluxes through leaf litterfall, earthworm abundance, and root biomass. Multiple regression models showed these relationships persisted after accounting for variability in soil texture and tree species composition. For a separate, more expansive study in Illinois, other authors reported little to no correlation between buckthorn abundance and 10 soil properties, including earthworm abundance, pH, and nutrient concentrations. However, like many other studies, their regression models only assessed predictors related to invader abundance. R2 values for models of ecosystem properties ranged from 0-0.79 (adjusted-R2) for our study in Minnesota and from <0.05-0.16 (unadjusted) for the prior study in Illinois. 4.Differences in sampling error and use of predictor variables between the two studies likely explain the contrasting results. 5.Synthesis and applications. To reduce the uncertainty of conclusions from observational studies of invasive plants, future studies must ensure that heterogeneity of soils and vegetation is adequately accounted for in the sampling strategy and statistical analyses (e.g., analysis of covariance, multiple regression). Particular attention should be given to ecosystem properties with variability that likely predates the invader (e.g., geophysical features and tree community composition). In our study, effects of buckthorn on ecosystem properties were not only robust to the inclusion of potentially confounding predictors, but also consistent with expectations based on ecological stoichiometry and mass balance of element flow.
Data from ATTRICI 1.1 - counterfactual climate for impact attribution
<p>Data as produced and presented in the publication</p> <pre><strong>ATTRICI v1.1 - counterfactual climate for impact attribution</strong></pre> <p>in Geoscientific Model Development.</p> <p>Abstract:</p> <p>Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC WGII a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system’s behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause". Impact attribution following this definition remains a challenge because the counterfactual baseline cannot be observed. Process-based and empirical impact models can fill this gap as they allow to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution set-up of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.</p>
Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2: data to prepare figures
<p>The file contains the data to plot the graphics displayed in Joos et al., Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2, Geophys. Res. Letters, in press, June 2023.</p>
Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling
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Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
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Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
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Physical Attributes of the Hubbard Brook Valley Plots, 1995 - 1998 Survey Data
The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. Multiple above- and below- ground attributes were measured between 1995 and 1998. This dataset includes physical attribute data; tree inventory, soil data and other measurements are presented in separate datasets. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data and code for: "Large reductions in tropical bird abundance attributable to heat extreme intensification "
<p>Data and code necessary to reproduce results of paper:</p> <p>Large reductions in tropical bird abundance attributable to heat extreme intensification</p> <p>By Maximilan Kotz, Tatsuya Amanon and James Watson.</p> <p>Please contact maximilian.kotz@bsc.es / maxkotz@pik-potsdam.de for questions.</p> <p> </p>
Luminoso Input Data for SemEval-2018 Task 10: "Capturing Discriminative Attributes"
<p>This is the data required to run Luminoso's entry to the SemEval-2018 task on Capturing Discriminative Attributes.</p> <p>This data includes:</p> <ul> <li>A recently-computed version of the <a href="https://github.com/commonsense/conceptnet-numberbatch">ConceptNet Numberbatch</a> word embeddings</li> <li>The output of an implementation of Semantic Matching Energy over ConceptNet</li> <li>A SQLite database containing the lead section of all articles on the <a href="http://en.wikipedia.org">English Wikipedia</a> on 2017-12-20</li> <li>The text file that that database is constructed from</li> <li>A SQLite database of words that co-occur in <a href="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html">Google Books 2-grams</a></li> <li>The text file containing total counts of 2-grams in the Google Books data, which that database is constructed from</li> </ul> <p>For more information, see the paper "Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge", by Robyn Speer and Joanna Lowry-Duda, to appear in the proceedings of the SemEval workshop at NAACL 2018.</p>
Input Data for "Distinguishing attributes using ConceptNet Numberbatch"
<p>In a post on blog.conceptnet.io, we're showing how to use ConceptNet Numberbatch alone to create a good solution to SemEval-2018 Task 10, Capturing Discriminative Attributes. This is an alternative, simplified implementation of a result presented in the SemEval paper <a href="http://aclweb.org/anthology/S18-1162">Distinguishing Attributes Using Text Corpora and Relational Knowledge</a>, by Robyn Speer and Joanna Lowry-Duda.</p> <p>This data repository contains the data necessary to make the simplified implementation work.</p>
Table 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine. a in Compositions of silver halides from the Broken Hill district, New South Wales
<p><b>Table</b> 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine.a</p><table><tbody><tr><th>No.</th><th><b>Analyses</b></th></tr></tbody><tbody><tr><th>D46338b</th><td>Cl</td><td>60</td><td>57</td><td>57</td><td>53</td><td>56</td><td>57</td><td>58</td><td>68</td><td>56</td><td>66</td><td></td></tr><tr><th></th><td>Br</td><td>32</td><td>35</td><td>34</td><td>37</td><td>35</td><td>34</td><td>32</td><td>31</td><td>35</td><td>33</td><td></td></tr><tr><th></th><td>I</td><td>8</td><td>8</td><td>9</td><td>10</td><td>9</td><td>9</td><td>10</td><td>1</td><td>9</td><td>1</td><td></td></tr><tr><th>D26168c</th><td>Cl</td><td>72</td><td>76</td><td>63</td><td>65</td><td>60</td><td>77</td><td>77</td><td>72</td><td>66</td><td>65</td><td></td></tr><tr><th></th><td>Br</td><td>28</td><td>24</td><td>37</td><td>35</td><td>40</td><td>22</td><td>23</td><td>28</td><td>34</td><td>35</td><td></td></tr><tr><th>D28184d</th><td>I Cl</td><td>75</td><td>68</td><td>86</td><td>70</td><td>70</td><td>1 70</td><td>78</td><td>70</td><td>84</td><td></td><td></td></tr><tr><th></th><td>Br</td><td>25</td><td>30</td><td>14</td><td>28</td><td>30</td><td>29</td><td>21</td><td>30</td><td>16</td><td></td><td></td></tr><tr><th>D28185d</th><td>I Cl</td><td>55</td><td>2 56</td><td>55</td><td>2 56</td><td>55</td><td>155</td><td>1 60</td><td>50</td><td>45</td><td>50</td><td>56</td></tr><tr><th></th><td>Br</td><td>43</td><td>43</td><td>44</td><td>43</td><td>44</td><td>43</td><td>40</td><td>46</td><td>45</td><td>45</td><td>41</td></tr><tr><th></th><td>I</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td></td><td>4</td><td>10</td><td>5</td><td>3</td></tr></tbody></table><p>a Analyses reported as in Table I. b Trace S detected. C Traces Pb, Cu, S, As detected. d Consols Mine; traces Pb, As, S, Fe detected.</p>
Data from: Urban street lighting differentially affects community attributes of airborne and ground-dwelling invertebrate assemblages
<p><span>The introduction of artificial light at night (ALAN) into natural and urbanised landscapes is a known and highly pervasive disruptor of invertebrate communities. However, the effect of variation in intensity and spectra of ALAN on invertebrate communities inhabiting different spatial niches is little understood. Further, the remarkable ability of ALAN to continue to disrupt biodiversity even in chronically-illuminated urban landscapes is not often acknowledged. </span></p> <p><span>Here, we simultaneously sampled airborne and ground-dwelling invertebrate assemblages under and between urban streetlights to explore the effects on community composition and abundance of a) proximity to decadal (i.e long-illuminated) nocturnal street lighting and b) variation in the spectral output of light. </span></p> <p><span>The two assemblages responded differently. For airborne invertebrates, night-time abundance doubled, and night-time assemblage composition was significantly different for traps under, compared with between, streetlights. These differences in abundance were not affected by streetlight intensity, and were absent in day samples, suggesting that even weak ALAN may be causing short-term redistribution of nocturnal invertebrates. Further, the abundance (but not composition) effects of ALAN on airborne invertebrates increased when the streetlights emitted a higher proportion of short-wavelength light. </span></p> <p><span>In contrast, for ground-dwelling invertebrates, we found only marginal effects of proximity and spectrum of lighting on abundance and no effect on assemblage composition. However, more intense streetlighting reduced abundance and altered composition at traps both under and between lights. </span></p> <p><span><i>Synthesis and Applications: </i>Public lighting managers must consider ALAN impacts on invertebrate communities not only when introducing ALAN to naïve environments, but also when changing lighting in areas that are highly urbanised and exposed to decades of artificial light at night. Further, lighting proposals and environmental monitoring of invertebrate communities must take into account effects on both ground-dwelling and airborne assemblages, as these may respond very differently to the presence, intensity and spectrum of ALAN. </span></p>
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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.