Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,696
datasets available to search
ShareScore release 0.9.0
Dataset results
3,696 results for “Reference”
Reference data for the Perram and Wertheim (1985) contact function of ellipsoids
<p>Reference data for the Perram and Wertheim (1985) contact function of ellipsoids</p> <p>This dataset provides reference values of the contact function of two ellipsoids, as defined by Perram and Wertheim (Perram, J. W., & Wertheim, M. S. (1985). Statistical mechanics of hard ellipsoids. I. Overlap algorithm and the contact function. Journal of Computational Physics, 58(3), 409–416. <a href="https://doi.org/10.1016/0021-9991(85)90171-8">DOI:10.1016/0021-9991(85)90171-8</a>). This paper will be referred to as PW85 in what follows.</p> <p>Reference values of the <code>F</code> function</p> <p>The data is shared as a HDF5 file <code>pw85_ref_data-YYYYMMDD.h5</code>, which contains the following datasets (to be described below)</p> <ul> <li><code>directions</code>: a 12×3 array,</li> <li><code>F</code>: a 108×108×12×9 array,</li> <li><code>lambdas</code>: a length-9 array,</li> <li><code>radii</code>: a length-3 array,</li> <li><code>spheroids</code>: a 108×6 array.</li> </ul> <p>The attached Python script <code>pw85_gen_ref_data.py</code> was used to generate the data; it uses the <a href="http://mpmath.org/">mpmath</a> library.</p> <p>Mathematical definition of the contact function</p> <p>The contact function is defined in PW85 as the maximum over <code>(0, 1)</code> of the <code>F</code> function which is defined as follows [see Eq. (3.7) in PW85, with slightly different notations]</p> <pre><code>F(λ) = λ(1-λ)r₁₂ᵀ⋅Q⁻¹⋅r₁₂,</code></pre> <p>where <code>0 ≤ λ ≤ 1</code> is a scalar, <code>r₁₂</code> is the center-to-center vector. <code>Q</code> is the matrix defined as follows</p> <pre><code>Q = (1-λ)Q₁ + λQ₂,</code></pre> <p>where <code>Qᵢ</code> is the symmetric, positive definite matrix that defines ellipsoid <code>Ωᵢ</code> through</p> <pre><code>m ∈ Ωᵢ iff (m-cᵢ)ᵀ⋅Qᵢ⁻¹⋅(m-cᵢ) ≤ 1,</code></pre> <p>where <code>cᵢ</code> is the center of <code>Ωᵢ</code>. Then, the contact function <code>F₁₂</code> is defined as the maximum of <code>F</code> [see Eq. (3.8) in PW85]</p> <pre><code>F₁₂(r₁₂, Q₁, Q₂) = max{ F(λ), 0 ≤ λ ≤ 1 }.</code></pre> <p>Parametrization</p> <p>The reference data is restricted to spheroids (equatorial radius: <code>aᵢ</code>; polar radius: <code>cᵢ</code>; direction of axis of revolution: <code>nᵢ</code>)</p> <pre><code>Qᵢ = aᵢ²I + (cᵢ²-aᵢ²)nᵢᵀ⋅nᵢ,</code></pre> <p>(<code>I</code>: identity matrix). The radii take the following values</p> <pre><code>aᵢ, cᵢ ∈ {0.01999, 1.999, 9.999}.</code></pre> <p>These values of the radii are stored in the <code>radii</code> dataset of the HDF5 file. The orientations <code>nᵢ</code> coincide with the vertices of an icosahedron</p> <pre><code>nᵢ = [0, ±u, ±v]ᵀ or nᵢ = [±v, 0, ±u]ᵀ or nᵢ = [±u, ±v, 0]ᵀ,</code></pre> <p>where</p> <pre><code> 1 φ 1+√5 u = ───────, v = ─────── and φ = ────. √(1+φ²) √(1+φ²) 2</code></pre> <p>The orientations are stored in the <code>directions</code> dataset as a 12×3 array. The matrices <code>Qᵢ</code> are precomputed and stored in the <code>spheroids</code> dataset as a 108×6 array (note: 108 = 12 orientations × 3 equatorial radii × 3 polar radii). <code>spheroids[i, :]</code> stores the upper triangular part of the corresponding matrix in row-major order</p> <pre><code>⎡ spheroids[i, 0] spheroids[i, 1] spheroids[i, 2] ⎤ ⎢ spheroids[i, 3] spheroids[i, 4] ⎥. ⎣ sym. spheroids[i, 5] ⎦</code></pre> <p>The scalar <code>λ</code> takes tabulated values (see the <code>lambdas</code> dataset)</p> <pre><code>λ ∈ {0.1, 0.2, …, 0.9}.</code></pre> <p>Note that <code>λ = 0.0</code> and <code>λ = 1.0</code> are excluded, since <code>F</code> is uniformly 0 in that case.</p> <p>Reference values of the <code>F</code> function</p> <p>The reference values of the function <code>F</code> are stored in the <code>F</code> dataset, which is a 108×108×12×9, such that <code>F[i, j, h, k]</code> is the value of <code>F</code> for</p> <pre><code>Q₁ = spheroids[i], Q₂ = spheroids[j], r₁₂ = directions[h] and λ = lambdas[k].</code></pre> <p>Note that the <code>r₁₂</code> vector takes values in the <code>directions</code> dataset. In other words, only unit-length center-to-center vectors are considered here. Indeed, <code>F</code> trivially depends on the norm of <code>r₁₂</code>, which is therefore not considered here in order to reduce the size of the dataset.</p> <p>Reference values of the contact function</p> <p>Note: the following is <em>not</em> implemented yet, as reference values of the contact function were not deemed useful. Indeed, once <code>F</code> is validated, it is straightforward to check that the implementation of <code>F₁₂</code> to be tested indeed maximizes <code>F</code>.</p> <blockquote> <p>The reference values of the contact function <code>F₁₂</code> are stored in the <code>contact_function</code> dataset, which is a 108×108×12×3 array, such that <code>contact_function[i, j, h, k]</code> is the value of <code>F₁₂</code> for</p> <pre><code>Q₁ = spheroids[i], Q₂ = spheroids[j] and r₁₂ = radii[h] * directions[k].</code></pre> <p>Note that the <code>r₁₂</code> vector is not normed, here.</p> </blockquote>
BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq
<p>Background<br> Time consuming computational assembly and quantification of gene expression and splicing analysis from RNA-seq data vary considerably. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.</p> <p>Results<br> A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al., 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al., 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5’ and 3’ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2,791 differentially alternatively spliced genes and 2,768 transcripts with differential transcript usage.</p> <p>Conclusion<br> A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.</p>
Fed4Fire/CDN-X-ALL M3AP, M2AP reference points implementation pcap datatsets for the 3GPP MBMS profile
<p>Following datasets were generated at VICOMTECH (https://www.vicomtech.org) under project/experiment CDN-X-ALL: "CDN edge-cloud computing for efficient cache and reliable streaming aCROSS Aggregated unicast-multicast LinkS".</p> <p>Project funded by Fed4FIRE+ OC5 (<a href="https://www.fed4fire.eu/">https://www.fed4fire.eu</a>) under grant 732638.</p> <p>The following data provide reference pcap captures that include relevant traces with standardized 3GPP interfaces for the 3GPP MBMS (Multimedia Broadcast Multicast Services) profile.</p> <p>Among others, traces include E-UTRAN and Evolved Packet Core pcap captures with standardized 3GPP M2AP and M3AP interfaces and UE MBMS end to end MAC layer PDUs:</p> <p>1- MBMS_OAI_M3AP_M2AP.pcap</p> <p>- OAI eNB LTE; Evolved Universal Terrestrial Radio Access Network (E-UTRAN); M3 Application Protocol (M3AP) in between an MME (Mobility Management Entity) and MCE (Multicast Control Entity) (3GPP TS 36.444 version 14.1.0 Release 14)</p> <p>- OAI eNB LTE; Evolved Universal Terrestrial Radio Access Network (E-UTRAN); M2 Application Protocol (M2AP) in between an MCE (Multicast Control Entity) and eNB (Evolved NodeB) (3GPP TS 36.443 version 14.0.1 Release 14)</p> <p>2- oai_opt_ue_mbms_SIB13_MTCH_MCCH.pcap</p> <p>- OAI UE side LTE MAC and RRC messages</p> <p>Moreover, the above data can be self-re-generated by downloading and building the original open source code pushed (as part of CDN-X-ALL project) to OpenAirInteface5G gitlab open repository. At the time of publishing these data, the original source code falls under branch (https://gitlab.eurecom.fr/oai/openairinterface5g/tree/fed4fire_fec5_cdn-x-all) and is part of ongoing merge request (https://gitlab.eurecom.fr/oai/openairinterface5g/merge_requests/673)</p>
A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples
<p>[This repository contains the source data for the manuscript "A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples" by deWaard et al., 2019. BioRxiv]</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Daily observed, modelled, and infilled river flows for an Irish hydrometric reference network of river flow stations
<p>Here we present a dataset of observed, modelled, and infilled daily river flow data relating to the newly updated Irish Hydrometric Reference Network (IHRN) of high-quality gauging stations located across the Republic of Ireland. Internationally applied selection criteria, analysis of historical observations and flow gauge metadata, stakeholder feedback, and trend assessments aided in the identification of the network’s 51 stations. A combination of the GR4J conceptual hydrological model and a backpropagation neural network driven by catchment specific precipitation and temperature extracted from gridded datasets was used to model flows that subsequently infilled gaps in the observational record for each of the series (from commencement of each station’s record till the end of 2022). Also included are the 2.5 and 97.5 quantile values for each station’s modelled data, which represent the upper and lower uncertainty bounds of the respective ensemble flows derived during the flow generation process. As well as providing a useful means for evaluating the impact of changing climatic conditions on Irish catchments, the IHRN data offers utility for assessing catchment based impacts for flow extremes, and the generation of both historical reconstructions and future climate projections for a range of flow regimes across the island of Ireland.</p>
The ScRAP yeast reference pangenome graphs
<p>The yeast reference pangenome graphs deposited here are constructed by <a href="https://www.evomicslab.org/">Evomics Lab</a> using the high-quality genome assemblies of globally-sampled <em>Saccharomyces cerevisiae </em>strains described in both the original <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) and its future extensions. </p> <p>The detailed construction specification for each graphs are described below:</p> <p>1) ScRAP_v20230121_163asm.minigraph.gfa</p> <p>This graph was built upon 163 yeast genome assemblies from 142 strains. Briefly, we took the <em>S. cerevisiae</em> reference genome (denoted as 'SGDref') retrieved from the Saccharomyces genome database (SGD) as well as 162 assemblies from our recently released <em>S. cerevisiae</em> Reference Assembly Panel (ScRAP; See <a title="O&rsquo;Donnell et al. Nature Genetics, 2023" href="https://www.nature.com/articles/s41588-023-01459-y" target="_blank" rel="noopener">O’Donnell et al. Nature Genetics, 2023</a>) to construct reference pangenome graph by using <a href="https://github.com/lh3/minigraph">minigraph</a> with the command ‘minigraph -cxggs -l 5000’. With the SGDref as the reference genome, we incrementally added those 162 assemblies of other <em>S. cerevisiae</em> strains into the graph according to their phylogenetic distances to SGDref. We further mapped each input assembly to the graph by minigraph with the command ‘minigraph -cxasm’ to calculate the corresponding mapping depth. </p> <p> </p>
Reference sheet for the dataset: BioCam ALR PLOCAN Trials (03/14/2024 – 03/16/2024)
<p>This reference document provides a brief description of the data and a link to the full data repository, which includes mapping and imagery data.</p> <p>For further information, please email <span><a href="mailto:adrian.bodenmann@soton.ac.uk">adrian.bodenmann@soton.ac.uk</a> or <a href="mailto:comms.techoceans@aquatt.ie">comms.techoceans@aquatt.ie</a>.</span></p>
gapseq reference sequence databases for Bacteria and Archaea
<p>The repository contains the protein sequences used by <a href="https://github.com/jotech/gapseq">gapseq</a> to predict the presence of metabolic reactions and to construct metabolic models.</p> <p>The workflow using gapseq to generate this set of reference protein sequences:</p> <p> </p> <p>```sh</p> <p># delete all "old" data<br>rm dat/seq/Bacteria/rev/*.fasta<br>rm dat/seq/Bacteria/unrev/*.fasta<br>rm dat/seq/Bacteria/rxn/*.fasta<br>rm dat/seq/Archaea/rev/*.fasta<br>rm dat/seq/Archaea/unrev/*.fasta<br>rm dat/seq/Archaea/rxn/*.fasta</p> <p># run gapseq find to re-download everything#<br># the genome is irrelevant as no blasting is performed ('-x')<br>gapseq find -p all -t Bacteria -n -x -U toy/ecoli.faa.gz > bac_update.log 2>&1<br>gapseq find -p all -t Archaea -n -x -U toy/ecoli.faa.gz > ar_update.log 2>&1</p> <p># create all sequence .tar.gz archives (rev/unrev/rxn)<br>cd dat/seq/Bacteria/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Bacteria/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/unrev/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../<br>cd dat/seq/Archaea/rxn/ && tar -czvf sequences.tar.gz ./*.fasta && cd ../../../../</p> <p># create md5sum table for all tar.gz archives<br>cd dat/seq/<br>find -mindepth 2 -type f -name "*.tar.gz" -exec md5sum {} \; > md5sums.txt</p> <p># create taxon-specific final archive for Zenodo upload<br>tar -czvf Bacteria.tar.gz Bacteria/*/*.tar.gz<br>tar -czvf Archaea.tar.gz Archaea/*/*.tar.gz</p> <p># Upload Bacteria.tar.gz, Archaea.tar.gz, and md5sums.txt to Zenodo via the web-interface</p> <p>```</p>
Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14070345">Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Meteocean conditions of Canary Islands</li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset: </strong></p> <p>zip that contains 243 csv files.</p> <p>Each file containing one-hour time series of load simulation (time step 0.5s). </p> <p><strong>Filenames</strong> specify details about the simulation:</p> <ul> <li>dlc - Design Load Case [12 : normal power production, 64 : idling]</li> <li>Vh [wind speed]</li> <li>Y [yaw angle]</li> <li>W [wave height _ period]</li> <li>D [wind direction]</li> <li>M [wave misalignment= wave direction with respect to wind direction]</li> </ul> <p>The <strong>columns </strong>of each csv file includes followind signals according to OpenFAST nomenclature and reference frames:</p> <ul> <li>Time (s)</li> <li>PtfmSurge (m)</li> <li>PtfmSway (m)</li> <li>PtfmHeave (m)</li> <li>PtfmRoll (deg)</li> <li>PtfmPitch (deg)</li> <li>PtfmYaw (deg)</li> <li>GenPwr (kW)</li> <li>RotThrust (kN)</li> <li>GenTq (kN-m)</li> <li>RotSpeed (rpm)</li> <li>BlPitch1 (deg)</li> <li>TipDxc1 (m)</li> <li>RootMxc1 (kN-m)</li> <li>RootMyc1 (kN-m)</li> <li>TwrBsMxt (kN-m)</li> <li>TwrBsMyt (kN-m)</li> <li>FAIRTEN1 (N)</li> <li>FAIRTEN2 (N)</li> <li>FAIRTEN3 (N)</li> </ul>
Virtual results DeltaWind + Innwind 10MW Reference Wind Turbine
<p><strong>Digitalización Offshore EU Project - CENER- Digital Twin site specific results.</strong></p> <p>Related dataset:</p> <ul> <li><a href="https://zenodo.org/records/14068807">Site-specific results DeltaWind + Innwind 10MW Reference Wind Turbine</a></li> </ul> <p>Related presentation:</p> <ul> <li><a href="https://zenodo.org/records/14067010">Digitalización de parques eólicos</a></li> </ul> <p> </p> <p>Simulation of floating offshore wind turbine</p> <ul> <li>DeltaWind platform + Innwind 10 MW Reference Wind Turbine)</li> <li>Virtual Meteocean conditions </li> <li>Depth: 350 m</li> </ul> <p>Simulations specifications:</p> <ul> <li>Simulation carried out with OpenFAST v3.4.1 version <a href="https://github.com/OpenFAST/openfast/releases/tag/v3.4.1">Release v3.4.1 · OpenFAST/openfast</a></li> <li>CENER in-house controller</li> <li>400 s transient removed</li> </ul> <p> </p> <p><strong>Dataset:</strong></p> <p>csv that contains statistics from 4320 simulations</p> <p>Statistics obtained from one-hour time series of load simulations</p> <p>- The <strong>definition</strong> of the simulations are included in the csv through the <strong>columns</strong>:</p> <ul> <li>DLC - Design Load Case [12 : production, 64 : idling]</li> <li>Wind Speed </li> <li>WaveHeight</li> <li>Wave Period</li> <li>Wind Direction</li> <li>Wave Direction</li> </ul> <p>- The <strong>statistics calculated</strong> are included in columns:</p> <ul> <li> <div>BlPitch1_avg (deg)</div> </li> <li> <div>FAIRTEN1_avg (N)</div> </li> <li> <div>FAIRTEN2_avg (N)</div> </li> <li> <div>FAIRTEN3_avg (N)</div> </li> <li> <div>GenPwr_avg (kW)</div> </li> <li> <div>GenTq_avg (kN-m)</div> </li> <li> <div>PtfmHeave_avg (m)</div> </li> <li> <div>PtfmPitch_avg (deg)</div> </li> <li> <div>PtfmRoll_avg (deg)</div> </li> <li> <div>PtfmSurge_avg (m)</div> </li> <li> <div>PtfmSway_avg (m)</div> </li> <li> <div>PtfmYaw_avg (deg)</div> </li> <li> <div>RootMxc1_avg (kN-m)</div> </li> <li> <div>RootMyc1_avg (kN-m)</div> </li> <li> <div>RotSpeed_avg (rpm)</div> </li> <li> <div>RotThrust_avg (kN)</div> </li> <li> <div>TipDxc1_avg (m)</div> </li> <li> <div>TwrBsMxt_avg (kN-m)</div> </li> <li> <div>TwrBsMyt_avg (kN-m)</div> </li> <li> <div>BlPitch1_std (deg)</div> <p> </p> </li> </ul>
Symphony pre-built single-cell reference atlases
<p>Pre-built Symphony reference objects that can be downloaded and used to map new query datasets.</p> <p>The Symphony algorithm is used to perform reference mapping to these atlases. </p> <ul> <li>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2">https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2</a></li> <li>Usage: <a href="https://github.com/immunogenomics/symphony">https://github.com/immunogenomics/symphony</a></li> </ul> <p><strong>References available for download:</strong></p> <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.rds)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.rds)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.rds)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.rds)</li> <li>T cell CITE-seq atlas (tbru_ref.rds)</li> <li>Cross-tissue Fibroblast Atlas (see <a href="https://sandbox.zenodo.org/record/772596#.YOqMiBNKhTY">here</a>)</li> <li>Cross-tissue Inflammatory Immune Atlas (<a href="https://sandbox.zenodo.org/record/888445#.YV9In2ZKijB">here</a>)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.rds)</li> </ol> <p>To read in a reference into R, one may simply execute: reference = readRDS('path/to/reference_name.rds')</p> <p>Note: To be able to map query datasets into the reference UMAP coordinates, you must also download the corresponding 'uwot_model' file and set the reference$save_uwot_path.</p>
RE-Lab-Projects/TRY_DE_2015_2045: Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale
<p>Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale</p> <p><strong>Summary:</strong></p> <p>The data set contains the updated test reference years (TRY) of the German Weather Service (DWD). By subdividing into 15 TRY regions, each postcode area can be assigned a representative weather data set. It should be emphasized that in addition to a mean, current test reference year for a region, there is also a year with extreme summer and extreme winter weather. To take climate change into account, there is then a time series for the year 2045 for each test reference year based on the IPCC climate models. This means that a total of 90 weather data sets are available with a one-hour time resolution.</p> <p>In order to use the data in simulations with a temporal resolution of 1min or 15min, the data set was extended by linear interpolation. While this approach is justifiable for air pressure and temperature, for example, it does not depict high fluctuations in solar radiation. Therefore, based on the one-minute open data measurement data set of the Baseline Surface Radiation Network, with an algorithm by Hofmann et. al. the time series of global radiation are newly generated for all test reference years. Another algorithm by Hofmann et. al. was used to calculate the corresponding diffuse radiation times series.</p> <p><strong>Sources:</strong></p> <ul> <li>Raw data from DWD: <a href="https://kunden.dwd.de/obt/">https://kunden.dwd.de/obt/</a> -> <code>1_raw-data</code></li> <li>Synthetic 1min radiation data: <a href="http://pvmodelling.org/">http://pvmodelling.org/</a> -> <code>2_synthetic-radiation</code></li> </ul> <p><strong>How to use or recreate the final dataset:</strong></p> <ol> <li>clone/download this repository</li> <li>unzip the files from the data.zip file <ol> <li><a href="https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip">https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip</a></li> </ol> </li> <li>Use or recreate the final dataset <ol> <li>use: Final datasets are then located in -> <code>3_processed-data</code></li> <li>recreate: run the <code>process-data.py</code></li> </ol> </li> </ol> <p><strong>Test reference stations / regions</strong></p> <p>No. | lon | lat | station | region<br> 1 | 53.5591 | 8.5872 | Bremerhaven | Nordseeküste<br> 2 | 54.0878 | 12.1088 | Rostock | Ostseeküste<br> 3 | 53.5299 | 10.0078 | Hamburg | Nordwestdeutsches Tiefland<br> 4 | 52.3938 | 13.0651 | Potsdam | Nordostdeutsches Tiefland<br> 5 | 51.4562 | 7.0568 | Essen | Niederrheinisch-westfälische Bucht und Emsland<br> 6 | 550.6461 | 7.9426 | Bad Marienburg | Nördliche und westliche Mittelgebirge, Randgebiete<br> 7 | 51.3334 | 9.4725 | Kassel | Nördliche und westliche Mittelgebirge, zentrale Bereiche<br> 8 | 51.7239 | 10.6069 | Braunlage | Oberharz und Schwarzwald (mittlere Lagen)<br> 9 | 50.8233 | 12.9181 | Chemnitz | Thüringer Becken und Sächsisches Hügelland<br> 10 | 50.3226 | 11.9124 | Hof | Südöstliche Mittelgebirge bis 1000 m<br> 11 | 50.4312 | 12.9522 | Fichtelberg | Erzgebirge, Böhmer- und Schwarzwald oberhalb 1000 m<br> 12 | 49.4902 | 8.4637 | Mannheim | Oberrheingraben und unteres Neckartal<br> 13 | 48.2432 | 12.5286 | Mühldorf | Schwäbisch-fränkisches Stufenland und Alpenvorland<br> 14 | 48.6536 | 9.8666 | Stötten | Schwäbische Alb und Baar<br> 15 | 47.4945 | 11.1046 | Garmisch Partenkirchen | Alpenrand und -täler</p> <p><strong>Content</strong></p> <ul> <li><strong>files</strong>: 90 test reference years (TRY) <pre><code>15 test reference regions x 3 reference conditions (average year, extreme summer, extreme winter) x 2 reference projections (year 2015 and year 2045) </code></pre> </li> <li><strong>columns per file</strong>: <pre><code>datetime [yyyy-MM-dd hh:mm:ss+01:00/02:00] temperature [degC] pressure [hPa] wind direction [deg] wind speed [m/s] cloud coverage [1/8] humidity [%] direct irradiance [W/m^2] diffuse irradiance [W/m^2] synthetic global irradiance [W/m^2] synthetic diffuse irradiance [W/m^2] clear sky irradiance [W/m^2] </code></pre> </li> <li><strong>length</strong>: 1 year</li> <li><strong>time increment</strong>: 60s / 900s / 3600s</li> </ul> <p><strong>Important hints</strong>:</p> <ul> <li>all files in <code>3_processed-data</code> were calculated with the skript <code>process-data.py</code></li> <li><em>A value with, for example, a timestamp 12:00:00 represents the mean value from this timestamp until the following timestamp.</em></li> <li><em>datetime column is in CET / CEST</em></li> </ul>
An open dataset of IPCC reports 'references (6th Assessment Cycle) – Version 1
<p>We provide a first version of an open dataset of publications cited by the IPCC reports of the 6<sup>th</sup> Assessment Cycle (<a href="https://www.ipcc.ch/reports/">Reports — IPCC</a>)</p> <p>The lists were extracted from the reference sections of three special reports and the first assessment report. [see Figure 1]. The data are presented in two formats: one on hand in text files with a list of references for each section of the reports (generally each chapter) and one other hand a structured format (json) with identifiers for the documents and the sections, the reference in string format as well as the extracted digital object identifiers (dois). In this first version, the dois extracted are mainly those which are provided in the references. The table 1 show the number of references and doi for each report.</p> <p>We plan, for subsequent releases, following enhancements:</p> <ul> <li>Further quality assurance of dois. We note that some entries are provided in references of the IPCC reports without dois although they are indexed in Crossref. The table 2 shows substantial differences in doi coverage among reference sections. Spot checks of the dataset suggest that those differences are mainly due to referencing behaviour of the section’s authors rather than on type of documents cited. We aim in the next version to complete those missing dois and systematically verify the dois included in the references.</li> <li>Expand the references lists to reports from past assessment cycles</li> </ul> <p>In addition, we plan a more detailed documentation of our dataflow & extraction process and to demonstrate how it can be used to create open, community curated, datasets of references of others non-scholarly documents.</p> <p>The json files have each two keys (1) schema with the structure of the table and (2) data: with the records.</p> <p>A simple way to read them into table is via a pandas dataframe</p> <p><em>import pandas as pd</em></p> <p><em>df = pd.read_json(file_name.json, orient = ‘table’)</em></p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>We thank Valentin Hancu (EC/DG ECFIN) - for fruitful discussions on the data extraction process.</p> <p><strong>Disclaimer: </strong></p> <p>The views expressed in this paper are the author’s. They do not reflect the views or official positions of the European Commission.</p>
Ouseley mission to Persia 1810-15 – Analysis of reference material
<p>These files form part of an archive of research data relating to the diplomatic mission to Persia 1810-15, led by Sir Gore Ouseley as ambassador. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are in the README file.. The files comprise a number of searchable listings relating to the reports produced by members of the mission and other relevant data. The data formed the basis for a published report on the Ouseley Mission referenced below, which is also part of the archive. </p> <p>This section of the archive contains a database, <em>ousref</em>, listing reference material relating to the Ouseley mission and the four key participants who wrote about their journeys. Th explanatory README text file contains details of the four reports and a table showing the fields included in the database and giving definitions of them and of the codings used in different cases. </p>
Reference Reflectance Transformation Imaging acquisitions for RTI stitching and acquisition optimization
<p>This dataset contains 1. RTI acquisitions a canvas painting and a metal print plate in parts, for development of RTI-stitching methods. 2. Dense RTI acquisitions of brushed metal and ruse coarse metal surfaces for development of methods for determining ideal light positions in a RTI acquisitions. </p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</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.