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1,047 results for “interpretability”
habitatmap_terr: the interpreted, terrestrial part of habitatmap_stdized
<p>The data source <code>habitatmap_terr</code> is the further interpreted, terrestrial part of '<a href="https://doi.org/10.5281/zenodo.3355192">habitatmap_stdized</a>' (which, in turn, was derived from the raw data source '<a href="https://doi.org/10.5281/zenodo.3354381">habitatmap</a>'). It is a GeoPackage that contains:</p> <ul> <li> <p><code>habitatmap_terr_polygons</code>: a spatial polygon layer in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p><code>habitatmap_terr_types</code>: a table with the types that occur in each polygon.</p> </li> </ul> <p>This version of <code>habitatmap_terr</code> was derived from version '<code>habitatmap_stdized_2023_v1</code>' as follows:</p> <ul> <li> <p>it excludes all polygons that are most probably aquatic habitat or RIB. These are the polygons for which <strong>all</strong> habitat or RIB types are aquatic. In the process, a distinction was also made between <code>2190_a</code> and <code>2190_overig</code>. There is no exclusion of aquatic types when these coexist with terrestrial types in the same polygon;</p> </li> <li> <p>it excludes types which most probably are <em>no</em> habitat or RIB at all. Those are the types where <code>code_orig</code> contains <code>"bos"</code> or is equal to <code>"6510,gh"</code> or <code>"9120,gh"</code>;</p> </li> <li> <p>it translates several main type codes into a corresponding subtype which they almost always represent: <code>6410</code> -> <code>6410_mo</code>, <code>6430</code> -> <code>6430_hf</code>, <code>6510</code> -> <code>6510_hu</code>, <code>7140</code> -> <code>7140_meso</code>, <code>9130</code> -> <code>9130_end</code>;</p> </li> <li> <p>it distinguishes types <code>rbbhfl</code> and <code>rbbhf</code>.</p> </li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/abf596e/src/generate_habitatmap_terr">'n2khab-preprocessing' at commit abf596e</a> for its creation from the <code>habitatmap_stdized</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Attributes of <code>habitatmap_terr_polygons</code>:</p> <ul> <li> <p><code>polygon_id</code></p> </li> <li> <p><code>description_orig</code>: polygon description based on the original type codes in the <code>habitatmap</code> data source</p> </li> <li> <p><code>description</code>: based on <code>description_orig</code> but with the interpreted type codes</p> </li> <li> <p><code>source</code>: states where <code>description</code> comes from: either <code>habitatmap_stdized</code> or <code>habitatmap_stdized + interpretation</code></p> </li> </ul> <p>Attributes of <code>habitatmap_terr_types</code>:</p> <ul> <li> <p><code>polygon_id</code></p> </li> <li> <p><code>type</code>: the interpreted habitat or RIB type.</p> </li> <li> <p><code>certain</code>: <code>TRUE</code> when the type is certain and <code>FALSE</code> when the type is uncertain.</p> </li> <li> <p><code>code_orig</code>: original type code in raw <code>habitatmap</code>.</p> </li> <li> <p><code>phab</code>: proportion of polygon covered by type, as a percentage.</p> </li> <li> <p><code>source</code>: states where <code>type</code> comes from: either <code>habitatmap_stdized</code> or <code>habitatmap_stdized + interpretation</code></p> </li> </ul>
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
<p>DFEI dataset</p> <p><em>The full description can also be found in README.md.</em></p> <p>The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle.</p> <p>The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy.</p> <p>Generated events</p> <p>The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table.</p> <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> ∼ 5</td> <td> ∼ 150</td> <td> ≪ 1</td> <td> ∼ 1</td> </tr> </tbody> </table> <p>Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets.</p> <p>Datasets</p> <p>The datasets are divided in three categories</p> <p>Training and testing</p> <p>The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays.</p> <p>Evaluation</p> <p>The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000).</p> <p>Exclusive decays</p> <p>In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region.</p> <p>The datasets contained are:</p> <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> <p>More information on them can be found in the paper.</p> <p>Loading the data</p> <p>The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar.</p> <p>An example snippet is given here:</p> <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> <p>The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves.</p> <p>Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles.</p> <p>Variables</p> <p>The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the <em>z</em> axis pointing along the beamline, the <em>x</em> axis beinng parallel to the horizontal and the <em>y</em> axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction.</p> <ul> <li> <p>General:</p> <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> <p>Node variables:</p> <ul> <li> <p>ParticleKey: unique number to identify each particle in a given event.</p> </li> <li> <p>Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> <li> <p>FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not.</p> </li> <li> <p>Transverse momentum (<em>p</em><sub><em>T</em></sub>): component of the three-momentum transverse to the beamline, i.e. the <em>x</em> and <em>y</em> component combined.</p> </li> <li> <p>Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event.</p> </li> <li> <p>Pseudorapidity (<em>η</em>): spatial coordinate describing the angle of a particle relative to the beam axis, computed as <em>η</em> = arctanh(<em>p</em><sub><em>z</em></sub>/∥<em>p⃗</em>∥).</p> </li> <li> <p>Charge (<em>q</em>): for the stable particles under consideration, the charge can take the value 1 or -1.</p> </li> <li> <p><em>O</em><sub><em>x</em></sub>, <em>O</em><sub><em>y</em></sub>, <em>O</em><sub><em>z</em></sub>: cartesian coordinates of the origin point of the particle.</p> </li> <li> <p><em>p</em><sub><em>x</em></sub>, <em>p</em><sub><em>y</em></sub>, <em>p</em><sub><em>z</em></sub>: cartesian coordinates of the three-momentum.</p> </li> <li> <p><em>P</em><em>V</em><sub><em>x</em></sub>, <em>P</em><em>V</em><sub><em>y</em></sub>, <em>P</em><em>V</em><sub><em>z</em></sub>: cartesian coordinates of the position of the associated primary vertex.</p> </li> </ul> </li> <li> <p>Edge variables:</p> <ul> <li> <p>FirstParticleKey: ParticleKey of one of the two particles connected by the edge.</p> </li> <li> <p>SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey.</p> </li> <li> <p>FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay.</p> </li> <li> <p>Opening angle (<em>θ</em>): angle between the three-momentum directions of the two particles.</p> </li> <li> <p>Momentum-transverse distance (<em>d</em><sub> ⊥ <em>P⃗</em></sub>): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles.</p> </li> <li> <p>Distance along the beam axis (<em>Δ</em><sub><em>z</em></sub>): difference between the <em>z</em>-coordinate of the origin points of the two particles.</p> </li> <li> <p><em>F</em><em>r</em><em>o</em><em>m</em><em>S</em><em>a</em><em>m</em><em>e</em><em>P</em><em>V</em>: boolean variable indicating whether the two particles share the same associated primary vertex.</p> </li> <li> <p>Order of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>O</em><em>r</em><em>d</em><em>e</em><em>r</em>): variable that can take the values 0, 1, 2 or 3, as explained in the paper.</p> </li> <li> <p>Identity of the “topological” Lowest Common Ancestor (<em>T</em><em>o</em><em>p</em><em>o</em><em>L</em><em>C</em><em>A</em><em>I</em><em>D</em>): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>.</p> </li> </ul> </li> </ul>
H.J. Andrews Forest Discovery Trail: An interpretation of place based on curriculum of interpretive learning trail and field trip support, 2016
The H.J. Andrews Experimental Forest (HJA) in the Oregon Cascades is one of 24 sites in the Long-Term Ecological Research (LTER) Network. It supports research on forests, streams, and watersheds, and fosters collaborations between ecosystem science, education, natural resource management, and the humanities. The site currently hosts 85 interdisciplinary research projects, as well as experiential training for undergraduate and graduate students. In addition, the HJA runs a vibrant professional development program for teachers. Because much of the HJA’s terrain is steep and occupied with sensitive research materials, middle and high school visits are limited to tours in designated areas. The Discovery Trail was developed in 2011 as a place for visitors (~1800 in 2014) to explore the forest and site research themes from HJA headquarters, but it is not yet amenable to unguided educational exploration. We have designed an interpretive learning trail and field trip support framework for the Discovery Trail. Our primary objective is to educate students about place while guiding them to reflect upon their own relationships with place and personal responsibility for stewardship behavior. Long-term place-based conservation research is woven with creative writing from the HJA writer’s residency program and paired with reflection and creative inquiry. Interactive trail stops enable students to engage the forest from multiple perspectives. The Discovery Trail is wired for intranet wifi and content and assessment will be delivered by digital media (i.e. iPads). We will evaluate conceptual learning according to the Framework for the Next Generation Science Standards, as well as observe affective changes in sense of place, empowerment, and expressions of care or empathy through analysis of student responses to the trail activities. Because conservation attitudes require not just knowledge about systems, but also emotional connections to the material, our learning experience will in
Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"
<p>This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data<br> by Maximum-Entropy Reweighting of Simulated Structural Ensembles".</p> <p>Contents:</p> <p>code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting</p> <p>data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml</p>
Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies
<p>Data corresponding to the paper "Crossreactive probes on Illumina DNA methylation arrays: a large study on ALS shows that a cautionary approach is warranted in interpreting epigenome-wide association studies."<br> <br> Corresponding scripts can be found at: <a href="https://github.com/pjhop/dnamarray_crossreactivity">https://github.com/pjhop/dnamarray_crossreactivity</a><br> All downstream analyses in <a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/c9_analysis.Rmd">c9_analysis.Rmd</a> and in<a href="https://github.com/pjhop/dnamarray_crossreactivity/blob/master/analysis/supplementary_note.Rmd"> supplementary_note.Rmd</a> can be reproduced using the deposited data as follows:</p> <ul> <li>Clone the dnamarray_crossreactivity repository: < git clone https://github.com/pjhop/dnamarray_crossreactivity.git ></li> <li>Download the data ('data.zip') and place it in the 'dnamarray_crossreactivity' folder.</li> <li>Unzip the data.zip folder</li> </ul> <p>Scripts used to generate the data in each subdirectory can be found at:</p> <ul> <li>data/processed/c9_matches/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/c9_matches</a></li> <li>data/output/ewas/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/ewas</a></li> <li>data/output/figs/: empty folder, running 'c9_analysis.Rmd' will save figures here.</li> <li>data/misc/: <a href="https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other">https://github.com/pjhop/dnamarray_crossreactivity/tree/master/analysis/other</a></li> <li>data/extdata: <ul> <li>Zhou <em>et al.</em> annotations (EPIC.hg19.manifest.tsv.gz, HM450.hg19.manifest.pop.tsv.gz, HM450.hg19.manifest.tsv.gz) were downloaded from: <a href="https://zwdzwd.github.io/InfiniumAnnotation">https://zwdzwd.github.io/InfiniumAnnotation</a> (downloaded at 17/09/2020)</li> <li>Naeem <em>et al.</em><em> </em>data (12864_2013_7006_MOESM2_ESM.csv) was downloaded from: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3943510/</a></li> <li>Chen <em>et al.</em> data (48639-non-specific-probes-Illumina450k.xlsx) was downloaded from <a href="https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx">https://github.com/Jfortin1/funnorm_repro/blob/master/bad_probes/48639-non-specific-probes-Illumina450k.xlsx</a></li> <li>The anno_450k.txt.gz and anno_EPIC.txt.gz are subsets of the annotation files included in the following package respectively: <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation450kanno.ilmn12.hg19.html</a> and <a href="https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html">https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylationEPICanno.ilm10b2.hg19.html</a></li> </ul> </li> <li> data/genome_bs: Scripts used to generate these data can be found at <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg19.R</a> and <a href="https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R">https://github.com/pjhop/DNAmCrosshyb/blob/master/data-raw/bisulfite_convert_hg38.R</a> .</li> <li> data/raw: Individual-level data is available upon access at: <a href="https://ega-archive.org/studies/EGAS00001004587">https://ega-archive.org/studies/EGAS00001004587</a></li> </ul>
A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey
<p>Open access to curated datasets positively impacts on scientific research of machine learning and deep learning techniques. It is a fact that benchmarks and public datasets prepared for data science assist researchers interested in evaluating, testing, and building new data-driven methodologies for specific domain areas.</p> <p>In geosciences, there has been a remarkable growth of public datasets arranged to address machine learning challenges related to the oil and gas industry, particularly for reserves exploration and data interpretation. </p> <p>For these reasons, we present the Taranaki dataset, which is a collection of seismic horizons interpreted for a seismic stratigraphic interpretation study in the Taranaki Basin, offshore New Zealand. This data comprises fourteen seismic horizons that mark stratigraphic discordances in the Tui-3D seismic dataset. We annotated five seismic horizons on 33 inline sections and nine horizons on 19 crossline sections.</p> <p>Besides, we present the results of a series of experiments that compare a method of interpolation and a method of deep learning for seismic segmentation. The deep learning experiments evaluated the result of different image tile sizes to train the model, which is presented separately in this dataset. </p> <p>Finally, we evaluated both methodologies to interpret the horizons of this dataset in selected seismic sections. Also, we assessed the absolute error of each method with the ground truth interpretations proposed in this dataset.</p>
Global Pasture Watch - Grassland reference samples based on visual interpretation of VHR imagery and harmonized datasets (2000–2024)
<p>Reference point samples used in the production of the <a href="https://doi.org/10.5281/zenodo.13890401">global maps of annual grassland class and extent for 2000—2022</a><strong> </strong>within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Wath</a> initiative. </p> <p>The reference samples (estabilished by Feature Space Coverage Sampling-FSCS) comprises <strong>2.3M points</strong> visually classified (<em>using Very High Resolution imagery</em>) in:</p> <ol> <li><strong>Cultivated grassland,</strong></li> <li><strong>Natural/semi-natural grassland</strong></li> <li><strong>Other land cover</strong></li> </ol> <p>The file <code>gpw_grassland_fscs.vi.vhr_tile.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> aggregates the samples by visual interpretation units ( 1x1 km) and includes the follow collumns:</p> <ul> <li>cluster_id: Cluster id defined by k-means (FSCS),</li> <li>cluster_distance: Distance from the sample tile to center of the cluster (FSCS),</li> <li>cluster_size: Size of cluster (strata) defined by the FSCS,</li> <li>priority: Priority used by the visual interpretation,</li> <li>tile_id: Sample tile id,</li> <li>imagery: VHR reference images used by the visual interpretation,</li> <li>min_year: Minimum of year covered by the reference samples,</li> <li>max_year: Maximum of year covered by the reference samples,</li> <li>n_years: Number of years covered by the reference samples,</li> <li>n_samples_c1: Number of reference samples for "Cultivated grass" (1),</li> <li>n_samples_c2: Number of reference samples for "Natural / Semi-natural grass" (2),</li> <li>n_samples_c3: Number of reference samples for "Open Shrubland" (2),</li> <li>n_samples_c4: Number of reference samples for "Not grass" (3),</li> <li>n_samples_all: Total number of reference samples,</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_point.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides individual points (with 60-m spatial support) and include the follow collumns:</p> <ul> <li>sample_id: Sample id deribed by MD5 Hash of columns x, y, imagery and year,</li> <li>x: Longitude in WGS84 (EPSG:4326),</li> <li>y: Latitude in WGS84 (EPSG:4326),</li> <li>vi_tile_id: 1-km tile id,</li> <li>tile_id: GLAD tile id (1x1 degree)</li> <li>imagery: VHR Reference image used by the visual interpretation (Google; Bing; Interpolated),</li> <li>ref_date: Reference date of GPW samples (based on VHR image) and of other existing datasets,</li> <li>year: Reference year of GPW samples (based on VHR image) and of other existing datasets,</li> <li>class: Class id (1: Cultivated grassland; 2: Natural/semi-natural grassland; 3: Open shrubland; 4: Other land cover) ,</li> <li>class_label: Class labels (Cultivated grassland; Natural/semi-natural grassland; Open shrubland; Other land cover) ,</li> <li>dataset_name: Existing dataset names (CGLS-LC, EuroCrops, GeoWiki, GeoWiki-feedback, LCMap-Conus, LUCAS, MapBiomas, WorldCereal, GPW) <br>dataset_class: Original land cover class provided by the maintainer of existing dataset</li> <li>esa_worldcover_2020: Land cover class labels extracted from ESA WorldCover 2020,</li> <li>glad_glcluc_yyyy: Land cover class labels extracted from UMD GLAD GLCLUC for the reference date,</li> <li>glc_fcs30d_yyyy: Land cover class labels extracted from GLC_FCS30D for the reference date,</li> <li>gpw_fscs_cluster: K-Means output ranging from 0—9999 according to Feature Space Coverage Sampling (FSCS),</li> <li>ml_cv_group: spatial block CV group (based on vi_tile_id),</li> <li>ml_type: specify if the sample was used for (1) training or (2) calibration.</li> </ul> <p>The file <code>gpw_grassland_fscs.vi.vhr_grid.samples_20000101_20241231_go_epsg.4326_v2.gpkg</code> provides the grid samples (with 10-m spatial support) and include the follow collumns:</p> <ul> <li>tile_id: 1-km tile id,</li> <li>bing_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Bing Maps Images,</li> <li>bing_image_start_date: Start date of the Bing Maps Images used in the visual interpretation,</li> <li>bing_image_end_date: End date of the Bing Maps Images used in the visual interpretation,</li> <li>google_class: Class labels (Cultivated grassland; Natural/semi-natural grassland; Other land cover) defined using as reference Google Maps Images,</li> <li>google_image_start_date: Start date of the Google Maps Images used in the visual interpretation,</li> <li>google_image_end_date: End date of the Google Maps Images used in the visual interpretation,</li> <li>missing_image_date: No images available,</li> <li>same_image_bing_google: Images from the same date available in Google and Bing Maps.</li> </ul> <p>The dataset was produced through the <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS plugin Fast Grid Inspection</a>.</p> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
global interpreted planted forest, natural forests validation samples
<p>This dataset provided the global validation dataset including planted forest, natural forest, and non-forest in 2015. This dataset was visually interpreted using the high spatial resolution (<1 m) images from Google Earth, and combining the spatial distribution of planted forets, and forest gain map.</p> <p>1 denotes planted forest, 2 denotes the natural forest, and 3 denotes the nonforest.</p> <p>Detailed information about how the global validation dataset was visually interpreted can be seen in the following reference:</p> <p>Xu, H., He, B., Guo, L., Yan, X., Zeng, Y., Yuan, W., et al. (2024). Global forest plantations mapping and biomass carbon estimation. Journal of Geophysical Research: Biogeosciences, 129, e2023JG007441.</p>
Global Biotic Interactions: Interpreted Data Products hash://md5/e76bf914309ad27dce6ab911d8854590 hash://sha256/ba79836caab5b7ba2d7d659123d27c89f4ad990bd50f97ded935edee9fbe9f87
<p>Global Biotic Interactions: Interpreted Data Products</p> <p>Global Biotic Interactions (GloBI, https://globalbioticinteractions.org, [1]) aims to facilitate access to existing species interaction records (e.g., predator-prey, plant-pollinator, virus-host). This data publication provides interpreted species interaction data products. These products are the result of a process in which versioned, existing species interaction datasets ([2]) are linked to the so-called GloBI Taxon Graph ([3]) and transformed into various aggregate formats (e.g., tsv, csv, neo4j, rdf/nquad, darwin core-ish archives). In addition, the applied name maps are included to make the applied taxonomic linking explicit. </p> <p>Citation<br>--------</p> <p>GloBI is made possible by researchers, collections, projects and institutions openly sharing their datasets. When using this data, please make sure to attribute these *original data contributors*, including citing the specific datasets in derivative work. Each species interaction record indexed by GloBI contains a reference and dataset citation. Also, a full lists of all references can be found in citations.csv/citations.tsv files in this publication. If you have ideas on how to make it easier to cite original datasets, please open/join a discussion via https://globalbioticinteractions.org or related projects.</p> <p>To credit GloBI for more easily finding interaction data, please use the following citation to reference GloBI:</p> <p>Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>Bias and Errors<br>--------</p> <p>As with any analysis and processing workflow, care should be taken to understand the bias and error propagation of data sources and related data transformation processes. The datasets indexed by GloBI are biased geospatially, temporally and taxonomically ([5], [6]). Also, mapping of verbatim names from datasets to known name concept may contains errors due to synonym mismatches, outdated names lists, typos or conflicting name authorities. Finally, bugs may introduce bias and errors in the resulting integrated data product.</p> <p>To help better understand where bias and errors are introduced, only versioned data and code are used as an input: the datasets ([2]), name maps ([3]) and integration software ([6]) are versioned so that the integration processes can be reproduced if needed. This way, steps take to compile an integrated data record can be traced and the sources of bias and errors can be more easily found.</p> <p>This version was preceded by [7]. </p> <p>Contents<br>--------</p> <p>README:<br>this file</p> <p>citations.csv.gz:<br>contains data citations in a in a gzipped comma-separated values format.</p> <p>citations.tsv.gz:<br>contains data citations in a gzipped tab-separated values format.</p> <p>datasets.csv.gz:<br>contains list of indexed datasets in a gzipped comma-separated values format.</p> <p>datasets.tsv.gz:<br>contains list of indexed datasets in a gzipped tab-separated values format.</p> <p>verbatim-interactions.csv.gz<br>contains species interactions tabulated as pair-wise interaction in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources.</p> <p>verbatim-interactions.tsv.gz<br>contains species interactions tabulated as pair-wise interaction in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>interactions.csv.gz:<br>contains species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>interactions.tsv.gz:<br>contains species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-interactions.csv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-interactions.tsv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic names are interpreted using taxonomic alignment workflows and may be different than those provided by the original sources.</p> <p>refuted-verbatim-interactions.csv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped comma-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>refuted-verbatim-interactions.tsv.gz:<br>contains refuted species interactions tabulated as pair-wise interactions in a gzipped tab-separated values format. Included taxonomic name are *not* interpreted, but included as documented in their sources. </p> <p>interactions.nq.gz:<br>contains species interactions expressed in the resource description framework in a gzipped rdf/quads format.</p> <p>dwca-by-study.zip:<br>contains species interactions data as a Darwin Core Archive aggregated by study using a custom, occurrence level, association extension.</p> <p>dwca.zip:<br>contains species interactions data as a Darwin Core Archive using a custom, occurrence level, association extension.</p> <p>neo4j-graphdb.zip:<br>contains a neo4j v3.5.32 graph database snapshot containing a graph representation of the species interaction data.</p> <p>taxonCache.tsv.gz:<br>contains hierarchies and identifiers associated with names from naming schemes in a gzipped tab-separated values format.</p> <p>taxonMap.tsv.gz:<br>describes how names in existing datasets were mapped into existing naming schemes in a gzipped tab-separated values format.</p> <p>References<br>-----</p> <p>[1] Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. doi: 10.1016/j.ecoinf.2014.08.005.</p> <p>[2] Poelen, J. H. (2020) Global Biotic Interactions: Elton Dataset Cache. Zenodo. doi: 10.5281/ZENODO.3950557.</p> <p>[3] Poelen, J. H. (2021). Global Biotic Interactions: Taxon Graph (Version 0.3.28) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4451472</p> <p>[4] Hortal, J. et al. (2015) Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. Annual Review of Ecology, Evolution, and Systematics, 46(1), pp.523–549. doi: 10.1146/annurev-ecolsys-112414-054400.</p> <p>[5] Cains, M. et al. (2017) Ivmooc 2017 - Gap Analysis Of Globi: Identifying Research And Data Sharing Opportunities For Species Interactions. Zenodo. Zenodo. doi: 10.5281/ZENODO.814978.</p> <p>[6] Poelen, J. et al. (2022) globalbioticinteractions/globalbioticinteractions v0.24.6. Zenodo. doi: 10.5281/ZENODO.7327955.</p> <p>[7] GloBI Community. (2024). Global Biotic Interactions: Interpreted Data Products hash://md5/946f7666667d60657dc89d9af8ffb909 hash://sha256/4e83d2daee05a4fa91819d58259ee58ffc5a29ec37aa7e84fd5ffbb2f92aa5b8 (0.7) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11552565</p> <p>Content References<br>-----</p> <p>hash://sha256/5f4906439eba61f936b3dd7455a62c51656a74206f82d3f654e330fda6fbbe45 citations.csv.gz<br>hash://sha256/c8100368dae39363b241472695c1ae197aaddc6e3d6c0a14f3f5ee704b37f3f6 citations.tsv.gz<br>hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f datasets.csv.gz<br>hash://sha256/e6f4aa897c5b325e444315e021b246ffed07fef764b0de6c0f1b2688bbdf9d0f datasets.tsv.gz<br>hash://sha256/f11dc825609cdb1d4a3e9ba8caca9bf93c90dd6f660c7f6a0c8aa01c035a5e1f dwca-by-study.zip<br>hash://sha256/7f16aacacae74e8b0cdef04c612ba776f508ff7ffe385abc57583e37aec8fe53 dwca.zip<br>hash://sha256/b65e4c9a3615f1386bb97e45fb907d053df55476149aa6d71e6f398351218d0d interactions.csv.gz<br>hash://sha256/0c28032392f82d753690be126805e6334ca46bdc4b5e2102a79b15ce0cc0ba90 interactions.nq.gz<br>hash://sha256/8a7031250c288ba0da3d5cdbedc19d54c2f16ba3aa70d49826a7369b6edeca04 interactions.tsv.gz<br>hash://sha256/d0c0fbf536cc63c004d057efc14600ba8cc5874f401b08f51837273b7854f1bb neo4j-graphdb.zip<br>hash://sha256/50e77636f8b58c040e38b6a70ba7cc8288b190ef252dc0d4eb2f12f4c541e82f README<br>hash://sha256/a74e2a39cfe133ae9de1eeea94f5dda8cbd58cfe61a8ccf91b7c540757719c74 refuted-interactions.csv.gz<br>hash://sha256/37b06e274e41ca749399763989816854101238ade9863365f384a2764c639e9d refuted-interactions.tsv.gz<br>hash://sha256/23315b6cd3fdc91f9c1d5d5bc39fa52cf1cef7a4e97d9d023d452751df13f30e refuted-verbatim-interactions.csv.gz<br>hash://sha256/ff82e40cee4f8a8852d0c241f5027f66157a2b8a9090ffa3a0a329a206828d96 refuted-verbatim-interactions.tsv.gz<br>hash://sha256/f072fbc7affb6e29978c7540af6cdccd3a219a23b0a4765b5bae56bd20df0d88 taxonCache.tsv.gz<br>hash://sha256/cd28c81bb2432646a81ad216bc11818f7568ce81826e0074d9a33579da2c1426 taxonMap.tsv.gz<br>hash://sha256/a1d14aa47806c624cf7e3a8c8236643dcf19ed1835c79c65958f7317ebfb9566 verbatim-interactions.csv.gz<br>hash://sha256/2284434219d5fdab1e2152955f04363852c132b76709c330d33e31517817a82e verbatim-interactions.tsv.gz</p> <p>hash://md5/d6ebf42729d988e15cb30adfa6112234 citations.csv.gz<br>hash://md5/42877ae68e51871b8eb7116e62f6b268 citations.tsv.gz<br>hash://md5/3e437580296fdeff3b6f35d1331db9d1 datasets.csv.gz<br>hash://md5/3e437580296fdeff3b6f35d1331db9d1 datasets.tsv.gz<br>hash://md5/fe88720fd992771bd64bfa220ad6a7d3 dwca-by-study.zip<br>hash://md5/cbe132a9288feaef2f3e0c0409b8dc2f dwca.zip<br>hash://md5/051f6db667c4b84616223c2776464dbf interactions.csv.gz<br>hash://md5/b66857f8750e56ba9abe484b1f72eac4 interactions.nq.gz<br>hash://md5/300839c346184b2fedc4e1fb31bcc29c interactions.tsv.gz<br>hash://md5/e79cf5ffee919672f99ea338f3661566 neo4j-graphdb.zip<br>hash://md5/898678f47561d7ef53722bc32957dcd9 README<br>hash://md5/65a185f19df304e53f92a7275f2de291 refuted-interactions.csv.gz<br>hash://md5/bc37a4354f8a2402e9335ae44f28cbd7 refuted-interactions.tsv.gz<br>hash://md5/42e817c31e2ca05e582be94e6ec283c5 refuted-verbatim-interactions.csv.gz<br>hash://md5/93639b70a1d8e47fd194b6384c0287a7 refuted-verbatim-interactions.tsv.gz<br>hash://md5/e32482b3697aa928a5fcb58a570191df taxonCache.tsv.gz<br>hash://md5/75251510925875d3fdc1952cc4b98043 taxonMap.tsv.gz<br>hash://md5/6a0c6224f4a4c3dca9994d70ad0b2fd2 verbatim-interactions.csv.gz<br>hash://md5/905acb49a700e5b5a292be02c917e710 verbatim-interactions.tsv.gz</p>
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Supplementary material for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer"
<p>Supplementary files for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer".</p>
OOPSLA 2022 Artifact for "A Fast in-place Interpreter for WebAssembly"
<p>This archive includes</p> <ul> <li>source-code snapshots of 3 Web Engines for executing WebAssembly</li> <li>3 non-Web engines: Wasm3, the WebAssembly Micro-Runtime, and the Wizard Research Engine</li> <li>binary builds for Linux x86-64</li> <li>build instructions</li> <li>PolyBenchC benchmark Wasm binaries</li> <li>benchmarking setup and scripts</li> <li>data collected from experiments included in the OOPSLA 2022 paper</li> <li>instructions for running the benchmarks on Linux systems</li> </ul>
Interpreting future climate conditions in Brazilian cities – Dashboard and EPW files
<h3>(English)</h3> <h1>1. Introduction</h1> <p>This project aims to address the impacts of climate change on the built environment by developing a set of future Brazilian EPW (Energy Plus Weather Format) files and a dashboard to interpret and evaluate the data. The future climate files were obtained using the Future Weather Generator (FWG) [1] with climate projections for Brazilian cities, integrating these projections into a code pipeline for automation. In this part of the project, thermal comfort indices, such as the Universal Thermal Climate Index (UTCI) and the Discomfort Index (DI), were also evaluated to understand future thermal comfort conditions. The methodology followed the structure available in the <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> repository:</p> <ol> <li>Climate-One-Building (COB) web-scrapping for all available Brazilian EPW files (we recommend doing this carefully so as not to damage the COB infrastructure);</li> <li>Automatic organisation of all EPW files in a folder, extracting them from the ZIP format;</li> <li>Simulation of future climate files using FutureWeatherGenerator [1] in a line of code with default parameters (shown in Table 1);</li> <li>Organisation of all available EPWs (original and simulated) in a single database;</li> <li>Calculation of thermal comfort indices using pythermalcomfort [2].</li> </ol> <p>The main objective is to provide researchers, policymakers and professionals with a comprehensive tool for assessing and mitigating the impacts of climate change in different Brazilian cities, offering accurate data for thermal comfort and energy efficiency modelling. The methodology involves generating future EPW files, validating them against existing literature and visualising the results through a user-friendly dashboard. The study highlights the importance of adaptive and climate-resilient strategies in urban planning and building design. Expected climate changes in Brazil include increased dry bulb temperature and variations in relative humidity, radiation and wind speed in the different bioclimatic zones.</p> <p>The dashboard has been designed to simplify the visualisation of future climate data, focusing on the main climate variables, thermal comfort indices and data visualisation. It allows users to filter by city and automatically calculate all the indices, providing detailed analyses and comparisons of different scenarios. By offering a free, open-access, multi-platform, extensible, customisable and easy-to-maintain tool, the project aims to facilitate continuous updates, new features and corrections. This tool supports decision-making in public policy and urban planning, promoting a more sustainable and resilient built environment in the face of climate change.</p> <p> </p> <h1>2. Further details on the methodology</h1> <p>Details on how the indices were selected and how the study was conducted may be found in Vaz et al. [3]. The GitHub repository in <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] also includes details on the step-by-step procedures.</p> <h3>Table 1 - Parameters used in the FWG simulation:</h3> <table> <tbody> <tr> <td> <p><strong>Parameter</strong></p> </td> <td> <p><strong>Data used in the simulations</strong></p> </td> </tr> <tr> <td> <p>Base files</p> </td> <td> <p>578 cities from COB</p> </td> </tr> <tr> <td> <p>CMIP-6 models</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Grid</p> </td> <td> <p>Bilinear interpolation of the four nearest points</p> </td> </tr> <tr> <td> <p>Month transition smoothness</p> </td> <td> <p>72 hours</p> </td> </tr> <tr> <td> <p>Apply variable limits</p> </td> <td> <p>True</p> </td> </tr> <tr> <td> <p>Scenarios</p> </td> <td> <p>A total of nine scenarios: One baseline for 2021 and eight future files (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2050 and 2080)</p> </td> </tr> <tr> <td> <p>Solar hour correction</p> </td> <td> <p>Made by day</p> </td> </tr> <tr> <td> <p>Diffuse irradiation model</p> </td> <td> <p>Engerer, 2015</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. References</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Current version of the dashboard: 1.0.0.</h1> <h1>Available at <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard Comfort.</a></h1> <p>Suggestions for improvements can be made directly in the GitHub repository at <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> or sent to igorcmvaz@gmail.com.</p> <p> </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p> </p> <h3>(Português-BR)</h3> <h1>1. Introdução</h1> <p>Este projeto tem como objetivo abordar os impactos das mudanças climáticas no ambiente construído, desenvolvendo um conjunto de futuros arquivos EPW (Energy Plus Weather Format) brasileiros e um <em>dashboard</em> para interpretar e avaliar os dados. Os arquivos climáticos futuros foram obtidos com o Future Weather Generator (FWG) [1] com projeções climáticas para cidades brasileiras, integrando essas projeções a um pipeline de código para automação. Nessa parte do projeto, os índices de conforto térmico, como o Universal Thermal Climate Index (UTCI) e o Discomfort Index (DI), também foram avaliados para entender as condições futuras de conforto térmico. A metodologia seguiu a estrutura que está disponível no repositório <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a>:</p> <ol> <li>Web-scrapping do Climate-One-Building (COB) para todos os arquivos EPW brasileiros disponíveis (recomendamos fazer isso com cuidado para não prejudicar a infraestrutura do COB);</li> <li>Organização automática de todos os arquivos EPW em uma pasta, extraindo-os do formato ZIP;</li> <li>Simulação dos arquivos climáticos futuros por meio do FutureWeatherGenerator [1] em linha de código com parâmetros padrão (mostrados na Tabela 1);</li> <li>Organização de todos os EPW disponíveis (originais e simulados) em um único banco de dados;</li> <li>Cálculo dos índices de conforto térmico com o pythermalcomfort [2].</li> </ol> <p>O objetivo principal é fornecer a pesquisadores, formuladores de políticas e profissionais uma ferramenta abrangente para avaliar e mitigar os impactos das mudanças climáticas em diferentes cidades brasileiras, oferecendo dados precisos para modelagem de conforto térmico e eficiência energética. A metodologia envolve a geração de futuros arquivos EPW, validando-os com a literatura existente e visualizando os resultados por meio de um <em>dashboard</em> de fácil utilização. O estudo destaca a importância de estratégias adaptativas e resistentes ao clima no planejamento urbano e no projeto de edificações. As mudanças climáticas esperadas no Brasil incluem o aumento da temperatura de bulbo seco e variações na umidade relativa, radiação e velocidade do vento nas diferentes zonas bioclimáticas.</p> <p>O <em>dashboard</em> foi projetado para simplificar a visualização dos dados climáticos futuros, concentrando-se nas principais variáveis climáticas, índices de conforto térmico e visualização dos dados. Ele permite que os usuários filtrem por cidade e calculem automaticamente todos os índices, fornecendo análises detalhadas e comparações de diferentes cenários. Ao oferecer uma ferramenta gratuita, de acesso aberto, multiplataforma, extensível, personalizável e de fácil manutenção, o projeto visa a facilitar atualizações contínuas, novos recursos e correções. Essa ferramenta apoia a tomada de decisões em políticas públicas e planejamento urbano, promovendo um ambiente construído mais sustentável e resiliente em face das mudanças climáticas.</p> <p> </p> <h1>2. Mais detalhes sobre a metodologia</h1> <p>Detalhes sobre a seleção dos índices de conforto e como o estudo foi conduzido podem ser encontrados em Vaz et al. [3]. O repositório GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> [4] também inclui detalhes sobre os procedimentos passo a passo.</p> <h3>Tabela 1 - Parâmetros usados na simulação do FWG</h3> <table> <tbody> <tr> <td> <p><strong>Parâmetro</strong></p> </td> <td> <p><strong>Dados utilizados na simulação</strong></p> </td> </tr> <tr> <td> <p>Arquivos base</p> </td> <td> <p>578 cidades do COB</p> </td> </tr> <tr> <td> <p>Modelos CMIP-6</p> </td> <td> <p>BCC-CSM2-MR, CAS-ESM2.0, CMCC-ESM2, CNRM-CM6.1-HR, CNRM-ESM2.1, EC-Earth3, EC-Earth3-Veg, MIROC-ES2H, MIROC6, MRI-ESM2.0, UKESM1.0-LL</p> </td> </tr> <tr> <td> <p>Malha</p> </td> <td> <p>Interpolação bilinear dos quatro pontos mais próximos</p> </td> </tr> <tr> <td> <p>Suavização da transição mensal</p> </td> <td> <p>72 horas</p> </td> </tr> <tr> <td> <p>Aplicar limites das variáveis</p> </td> <td> <p>Sim</p> </td> </tr> <tr> <td> <p>Cenários</p> </td> <td> <p>Total de nove cenários: Um arquivo base em 2021 e oito arquivos futuros (SSP1-2.6, SSP2-4.5, SSP3-7.0 e SSP5-8.5 para 2050 e 2080)</p> </td> </tr> <tr> <td> <p>Correção de hora solar</p> </td> <td> <p>Feita por dia</p> </td> </tr> <tr> <td> <p>Modelo de radiação difusa</p> </td> <td> <p>Engerer (2015)</p> </td> </tr> </tbody> </table> <p> </p> <h1>3. Referências</h1> <p>[1] E. Rodrigues, M.S. Fernandes, D. Carvalho, Future weather generator for building performance research: An open-source morphing tool and an application, Building and Environment 233 (2023) 110104. https://doi.org/10.1016/j.buildenv.2023.110104.</p> <p>[2] F. Tartarini, S. Schiavon, pythermalcomfort: A Python package for thermal comfort research, SoftwareX 12 (2020) 100578. https://doi.org/10.1016/j.softx.2020.100578.</p> <p>[3] Vaz, I.C.M.; Ghisi, E.; Thives, L.P.; Vieira, A.S.; Rupp, R.F.; da Rosa, A.S.; Flores, R.A.; Bastos, M.B.; Marinoski, D.L.; Silva, A.S.; Weeber, M.; Invidiata, A. (2024). Dashboard for interpreting future climate files used in the simulation of buildings – an outdoor thermal comfort approach. Under submission.</p> <p>[4] Future EPW Analysis - A pipeline of processes aimed at providing future EPW files based on existing models from the literature. Available at: https://github.com/igorcmvaz/future-EPW-analysis.</p> <p> </p> <h1>Versão atual do <em>dashboard</em>: 1.0.0. </h1> <h1>Disponível em <a title="Dashboard comfort - 1.0.0." href="https://app.powerbi.com/view?r=eyJrIjoiNWI0ZTk5YjMtZjA5Ny00ZjE3LTk2ZDUtNDA1OThhNWQ3NWYxIiwidCI6ImZhNzk1MzFjLThjZTUtNGJkMy05N2VlLTI0NWU2ZWUyNjZiOCJ9" target="_blank" rel="noopener">Dashboard conforto.</a></h1> <p>As sugestões de melhorias podem ser feitas diretamente no repositório do GitHub em <a title="Future-EPW-analysis" href="https://github.com/igorcmvaz/future-EPW-analysis" target="_blank" rel="noopener">future-EPW-analysis</a> ou enviadas para igorcmvaz@gmail.com.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p>
Netherlands F3 Interpretation Dataset
<p><strong>Netherlands F3 Interpretation Dataset</strong></p> <p>Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. Such a fact is arguably due to three main factors: powerful computers, new techniques to train deeper networks and more massive datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields today, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. Such demand is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset was acquired in the North Sea, offshore Netherlands. The data is publicly available and comprises pos-stack data, eight horizons and well logs of 4 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating nine horizons separating different seismic facies intervals. The interpreted horizons were used to create 651 labeled masks for inlines and 951 for crosslines. We present the results of two experiments to demonstrate the utility of our dataset. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Crosslines: <ul> <li>Classes: 10</li> <li>Number of slices: 651</li> <li>Records per class: 9,440</li> <li>Total of records: 94,400</li> </ul> </li> <li>Inlines: <ul> <li>Classes: 10</li> <li>Number of slices: 951</li> <li>Records per class: 9,720 <ul> <li>Total of records: 94,720</li> </ul> </li> </ul> </li> <li>Configuration: <ul> <li>Crop: [0, 0, 0, 0]</li> <li>Gray levels: 256</li> <li>Noise: 0.3</li> <li>Percentile: 5.0</li> <li>Strides: [20, 48]</li> <li>Tile shape: [25, 64, 1]</li> </ul> </li> </ul>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations
<p>This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet</p> <p>File descriptions</p> <p>dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites</p>
Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight
<p>Shapefiles associated with the GRL publication: Intermittency in wind-driven surface alteration on Mars interpreted from wind streaks and measurements by InSight</p>
Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning
<p>This data is supplementary to the paper titled "Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning". The file contains the main results.<br><br>For any queries, please visit <a href="https://hydrosense.iitd.ac.in" target="_blank" rel="noopener">Hydrosense Lab (IIT Delhi)</a>.</p>
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.