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10,939 results for “K”
Replication data for: Reconciliation k-median: Clustering with non-polarized representatives
<p># Description<br> These files contain the data employed in the experiments described in Bruno Ordozgoiti and Aristides Gionis. 2019. Reconciliation k-median: Clustering with Non-Polarized Representatives. In Proceedings of the 2019 World Wide Web Conference (WWW’19), May 13–17, 2019, San Francisco, CA, USA.</p> <p>Twitter ID's have been anonymized.</p> <p># Contents<br> domain_mentions.txt: Each line contains a domain name, a user ID and the number of times this user has mentioned this domain name in a tweet.<br> format: domain_name <TAB> user_id <TAB> mention_count</p> <p>domains_ideology_score.txt: Domain names and their ideology score, estimated as described in (Lahoti et al. WSDM 2018). Note: missing scores can be retrieved from supplementary data in https://doi.org/10.1093/poq/nfw006<br> format: domain_name <TAB> ideology_score</p> <p>follow_graph.txt: The Twitter follower graph. Each line contains a user id and the user id of one of its followers.<br> format: user_id <TAB> follower_user_id</p> <p>representatives.txt: US Congress representatives, each with Twitter handle and polarity score computed using Barbera's method (Barbera, 2015).<br> format: rep_name <TAB> website_url <TAB> district <TAB> twitter_handle <TAB> party <TAB> barbera_polarity_score</p> <p>user_polarity.txt: User ID's and polarity score computed using Barbera's method (Barbera, 2015).<br> format: user_id <TAB> barbera_polarity_score</p>
Comparative proteomics analysis of whole-cell catalyst of K. rhizophila strain SA117 catabolism of SMX
<p><span>Sulfamethoxazole (SMX), an oral sulfonamide antibiotic, presents significant environmental challenges due to its persistence and potential role in promoting antibiotic resistance. The bacterial strain <em><span>Kocuria rhizophila</span></em> SA117, isolated from polluted soils, has demonstrated a remarkable capability to metabolize SMX. Proteomic analysis revealed the presence of various enzymes and metabolic pathways that may contribute to SMX degradation, including those involved in para-aminobenzoate condensation and protocatechuate metabolism. Notably, the genome of SA117 harbors eight monooxygenase genes, including those related to antibiotic biosynthesis and flavin family monooxygenases. Additionally, several cytochrome c-encoding genes, known for their role in respiratory versatility and potential application in bioremediation, were identified. Genes associated with sulfur metabolism, including an iron-sulfur cluster gene cluster (SufB, C, D, R, E) linked to oxidative stress response, were also found. A comparative proteomic study under SMX exposure highlighted significant upregulation of stress-related proteins. These findings underscore the metabolic adaptability of <em><span>Kocuria rhizophila</span></em> SA117 and its potential application in the bioremediation of SMX-contaminated environments.</span></p>
Oxidation of Ammonia/Methanol Mixtures in a plug-flow reactor with TOF-MS at 373-973K
<p>TOF-mass spectrometric measurements with a plug-flow reactor for ammonia/methanol gas mixtures (neat, 10% and 20% methanol in ammonia for equivalence ratios 1 and 2)</p> <ul> <li>temperature range: 373-973 K</li> <li>pressure: 3 bar</li> <li>dilution: 98 %</li> </ul> <p>Dataset described, analyzed and discussed in: A. Welp, C. Rudolph, B.R. Giri, K.P. Shrestha, R. Verma, F. Mauss, and B. Atakan, Oxidation of Ammonia Methanol Blends: An Experimental and Kinetic Modeling Study, 2025, accepted for publication in Combustion and Flame.</p>
Data: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites
<p>Complementary data for the paper: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites.</p>
RESCUER: Cosmological K-corrections for star clusters
<p>**RESCUER: Cosmological K-corrections for star clusters**<br>Authors: Marta Reina-Campos and William E. Harris<br>Date: May 2024</p> <p>Manuscript arXiV ID: arXiv:2310.02307 -- Accepted by MNRAS on May 2024</p> <p>* These tables contain the K-corrections and their uncertainties calculated for star clusters using the E-MILES stellar library.<br>* The authors assumed that star clusters are well represented by single-age and metallicity simple stellar populations (SSPs) described by the BaSTi stellar isochrones and the Chabrier 2003 initial mass function.<br>* Each table corresponds to the K-corrections and their uncertainties for a given combination of filters. The authors considered eleven broad-band filters from the HST/ACS and the JWST/NIRCam cameras. <br>* The uncertainties are estimated using all models within 0.3 dex and 20% in metallicity and age space, respectively, of the target model, and they correspond to the distances to the 10-90th percentiles of the K-corrections of these models.<br>* The tables labeled "csv_homo_filter_X_" correspond to homochromatic K-corrections within the wavelength range of the filter X, whereas those labeled "csv_hetero_filter_X_filter_Y_" contain the K-correction from the observed filter X to the rest-frame filter Y.</p> <p>Within every table:<br>* The K-corrections are given in AB mags<br>* The first column represents the redshift at which the K-correction has been calculated<br>* All of the subsequent columns correspond to the redshift evolution of the K-correction (_target) and their asymmetric uncertainties (_lower and _upper) for a given stellar population, as indicated at the top<br>* The stellar populations are labeled as in the E-MILES stellar library: e.g. "Ech1.30Zm2.27T01.0000", corresponds to a SSP of [M/H] = -2.27 and 1 Gyr old<br>* Dummy values of -100 are placed in the redshifts larger than than the one allowed by the Planck 2018 cosmology.<br>* When an uncertainty equals zero indicates that the target K-correction was smaller/larger than the 10th/90th percentile of the distribution of K-corrections. This typically occurs in models at the edge of the grid of models (i.e. at a corner).</p>
Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach
<p>This dataset contains the experimentally determined fundamental parameters for the sulfur K-subshells from as shown in the publication with the title "Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach". The paper will be published soon in a peer-reviewd journal.</p> <p>This file contains the following fundamental parameters for sulfur: K-subshell fluorescence yield, K-shell Auger yield, Ka and Kb transition probabilities, K-subshell photo ionization cross sections up to 10 keV, K-subshell fluorescence prodution cross sections up to 10 keV</p>
EFSA Project on the use of NAMs to explore the immunotoxicity of PFAS (Annexes B, C, D1, E, G, I, K, M, O)
<p>In vitro raw data, RIN values and RNA concentrations, DNA quality assessment, RNAseq outputs and analysis of EFSA Project on the use of NAMs to explore the immunotoxicity of PFAS (OC/EFSA/SCER/2021/13). </p>
Classification of GTP-dependent K-Ras4B active and inactive conformational states
<p>Dataset for the paper: Classification of GTP-dependent K-Ras4B active and inactive conformational states.</p> <p>Cite as: J. Chem. Phys. 158, 000000 (2023); DOI: 10.1063/5.0139181<br> Submitted: 18 December 2022; Accepted: 13 February 2023; Published Online: 13 February 2023.</p> <p>All molecular dynamics and molecular docking data presented, analyzed, and discussed in this paper are available at reasonable<br> requests submitted to the corresponding author. The following data are also available online: (i) file KRas4B_pdbs.zip, a compressed<br> archive including coordinate (.pdb) and structure (.psf) files for KRas-4B WT and D33E proteins, (ii) KRas4B_WT_traj.zip,<br> a compressed archive including trajectory files (.trr) for the WT KRas-4B runs, including 120 "*.trr"-formatted trajectories each corresponding to 40 ns of MD simulation time, and (iii) a sample Python script to generate a free energy plot as shown in Fig. 2.</p>
Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function
<p><strong>Transmission ultrasound data simulated using the k-Wave toolbox as a benchmark for biomedical quantitative ultrasound tomography using a ray approximation to Green's function </strong></p> <p> </p> <p>The folder ‘’simulation<em>’’ </em>includes the transmission ultrasound data sets used in the project:<a href="https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography">https://github.com/Ash1362/ray-based-quantitative-ultrasound-tomography</a>. In the Github link, the associated project can be found in the branch master in the folder r-Wave #V1.1. (The folder ‘’data_ust_kWave_transmission.zip<em>’’ </em>is deprecated.)</p> <p>...........................................................................................</p> <p>The ultrasound data were simulated using the k-Wave toolbox (version 1.3.) [5] and using a digital breast phantom [4]. In k-Wave version 1.4., no changes have been reported that affects the simulations. The simulations were done assuming isotropic point sources.</p> <p>The folder ‘’simulation<em>’’ </em> must be added to the path:</p> <p><em>''…r-Wave/data/simulation/…''</em></p> <p>For running the Matlab example scripts in the project in the github, the user has two choices: </p> <ol> <li>Simulate the k-Wave ultrasound data by setting <em>data_sim=true;</em> in the examples in the project.</li> <li>Upload the already simulated k-Wave ultrasound data according to the description below and load them by setting <em>data_sim=false;</em> in the examples in the project.</li> </ol> <p>Please read the description in the example scripts!</p> <p>…………………………………………………………………………………</p> <p>The folder simulation includes 2 subfolders, ‘’phantom<em>’’ </em>and ‘’data_ust_kWave_transmission<em>’’.</em></p> <p>1) The subfolder ‘’simulation/phantom<em>’’ </em> includes ‘’OA-BREAST<em>’’. </em></p> <p>In the project: https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/,</p> <p>the user must upload the folder ‘’Neg_47_Left<em>’’ </em>, and add it as ‘’r-wave/data/simulation/phantom/OA-BREAST/Neg_47_Left/<em>’’.</em></p> <p><em>.......................................................................................................................................................................</em></p> <p>2) The subfolder ‘’simulation/data_ust_kWave_transmission’<em>’ </em>includes 2 subfolders, ‘’2D<em>’’ </em> and ‘’3D<em>’’ </em>.</p> <p>The subfolder ‘’2D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water according to section <em>‘’6.1. data simulation’’</em> in [1]. 64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach and then the Green's approach.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_plane_nonsmooth.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-wave for only water and breast in water. 64 emitters and 256 receivers are simulated as off-grid points which are placed on 16 planar arrays which are all aligned with a circle. Each planar array includes 4 emitters and 16 receivers. Therefore, in contrast with the data mentioned above, the ray linking is performed using the line equations defining the 2D geometry of the linear arrays. (The characters ‘’_plane_’’ are added to indicate that the transducers are placed on line.) To simulate the data, each emitter was individually driven by an excitation pulse, and the induced acoustic pressure time series were recorded on all the receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. This data set is used for the purpose of image reconstruction, and therefore, the sound speed and absorption coefficients maps are not smoothed, i.e., the original maps are used for simulations. This data set can be used for image reconstruction using the time-of-flight-based approach, but ahs not been extended to the Green's approach yet. The image reconstruction should be slower than the circular array. the reason is for circular array, for each emitter, the raylinking problem is solved for all receivers once using the equation of circle. However, for this data set, for each emitter, the ray linking problem is solved for each receiver array separately, because receiver arrays are defined with different line equations.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_1.mat</strong></p> <p>Two transmission ultrasound data sets were simulated using the k-Wave for only water and breast in water as the benchmark for validation of ray approximation to Green’s function in homogeneous and heterogenous media, respectively. The simulation was performed according to section <em>‘’6.2. Numerical validation of the ray approximation to the Green’s function’’</em> in [1].</p> <p>64 emitters and 256 receivers are simulated as off-grid points which are placed on a 2D circular ring. (The characters ‘’_sphere_’’ are added to indicate that the transducers are placed on a ring.) The pressure field was produced by emitter 1 (of the 64 emitters) and was recorded in time on all 256 receivers. The k-Wave simulation was performed on a grid with grid spacing 0.4 mm, and the time spacing was set using a CFL number 0.1. The acoustic absorption and dispersion were accounted for based on the frequency power law. The sound speed and absorption coefficient maps were smoothed by an averaging window of size 17 grid points. This data set is used as the benchmark for measuring accuracy of ray approximation to Green’s function for computing phase and amplitude of the pressure field on the receivers.</p> <p><strong>data_ust_kWave_transmission/2D/PulsePammoth_1_dx4_cfl1_Nr256_Ne64_Interpoffgrid_Transgeompoint_Absorption1_CodeMatlab/data4_sphere_smooth_17_20.mat</strong></p> <p> This data set is the same as data4_smooth_17_1 except the pressure field is produced by emitter 20.</p> <p>………………………………………………………………………………………………………………….</p> <p>The subfolder ‘’3D<em>’’ </em> includes:</p> <p><strong>data_ust_kWave_transmission/3D/PulsePammoth_1_dx5_cfl1_Nr4096_Ne1024_Interpnearest_Transgeompoint_Absorption0_CodeCUDA/data5_sphere_nonsmooth_tof_singram.mat</strong></p> <p>The discrepancy of time-of-flight data for two transmission ultrasound data sets simulated by the k-wave for breast in water and only water according to section 5.2 in [3]. The pressure fields were produced by 1024 emitters separately and were recorded on 4096 receivers. The emitters and receivers were simulated as points which are placed on a 3D hemispherical surface, and are interpolated onto the grid using a neighboring interpolation. The k-Wave simulations were performed on a grid with grid spacing 0.5 mm, and the time spacing was set using a CFL number 0.1. The time-of-flight data were computed and will be used for a refraction-corrected image reconstruction of the sound speed based on the inversion approach proposed in [3].</p> <p><strong>References</strong></p> <p>1 - A. Javaherian, ❝Hessian-inversion-free ray-born inversion for high-resolution quantitative ultrasound tomography❞, 2022, <a href="https://arxiv.org/abs/2211.00316/">https://arxiv.org/abs/2211.00316/</a> .</p> <p>2 - A. Javaherian and B. Cox, ❝Ray-based inversion accounting for scattering for biomedical ultrasound tomography❞, Inverse Problems vol. 37, no.11, 115003, 2021. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/">https://iopscience.iop.org/article/10.1088/1361-6420/ac28ed/</a></p> <p>3- A. Javaherian, F. Lucka and B. T. Cox, ❝Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast❞, Inverse Problems, 36 125010. <a href="https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/">https://iopscience.iop.org/article/10.1088/1361-6420/abc0fc/</a> </p> <p>4- Y. Lou, W. Zhou, T. P. Matthews, C. M. Appleton and M. A. Anastasio, ❝Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging❞, J. Biomed. Opt., vol. 22, no. 4, pp. 041015, 2017. <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p> <p>5 - B. E. Treeby and B. T. Cox, ❝k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields❞, J. Biomed. Opt. vol. 15, no. 2, 021314, 2010. <a href="http://www.k-wave.org/">http://www.k-wave.org/</a></p>
Integrated freshwater abundance and connectivity clusters at the Hydrologic Unit 8 scale for the Midwest and Northeast U.S.A. – freshwater metric variables and k-means cluster assignment
This dataset includes integrated freshwater abundance and connectivity cluster output, principal component scores, and lake, wetland, and stream abundance and connectivity metrics measured at the Hydrologic Unit 8 (HU8) scale for 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of the integrated freshwater landscape that includes lakes, wetlands, and streams and their surface connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). The integrated freshwater clusters were created through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics for lakes, streams, and wetlands separately, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of freshwater abundance and connectivity in the landscape.
Freshwater connectivity clusters for lakes, wetlands, and streams at the Hydrologic Unit 12 scale in the Midwest and Northeast U.S.A. – freshwater metric variables and K-means cluster assignment
This dataset includes freshwater connectivity cluster output and principal component scores for lakes, wetlands, and streams measured at the Hydrologic Unit 12 (HU12) scale in 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of freshwater connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). Freshwater connectivity clusters were created separately for lakes, wetlands, and streams through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of lake, wetland, and stream connectivity in the landscape.
Approximate sum of squares decompositions for Adj₅ + k·Op₅ - λΔ₅ ∈ ISAut(F₅)
<p>This is the dataset accompanying <em>On property (T) for Aut(Fₙ) and SLₙ(</em>ℤ<em>) </em>paper (https://arxiv.org/abs/1812.03456). See the appendix thereof and Section 4 of (<a href="https://arxiv.org/abs/1712.07167">Aut(F₅) has property (T)</a>) for more details.</p> <p><strong>Content</strong></p> <ol> <li><code>1812.03456-cf6dee7.zip</code> contains a julia environment specification (<code>Project.toml</code> and <code>Manifest.toml</code>) as well as <code>1812.03456.jl</code> script used for automatic certification and jupyter noteboks in <code>./notebooks</code> directory.</li> <li><code>SAutF5_r2.tar.xz</code> contains the precomputed solutions for expressing <code>Adj₅+2·Op₅-0.28Δ₅</code> and <code>Adj₅+3·Op₅-1.4Δ₅</code> as sum of (hermitian) squares in the group ring of <code>SAut(F₅)</code>. The contents of this archive must be placed inside `<code>1812.03456`</code>directory from the <code>zip</code> file.</li> </ol> <p><strong>Preparation</strong></p> <p>The code needs to be run with <code>julia-1.4.0</code> or higher (tested versions include also versions <code>julia-1.5</code>). In principle any version in <code>[1.4-2.0)</code> should work due to the promise of forward compatibility.</p> <p>While located in the main directory (<code>1812.03456</code>) you should run the following code in <code>julia</code>s <code>REPL</code> console to instantiate the environment for computations:</p> <pre><code class="language-julia">using Pkg Pkg.activate(".") Pkg.instantiate()</code></pre> <p>(this needs to be done once per installation). Then the directory <code>SAutF5_r2</code> (from the <code>SAutF5_r2.tar.xz</code> archive) needs to be placed in <code>1812.03456</code>.</p> <p><strong>Replication: Jupyter notebook</strong></p> <p>A jupyter server may be launched then within the directory <code>1812.03456</code> by issuing from julia command-line (<code>REPL</code>) the following commands.</p> <pre><code>using Pkg Pkg.activate(".") using IJulia notebook(dir=".")</code></pre> <p>During the first run the user may be asked for installation of <code>Jupyter</code> program (a server for running this notebook) within <code>miniconda</code> environment, which will happen automatically after confirmation. To execute the commands in the notebook, one needs to navigate to <code>notebooks</code> subdirectory of <code>1812.03456</code> and click either of the notebooks.</p> <p>One can replicate the main computational results of the paper by executing all the cells in the <code>Positivity of Adj_n + kOp_n in ISAut(F_n)</code> notebook.</p> <p><strong>Replication: script</strong></p> <p>To verify that <em>(Adj₅ + 3.0·Op₅) - 1.4·Δ₅</em> admits an approximate sum of squares decomposition run in <code>1812.03456</code> directory</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 3 -l 1.4</code></p> </blockquote> <p>On a modern laptop computer this should finish in less than 2h.</p> <p>At the end of computations you will see lines such as:</p> <blockquote> <p>┌ Info: λ is certified to be ><br> └ λ_cert.lo = 1.3701131733828074<br> [ Info: i.e Adj_5 + 3.0·Op_5 - (1.3701131733828074)·Δ_5 ∈ Σ²₂ ISAut(F_5)</p> </blockquote> <p>This means that <em>Adj₅ + 3.0·Op₅ - λΔ₅</em> is a sum of Hermitian squares of elements from <em>ISAut(F₅)</em> for every <code>λ < 1.370....</code></p> <p>A similar verification for <em>Adj₅ + 2.0·Op₅ - 0.28·Δ₅</em> can be run by executing</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 2 -l 0.28</code></p> </blockquote> <p><strong>Generating the provided files</strong></p> <p>If you wish to produce the whole certificate on your own (including the generation of group ring and its multiplication table), delete all <code>*.jld</code> files from the <code>SAutF5_r2</code> folder and run one of the above commands with the same (or different) parameters again. Note: To do this you need at least 16GB of RAM and spare 24h of your CPU.</p> <p>This research was supported in part by National Science Center, Poland, grant 2017/26/D/ST1/00103.</p>
Human TWIK-related Acid-Sensitive K+ Channel 1 (TASK1): A Target Enabling Package
<p>The TWIK related acid-sensitive K<sup>+</sup> channel 1 (<a href="https://www.ncbi.nlm.nih.gov/gene/3777">TASK-1</a>) belongs to the family of two-pore domain potassium (K<sub>2P</sub>) channels. It regulates resting membrane potential and is expressed in cardiomyocytes, neurons and vascular smooth muscle cells. Loss of function mutations in TASK-1 lead to primary pulmonary hypertension type 4 (PPH4) which is often fatal in mid-life (1). We have produced TASK-1 and determined structures of this protein alone and in complex with two highly potent inhibitors, BAY 1000493 and BAY 2341237, with EC<sub>50</sub> values of 9.5 nM and 7.6 nM, respectively. We have used a two-electrode voltage clamp assay to measure the effect of mutations in TASK-1 and the effect of inhibitors. The native structure of TASK-1 also allowed us to map the six known disease mutations leading to PPH4.</p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Dataset of publication "Speed of Sound Measurements in Helium at Pressures from 15 to 100 MPa and Temperatures from 273 to 373 K"
<p>This is a dataset of the speed of sound in helium, which was measured along five isotherms in a temperature range from 273 to 373 K at pressures from 15 to 100 MPa with a relative expanded uncertainty (k = 2) from 0.02 to 0.04%. A dual-path pulse-echo device was utilized to conduct these measurements.</p>
3d Transition Metal K-edge XANES Dataset for Machine Learning Models
<p><strong>Data</strong><br><br>This dataset contains machine learning data for K-edge X-ray Absorption Near-Edge Structure (XANES) prediction models for eight 3d transition metals (Ti -Cu).</p> <ul> <li><strong>features_and_spectra:</strong> Material features (X) and corresponding XAS spectra (y) for each dataset split: training (train), validation (val), and test.</li> <li><strong> material_id_and_site:</strong> Material identifiers and site indices (according to <a href="https://github.com/AI-multimodal/Lightshow">Lightshow</a>) for each dataset split. </li> </ul> <p><strong>Funding</strong><br><br>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, at Brookhaven National Laboratory under Contract No. DE-SC0012704 and by Brookhaven National Laboratory (BNL), Laboratory Directed Research and Development (LDRD) grant no. 24-004.</p> <p> </p>
Correlation Between Insulation Resistance and Temperature Measurement Error in Type K and Type N Mineral Insulated, Metal Sheathed Thermocouples
<p>Mineral insulated, metal sheathed (MI) Type K and Type N thermocouples are<br> widely used in industry for process monitoring and control. One factor that limits<br> their accuracy is the dramatic decrease in the insulation resistance at temperatures<br> above about 600 °C which results in temperature measurement errors due to electrical<br> shunting. In this work the insulation resistance of a cohort of representative MI<br> thermocouples was characterised at temperatures up to 1160 °C, with simultaneous<br> measurements of the error in indicated temperature by in situ comparison with a reference<br> Type R thermocouple. Intriguingly, there appears to be a systematic relationship<br> between the insulation resistance and the error in the indicated temperature. At<br> a given temperature, as the insulation resistance decreases, there is a corresponding<br> increasingly negative error in the temperature measurement. Although the measurements<br> have a relatively large uncertainty (up to about 1 °C in temperature error and<br> up to about 10 % in insulation resistance measurement), the trend is apparent at all<br> temperatures above 600 °C, which suggests that it is real. Furthermore, the correlation<br> disappears at temperatures below about 600 °C, which is consistent with the<br> well-established diminution of insulation resistance breakdown effects below that<br> temperature. This raises the intriguing possibility of using the as-new MI thermocouple<br> calibration as an indicator of insulation resistance breakdown: large deviations<br> of the electromotive force (emf) in the negative direction could indicate a correspondingly<br> low insulation resistance.</p>
Dataset: Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample
<p>Original measurement and processed data of the study <em>Infrared-radiofluorescence: dose saturation and long-term signal stability of a K-feldspar sample </em>submitted for review to Radiation Measurements. The data are structured as follows:</p> <ol> <li><strong>Measurement data </strong></li> <li><strong>Processed data</strong></li> </ol> <p>Experiments were carried out at the Archéosciences Bordeaux (UMR 6034, CNRS - Université Bordeaux Montaigne; former IRAMAT-CRP2A) in Bordeaux (France) and at the Département des sciences de la Terre of the Université du Québec à Montréal (Canada). The subfolders are organised by the laboratory where the experiments were carried out: spectrometer measurements in Montréal (00_Montreal_Spectrometer) and spatially resolved measurements (camera) in Bordeaux (10_Bordeaux_Camera). </p> <p><strong>Measurement data </strong>contains sequence files used to run the experiments (so-called *.lseq files) as well as the raw, unaltered measurement output in the form of files with the ending *.xsyg and *.tiff. For the camera measurements in Bordeaux, the system returned a couple of single TIFF files. We merged those files in two files, one for <em>RF<sub>nat</sub></em> and <em>RF<sub>reg</sub></em>, for convenience reasons. The data are, however, unprocessed. </p> <p><strong>Processed data</strong> is organized like the measurement data folder containing all kinds of semi-automated processed data (PDF files, images). All data were processed with the R (R Core Team, 2021) package 'Luminescence' (Kreutzer et al., 2012; 2021) and an <em>ImageJ </em>macro detailed in Mittelstraß and Kreutzer (2021)</p> <p> </p> <p><strong>References</strong></p> <p>Kreutzer, S., Schmidt, C., Fuchs, M.C., Dietze, M., Fischer, M., Fuchs, M., 2012. Introducing an R package for luminescence dating analysis. Ancient TL 30, 1–8.</p> <p>Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Smedley, R.K., Christophe, C., Zink, A., Durcan, J., King, G.E., Philippe, A., Guérin, G., Riedesel, S., Autzen, M., Guibert, P., Mittelstrass, D., Gray, H.J., 2021. Luminescence: Comprehensive luminescence dating data analysis. CRAN. https://doi.org/10.5281/zenodo.4729933</p> <p>Mittelstraß, D., Kreutzer, S., 2021. Spatially resolved infrared radiofluorescence: single-grain K-feldspar dating using CCD imaging. Geochronology 3, 299–319. https://doi.org/10.5194/gchron-3-299-2021</p> <p>R Core Team, 2021. R: A language and environment for statistical computing. https://www.r-project.org</p> <p> </p> <p> </p>
ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> </p> <p>Included files: </p> <ul> <li>modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS</li> <li>modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS </li> <li>bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model </li> <li>COSP_vars_bx400_2015-2019.nc - model data for analysis </li> </ul> <p>The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval</p>
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
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.