Hoffmann, Daniel

2 files · 6.3 MB · pdf, zipdeclared

An introductory course on quantitative concepts in biology for undergraduates in biology and natural sciences.

Modiri, Ehsan · Shrestha, Pallav Kumar · Samaniego Eguiguren, Luis Eduardo

100 files · 49 GB · netcdf, tardeclared

Historical Hydrological Simulations over the South African Domain (1990-2024) The mHM's simulations of the Planet4Health project This dataset contains historical hydrological simulations for the South African domain (domain 1020011530) conducted with the Mesoscale Hydrological Model (mHM) at a spatial resolution of 0.015625°. The simulation period spans 1990-2024 and was part of the Planet4Health (P4H) project, utilising the ERA5 meteorological forcing. This archive is prepared for DOI assignment and ensures long-term reproducibility. It includes relevant clipped NetCDF components for soil moisture layers 4, 5 and 6, consistent with the infrastructure provided within the Helmholtz Centre for Environmental Research (UFZ). The simulations were executed using a specific version of the mHM model with the SCC method for gauges, paired with the mRMv1.0 routing configuration. 🛰️ Simulation Details Model: Mesoscale Hydrological Model (mHM) Codebase: scc_for_gauges branch (https://git.ufz.de/shresthp/mhm/-/tree/scc_for_gauges?ref_type=heads) Spatial resolution: 0.015625° Temporal resolution: Daily Simulation period: 1990-2024 Simulation type: Historical simulation Spin-up: 30-year spin-up using 1990-2019 ERA5 climatology Model version: v1.0 Setup Scope: Model run for domain 1020011530, post-processed and clipped. Configuration & Modules The configuration utilises standard structural components with the SCC methodology. Modules included: Snow processes: Degree-day method Soil moisture: Feddes equation for evapotranspiration reduction Infiltration: Multi-layer Brooks-Corey-like approach Direct runoff: Linear reservoir exceedance method Potential evapotranspiration: Hargreaves-Samani method Interflow: Storage reservoir with nonlinear outflow Groundwater: Linear reservoir Routing: Adaptive time-step routing with mRMv1.0 mechanisms 📥 Input Datasets Meteorological Forcing: ERA5 (Hersbach et al., 2020) at a native input meteorological resolution of 0.25°, dynamically downscaled/mapped to model requirements. Processing Infrastructure: Tracked, processed, and validated under the Planet4Health deployment pipeline (https://git.ufz.de/planet4health/mhm_production/-/tree/main/postproc?ref_type=heads). Data Interfaces: Climate Data Interface version 2.2.4 (CDI) | Climate Data Operators version 2.2.2 (CDO) | NetCDF Operators version 5.1.7 (NCO). 📤 Output Variables sm_l04: Volumetric soil moisture layer 4 (300-500 mm depth) [mm mm-1, fraction between 0 and 1] sm_l05: Volumetric soil moisture layer 5 (500-1000 mm depth) [mm mm-1, fraction between 0 and 1] sm_l06: Volumetric soil moisture layer 6 (1000-2000 mm depth) [mm mm-1, fraction between 0 and 1] 📫 Contact Ehsan Modiri - ehsan.modiri@ufz.de Pallav Kumar Shrestha - pallav-kumar.shrestha@ufz.de Institution Helmholtz Centre for Environmental Research - UFZ, Department of Computational Hydrosystems

Metz, Markus · Rocchini, Duccio · Neteler, Markus

12 files · 858 MB · png, zipdeclared

Seamless and gap-free Land Surface Temperature (LST) dataset for Europe and neighbouring countries with a temporal resolution of four records per day and enhanced spatial resolution of 250 m. Covers the period from 2001-2011. Includes BIOCLIM-like European LST maps derived from 10 years of reconstructed MODIS LST. Date of file export: 2014-08-25 Short Overview: https://neteler.org/blog/eurolst-seamless-gap-free-daily-european-maps-land-surface-temperatures/ Dataset overview: BIOCLIM-like European LST maps following the 'Bioclim' definition (Hutchinson et al., 2009), derived from 10 years of reconstructed MODIS LST as GeoTIFF files, 250m pixel resolution, in EU LAEA projection: BIO1: Annual mean temperature (°C*10): eurolst_clim.bio01.zip (MD5), 72MB BIO2: Mean diurnal range (Mean monthly (max - min tem)): eurolst_clim.bio02.zip (MD5), 72MB BIO3: Isothermality ((bio2/bio7)*100): eurolst_clim.bio03.zip (MD5), 72MB BIO4: Temperature seasonality (standard deviation * 100): eurolst_clim.bio04.zip (MD5), 160MB BIO5: Maximum temperature of the warmest month (°C*10): eurolst_clim.bio05.zip (MD5), 106MB BIO6: Minimum temperature of the coldest month (°C*10): eurolst_clim.bio06.zip (MD5), 104MB BIO7: Temperature annual range (bio5 - bio6) (°C*10): eurolst_clim.bio07.zip (MD5), 132MB BIO10: Mean temperature of the warmest quarter (°C*10): eurolst_clim.bio10.zip (MD5), 77MB BIO11: Mean temperature of the coldest quarter (°C*10): eurolst_clim.bio11.zip (MD5), 78MB Each ZIP file contains the respective GeoTIFF file (for cell value units, see below), the color table as separate ASCII file and a README.txt with details. LICENSE: Open Data Commons Open Database License (ODbL) https://opendatacommons.org/licenses/odbl/ Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS V005.

França, Daniel

1 files · 288 KB · zipdeclared

Python library for high-dimensional Takagi-Sugeno-Kang (TSK) fuzzy inference systems, built on PyTorch with a scikit-learn compatible API.

Añorve, Christopher · Reyes-Amador, Ulises · de Ramon Toledo, Diego · et al.

1 files · 3.6 MB · zipdeclared

A surface brightness analysis and measurement tool for GALFIT output.

Gearty, William · Jones, Lewis A.

1 files · 3.7 MB · zipdeclared

PhyloPic API responses and parsed images are now cached in a temporary in-memory R environment to speed up repeated calls (#123) The cache is cleared when the R session ends The cache can also be manually cleared using clear_phylopic_cache() Added support for using PhyloPic silhouettes as vertices when plotting {igraph} networks via a new "phylopic" vertex shape, registered automatically when both packages are loaded (#115, #118) Added new "Network plots" sections to both advanced vignettes, demonstrating the new {igraph} integration in base R and the use of geom_phylopic() inside {ggraph} plots Deprecation: The "ysize" and "size" arguments/aesthetics are now fully deprecated in favor of "height" and "width" arguments/aesthetics. These arguments/aesthetics will be removed in a future version of rphylopic.

Wijata, Agata · Ruszczak, Bogdan · Niepala, Adriana · et al.

6 files · 88 GB · zipdeclared

Task The primary task is the detection of bare soil areas in Earth observation data. This is an important step in Precision Agriculture (PA) applications related to quantifying soil parameters and quality. Accurately identifying bare soil allows researchers to isolate the spectral response originating directly from the soil surface, which enhances the reliability of subsequent analyses aimed at estimating crucial soil properties, such as moisture content, nutrient levels, organic matter, and texture. Bare soil identification is also essential for monitoring agricultural practices like tillage and assessing soil erosion risks. While bare soil detection is commonly addressed at the pixel level (classifying pixels as soil or background), HyBEAR 🐻 aims to support the development of methods that identify entire fields with no vegetation (entire agricultural parcels). Dataset HyBEAR 🐻 is introduced as a novel large-scale collection of high-resolution hyperspectral aerial images. It is the largest and most heterogeneous dataset for bare soil detection released to date. Size and Scale: The dataset contains 1,954 hyperspectral image patches, totaling 108,064,591 pixels, corresponding to 43,225 hectares. The compressed dataset has a total size of 96 [GB]. Resolution: The Ground Sampling Distance (GSD) is 2 [m]. Acquisition: Data was acquired by QZ Solutions in Southern Poland on March 3, 2021. The imaging system used was the HySpex VS-725 (Norsk Elektro Optikk AS), flown on a Piper PA-31 Navajo aircraft. Spectral Information: 430 spectral bands are captured for each pixel, covering the range 414.1-2357.4 [nm]. This includes data from two sensors: SWIR-384 (288 bands, 930-2500 nm) and VNIR-1800 (186 bands, 400-1000 nm). Location and Heterogeneity: Data was collected for two areas: P1 (Lower Silesian Voivodeship, near Przeworno) and P2 (Opolskie Voivodeship, south of Głubczyce). These areas are geographically separated by more than 60 km, and images were acquired within an hour of each other, introducing variability in acquisition conditions and contributing to the dataset's heterogeneity. Annotations (Ground Truth - GT): GT was meticulously prepared using a combination of automated and manual interpretation methods, verified by domain experts. Manual labeling leveraged RGB, NDVI, and especially CIR (Color Infrared) compositions to accurately delineate bare soil. The annotations are binary: SOIL class is encoded as ( 1 ). NON-SOIL class is encoded as ( 0 ). Background/No Data pixels are encoded as ( -9999 ). Data Structure: The data consists of square patches of fixed dimensions 250x250 pixels. Versions: The dataset is available in two versions: FULL (the complete collection of 1,954 patches) and MINI (a random, stratified subset of 250 images, 50 from each fold). Validation Procedure and Baseline Results HyBEAR defines a standardized validation procedure, protocols, and quality metrics to ensure reproducibility and unbiased confrontation of emerging algorithms. Cross-Validation: A five-fold cross-validation protocol is defined using 5 spatially-disjoint folds (F0 to F4). Fold F0 represents map P1, and F1-F4 represent map P2. This spatial splitting is designed to evaluate the algorithms' ability to generalize to new, unknown areas and verify their robustness to variable acquisition conditions. Evaluation Metrics: Performance is assessed using standard classification and segmentation metrics: Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), F-score (F1), Intersection over Union (IoU), Matthews Correlation Coefficient (MCC), and the Area Under the ROC Curve (AUC). Baseline Results: Baseline results were established using classic Machine Learning (ML) models operating on 430-size feature vectors (all spectral bands per pixel). For the FULL dataset, the Logistic Regression (LR) and Support Vector Machines (SVM) models achieved the highest performance. The average accuracy (ACC) for LR was 0.927 ± 0.016, and for SVM 0.926 ± 0.016. Instructions and Availability The HyBEAR dataset, along with code and trained baseline models, is released to ensure full reproducibility of bare soil detection research. Availability: HyBEAR is published on Zenodo. DOI: https://doi.org/10.5281/zenodo.17607897 . Code: The accompanying package includes Python code (Jupyter Notebooks) for displaying data, reproducing benchmark results, training baseline models, and configuration files necessary to process the dataset. Models: Trained Logistic Regression and Support Vector Machine models (10 files in total) are delivered under the suggested 5-fold cross-validation regime. Citation @article{2026HyBEAR, title = {{HyBEAR} : A Large-Scale Hyperspectral Benchmark for Bare Soil Detection}, author = {Wijata, Agata M. and Ruszczak, Bogdan and Niepala, Adriana and Gumiela, Micha\l{} and Smykala, Krzysztof and Long\'ep\'e, Nicolas and Nalepa, Jakub}, journal = {Earth System Science Data (ESSD)}, year = {2026}, volume = {TBD}, % To Be Determined pages = {TBD}, doi = {10.5194/essd-2026-64} } The dataset files HyBEAR_MINI.zip - 250 images (50 images for each fold of 5 folds) plus: all the metadata, Python code examples, baseline ML models, and configuration files. HyBEAR_F0_FULL.zip - 310 images from fold 0 HyBEAR_F1_FULL.zip - 339 images from fold 1 HyBEAR_F2_FULL.zip - 344 images from fold 2 HyBEAR_F3_FULL.zip - 350 images from fold 3 HyBEAR_F4_FULL.zip - 361 images from fold 4

Fournier, Patrick · Larribe, Fabrice

1 files · 476 KB · zipdeclared

Ancestral Recombination Graph Modelling & Inference in Julia.

Rumley, Sébastien · ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (EPFL) · School of Engineering and Architecture of Fribourg

1 files · 501 KB · zipdeclared

Data-Cockpit is a suite of integrated software tools designed for configuring, executing, and visualizing digital experiments. A digital experiment involves running a software application based on predefined hypotheses and parameters to perform calculations and generate results. Experiment plans involve multiple runs of the software with varying configurations, enabling users to compare the impact of different parameters, hypotheses, or strategies on the outcomes. Data-Cockpit facilitates the entire process, from the setup and execution of experiments to the analysis of data generated by Java programs.

von Stockhausen, Annette

1 files · 213 KB · zipdeclared

This dataset contains the TEI-Schema and its documentation

Chestnova, Elena · Weidmann, Dieter

1 files · 87 MB · zipdeclared

This dataset contains gold standard transcriptions of manuscripts from ca. 1856-1863. The pages contain handwritten text in German Kurrent with additions in Latin and Greek scripts. The majority of the text is in German with occasional additions in Greek, Latin, French, and English. The handwritten text is in ink from a variety of hands who were making fair copies from draft book manuscripts of Gottried Semper (1803-1879), German and Swiss architect and art theorist. Additions in lead and ink are from the hand of Gottfried Semper himself. Identifiable hands include Gottlieb Baumann (1828-1900), Hans Semper (1845-1920), Elisabeth Semper (1836-1872), page numbers in lead by Wolfgang Herrmann (1899-?). These transcriptions were created for later processing and publication as part of the Semper Edition, www.semper-edition.ch. The transcription work was completed between October 2017 and August 2023. The text was transcribed with initially with the Semper HTR model and subsequently with the German_Kurrent_XIX_pylaia model in Transkribus. In both cases the automatic HTR was corrected by an expert human transcriber. The long German "s" ("ß") has been transcribed as "ss". Other orthographic specificities of the time of creation were transcribed verbatim. This work was undertaken as part of a project "Gottfried Semper. Style. Critical and commented edition" conducted over the period 2017 - 2028 jointly by the Università della Svizzera Italiana and the ETH Zürich and funded by the Swiss National Science Foundation. The documents transcribed here are preserved within the fonds of the gta Archives at ETH Zürich. Each folder represents a document as it is described in its archival context. In each folder you will find sub-folders containing page-xml and alto-xml transcriptions. The images of the transcribed documents are not included in this dataset but are available over IIIF. Each XML file contains a URL for the corresponding image. In the PAGE files this is found as part of the Metadata element and in ALTO files - as part of the sourceImageInformation element.

Prunayre, François · García, Jose · Ticheler, Jeroen · et al.

1 files · 90 MB · zipdeclared

GeoNetwork is a catalog application to manage spatial and non-spatial resources. It is compliant with critical international standards from ISO, OGC and INSPIRE. It provides powerful metadata editing and search functions as well as an interactive web map viewer.

Fliedner, Niels Hendrik

1 files · 353 KB · zipdeclared

A Rapid Data-Driven Autoconfiguration Tool for 5G Base Stations.

D'Souza, Jennifer · Sadruddin, Sameer · Rula, Anisa · et al.

1 files · 25 MB · zipdeclared

This dataset contains the materials used to develop and evaluate SciSchema.org : a collection of machine-actionable schemas for describing scientific processes. It includes the final expert-annotated schemas, intermediate model-generated schemas, domain-expert feedback, source materials, community-building documents, and analysis outputs. Navigating the dataset core/ contains one folder for each of the 16 scientific processes. For each process, master-schema/ holds the final expert-annotated schema in JSON Schema ( .json ) and SHACL, serialized in Notation3 ( .n3 ). The stage-* folders preserve the process descriptions, model-generated schemas, and expert feedback from successive development stages; metadata.csv records process-level metadata. raw-expert-feedback/ contains the exported expert-feedback form responses in TSV format. community-building/ contains calls for participation and instructions used during community data collection and schema development. paper-analysis/ contains analysis data, scripts, and generated figures used for the accompanying paper. This includes token-length analyses, structural schema-complexity analyses, final expert-rating analyses, and figure-generation scripts. The subfolder paper-analysis/figure_3/ contains the data, script, and outputs for the Data Overview section figure in the paper, summarizing the structural characteristics of the 16 final expert-annotated master schemas. For direct reuse, begin with core/<process>/master-schema/ . Use the staged materials, raw feedback, and paper-analysis/ files when examining how a master schema was developed or reproducing the analyses reported in the accompanying paper. Online access A live version of SciSchema.org is actively maintained. Persistent access to the collection is provided through the SciSchema W3ID namespace . The schema catalogue provides persistent access to the published schemas. The development repository contains the JSON Schema files served by the website. This archive is a versioned research snapshot intended to accompany its Zenodo record and the associated paper. Use the live resource for current schemas and this archived version for reproducible reference. License This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise stated. Reusers may share and adapt the materials, including the schemas, provided that appropriate credit is given and adaptations are distributed under the same license. Analysis scripts included in this archive are provided for reproducibility. Unless otherwise indicated, they may be reused under the same license as the dataset. Related Data Descriptor This dataset accompanies the Data Descriptor SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions . Citation If you reuse this dataset, please cite the Zenodo record. The published Data Descriptor citation is forthcoming.

Louis Moresi · Julian Giordani · Matt Knepley · et al.

1 files · 134 MB · zipdeclared

Underworld3 v3.1.0 Highlights Units and Scaling System — first-class dimensional quantities across the solver stack: write models in physical units (Pa·s, km, K) and let the framework non-dimensionalise and re-dimensionalise automatically. Mesh Adaptation infrastructure — the Winslow mesh smoother and MMPDE mesh mover are now formally supported, providing the foundation for anisotropic adaptive remeshing workflows. 40+ merged feature PRs since v3.0.1 — including Nitsche BCs, mesh deformation capability gate, semi-Lagrangian improvements, projection enhancements, and Darcy sign-convention corrections. Supported (validated) <!-- Features whose tier_a/b validation passed on this release. Guaranteed. --> Units and Scaling System — Pint-backed dimensional quantities, non-dimensionalisation, and unit-aware expression arithmetic. Winslow Mesh Smoother — Parallel-safe interior mesh smoothing (smooth_mesh_interior) — the foundation the adaptive movers and geometric multigrid build on. Constant Nullspace (singular scalar solvers) — SNES_Scalar.constant_nullspace returns the minimum-norm solution for pure-Neumann / closed-manifold scalar problems whose operator has a constant kernel (the singular Poisson case). Preview (present, unguaranteed) <!-- Code is on main but NOT guaranteed to work. Use at your own risk. --> MMPDE Mesh Mover / Adaptive Remeshing — follow_metric / OT mesh movement and variable transfer for anisotropic mesh adaptation. Geometric Multigrid / FMG Preconditioner — Automatic geometric full-multigrid preconditioning for the SNES solvers on refinement meshes (the preconditioner property). Bug Fixes Darcy transient velocity sign and sign-convention corrected (#255) Mesh variable arithmetic with unit-aware operands (#282, #283) Stokes bodyforce setter now accepts UWQuantity components (#284) Boundary normals and domain membership now track deformed meshes (#264) CLI -uw_* parameter overrides now apply regardless of platform (#280) PETSc IS size query during boundary rebuild avoided (#287) New Features Mesh deformation capability gate — public mesh.deform() API with safe cache invalidation, replacing the internal mesh._deform_mesh() (#188) Nitsche boundary conditions for weakly-imposed constraints (#275) Semi-Lagrangian traceback with old-frame interpolation (#186) Monotone advection-diffusion options (#234) Projection linear solver enhancements (#281) Improvements Mesh smoother and MMPDE mover robustness improvements (#190, #225, #228) Units system extended to cover mesh variable arithmetic and coordinate evaluation Stokes constrained solver parallel correctness (#265) Breaking Changes mesh._deform_mesh() now raises if the mesh carries live variables — use the public mesh.deform() instead (#188) Contributors Thanks to everyone who contributed to this release: Louis Moresi, Thyagarajulu Gollapalli, Ben Knight, Neng Lu, Julian Giordani, Juan Carlos Graciosa, Saurabh Shukla, @tiannh7.

Clayden, Jonathan D · Muñoz Maniega, Susana · Deligianni, Fani · et al.

1 files · 64 MB · zipdeclared

The track script could produce incomplete results when more than one seed region was specified – for example a named region matching several parcellation labels – with the default Strategy:global . Only one of the requested regions ended up contributing to the output. This has been corrected, and the whole seed area is now always combined into a single result, as documented. Profile outputs now have named columns where region names are available, as intended. The reg-nonlinear script will no longer produce an error when the named transform does not already exist (although performing linear registration first is still recommended). The values script will no longer fail if a mask image is not specified. Using the apply script's "Combine" mode with a function that reduces each image to a short vector, rather than operating pointwise, could cause an error. This has been fixed. The smooth script with the WidthType:fwhm option previously produced a Gaussian kernel of the wrong width, due to an operator-precedence bug in the underlying calculation. This has been corrected. Reading a corrupted or truncated MRtrix .tck file with an incomplete header could cause TractoR to hang indefinitely rather than reporting an error. This has been fixed. Reading and mapping .trk files, for example with trkmap , has been made more robust. An invalid seed-point index, or streamline coordinates lying outside the image bounds could previously cause a crash, and a separate, rare defect could cause streamline "seed" properties to go unrecognised. When processing a batch of sessions, the pnt-viz script could use fewer seed points than requested for sessions handled after one with relatively few valid matches. This has been corrected. An unusual case in which trimming a streamline left only its seed point, which can arise when building tract models for probabilistic neighbourhood tractography, is now handled correctly. The compare script produced an obscure error, rather than simply reporting no match, when comparing two images with IgnoreZeroes:true that share no nonzero voxels. This has been corrected. The transform script now produces a more informative error if asked to transform a point without a source space specified. The supplied Rprofile now checks whether the target of TRACTOR_HOME exists, and only prioritises packages installed in TractoR's library for calls to its scripts. The tractor binary is now more defensive when parsing its arguments, and avoids potentially dereferencing an uninitialised pointer.

Masson, Antoine · Borel, Alain

1 files · 1.6 MB · zipdeclared

A dynamic online tool for calculating the cost of research data storage services. It's highly modular and can be adapted for all kind of application. The system is really easy to install as it uses only opensource javascript libraries.

Tenoux, Thierry · charbonnier, romain · Corre, Yoann

5 files · 4.4 MB · csv, pdfdeclared

This dataset provides synthetic radio channel samples generated by ray-tracing simulation, for use in the study of the Future Railway Mobile Communication System (FRMCS). The data were produced by SIRADEL using their InoWave ray-tracing engine, in the framework of two collaborative research projects: 5G-RACOM and 5G-REMORA . 5G-RACOM (5G for Resilient and Green RAil COMmunications) investigates spectrum-efficient, resilient, and robust designs for FRMCS, covering radio propagation and channel modeling at 900 MHz (band n100) and 1900 MHz (band n101) across various railway operational scenarios, GSM-R/FRMCS coexistence, and hybrid FRMCS network solutions. More information: 5G-RACOM - Franco-German Ecosystem for Private 5G Networks . 5G-REMORA (5G-REalistic radio channel MOdels enabled system evaluation for RAilway environments) supports a "zero-on-site testing" approach for FRMCS deployment, combining realistic railway radio channel characterization from on-site sounder measurements, stochastic modeling, and enhanced ray-tracing, targeting the 900-1900 MHz railway frequency bands. More information: ANR Project-ANR-22-CE22-0015 . Dataset content The dataset includes two railway propagation scenarios, both simulated at a 1900 MHz central frequency between a macro-cell base station (BS) located near the track and a dual-polarized antenna mounted on the train rooftop: Montparnasse scenario : An outdoor-to-indoor transition scenario at the edge of the Montparnasse railway station in Paris, covering a 60 m trajectory (30 m outdoor / 30 m indoor) at 20 km/h. Paris scenario : An urban propagation scenario in the south of Paris, covering a 163 m trajectory at 100 km/h, including scattering effects from 22 metallic catenary pylons along the track. Each scenario is provided as a CSV file containing, for each simulated user-equipment (UE) position along the train trajectory, the set of ray-traced multipath components (delay, Doppler shift, azimuth/elevation angles of departure and arrival, and complex path amplitudes for all four V/H polarization combinations), along with UE position, timestamp, and line-of-sight/obstruction status. The channel data is provided as raw propagation output - no transmit power, antenna pattern, or signal bandwidth is applied. The complete CSV field definitions and coordinate/angle conventions are documented in the accompanying file SIRADEL railway channel data format - v1.3.pdf . The data is licensed under a Creative Commons "CC BY-NC-SA 4.0" license, which excludes any commercial use. The users are encouraged to publicly communicate the results of studies based on this channel data; and clearly state the source of the data. If you are using the data, we would pleased you keep us informed, and let us know about the target application.