This is an out-of-date version of our pRF mapping toolbox and no longer supported! While this version should be stable, USE IT AT YOUR OWN RISK! We instead recommend our new version SamSrf X, which will continue to be updated with bugfixes and new features. You can also use the new version for further analysis of maps from the old version.SamSrf X is available for download at:http://osf.io/2rgsm.--------------------------------------------------------------------------------Version: 5.84 (18-09-2017)Our Matlab toolbox for pRF mapping analysis. Uses SPM8 or SPM12 and FreeSurfer functionality for preprocessing. Also requires Statistics Toolbox, Optimization Toolbox, and Curve Fitting Toolbox (not strictly necessary) for Matlab.An extensive documentation "cookbook" is included. Please contact Sam (sampendu.wordpress.com) for any questions but please be advised that we are not able to provide tech support for people we don't collaborate with.As of version 5.63, we included a new tutorial explaining how to delineate visual areas using the DelineationTool in MatLab and giving advice on what to do with tricky retinotopic maps.

This dataset was obtained at the Queensland Brain Institute, Australia, using a 64 channel EEG Biosemi system. 21 healthy participants completed an auditory oddball paradigm (as described in Garrido et al., 2017).For a description of the oddball paradigm, please see Garrido et al., 2017:Garrido, M.I., Rowe, E.G., Halasz, V., & Mattingley, J. (2017). Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli. Cerebral Cortex, 1-12. DOI: 10.1093/cercor/bhx087If you use this dataset, please cite its doi, as well as citing the associated methods paper, which is as follows:Harris, C.D., Rowe, E.G., Randeniya, R. and Garrido, M.I. (2018). Bayesian Model Selection Maps for group studies using M/EEG data.For scripts to analyse the data, please see: https://github.com/ClareDiane/BMS4EEG

This data set is uploaded as supporting information for the publication entitled:A Comprehensive Tutorial on the SOM-RPM Toolbox for MATLABThe attached file 'case_study' includes the following:X : Data from a ToF-SIMS hyperspectral image. A stage raster containing 960 x800 pixels with 963 associated m/z peaks.pk_lbls: The m/z label for each of the 963 m/z peaks.mdl and mdl_masked: SOM-RPM models created using the SOM-RPM tutorial provided within the cited article.Additional details about the datasets can be found in the published article.V2 - contains modified peak lists to show intensity weighted m/z rather than peak midpoint. If you use this data set in your work, please cite our work as follows:[LINK TO BE ADDED TO PAPER ONCE DOI RECEIVED]

This data release provides remotely sensed data, field measurements, and MATLAB code associated with an effort to produce image-derived velocity maps for a reach of the Sacramento River in California's Central Valley. Data collection occurred from September 16-19, 2024, and involved cooperators from the Intelligent Robotics Group from the National Aeronautics and Space Administration (NASA) Ames Research Center and the National Oceanographic and Atmospheric Administration (NOAA) Southwest Fisheries Science Center. The remotely sensed data were obtained from an Uncrewed Aircraft System (UAS) and are stored in Robot Operating System (ROS) .bag files. Within these files, the various data types are organized into ROS topics including: images from a thermal camera, measurements of the distance from the UAS down to the water surface made with a laser range finder, and position and orientation data recorded by a Global Navigation Satellite System (GNSS) receiver and Inertial Measurement Unit (IMU) during the UAS flights. This instrument suite is part of an experimental payload called the River Observing System (RiOS) designed for measuring streamflow and further detail is provided in the metadata file associated with this data release. For the September 2024 test flights, the RiOS payload was deployed from a DJI Matrice M600 Pro hexacopter hovering approximately 270 m above the river. At this altitude, the thermal images have a pixel size of approximately 0.38 m but are not geo-referenced. Two types of ROS .bag files are provided in separate zip folders. The first, Baguettes.zip, contains "baguettes" that include 15-second subsets of data with a reduced sampling rate for the GNSS and IMU. The second, FullBags.zip, contains the full set of ROS topics recorded by RiOS but have been subset to include only the time ranges during which the UAS was hovering in place over one of 11 cross sections along the reach. The start times are included in the .bag file names as portable operating system interface (posix) time stamps. To view the data within ROS .bag files, the Foxglove Studio program linked below is freely available and provides a convenient interface. Note that to view the thermal images, the contrast will need to be adjusted to minimum and maximum values around 12,000 to 15,000, though some further refinement of these values might be necessary to enhance the display. To enable geo-referencing of the thermal images in a post-processing mode, another M600 hexacopter equipped with a standard visible camera was deployed along the river to acquire images from which an orthophoto was produced: 20240916_SacramentoRiver_Ortho_5cm.tif. This orthophoto has a spatial resolution of 0.05 m and is in the Universal Transverse Mercator (UTM) coordinate system, Zone 10. To assess the accuracy of the orthophoto, 21 circular aluminum ground control targets visible in both thermal and RGB (red, green, blue) images were placed in the field and their locations surveyed with a Real-Time Kinematic (RTK) GNSS receiver. The coordinates of these control points are provided in the file SacGCPs20240916.csv. Please see the metadata for additional information on the camera, the orthophoto production process, and the RTK GNSS survey. The thermal images were used as input to Particle Image Velocimetry (PIV) algorithms to infer surface flow velocities throughout the reach. To assess the accuracy of the resulting image-derived velocity estimates, field measurements of flow velocity were obtained using a SonTek M9 acoustic Doppler current profiler (ADCP). These data were acquired along a series of 11 cross sections oriented perpendicular to the primary downstream flow direction and spaced approximately 150 m apart. At each cross section, the boat from which the ADCP was deployed made four passes across the channel and the resulting data was then aggregated into mean cross sections using the Velocity Mapping Toolbox (VMT) referenced below (Parsons et al., 2013). The VMT output was further processed as described in the metadata and ultimately led to a single comma delimited text file, SacAdcp20240918.csv, with cross section numbers, spatial coordinates (UTM Zone 10N), cross-stream distances, velocity vector components, and water depths. To assess the sensitivity of thermal image velocimetry to environmental conditions, air and water temperatures were recorded using a pair of Onset HOBO U20 pressure transducer data loggers set to record pressure and temperature. Deploying one data logger in the air and one in the water also provided information on variations in water level during the test flights. The resulting temperature and water level time series are provided in the file HoboDataSummary.csv with a one-minute sampling interval. These data sets were used to develop and test a new framework for mapping flow velocities in river channels in approximately real time using images from an UAS as they are acquired. Prototype code for implementing this approach was developed in MATLAB and is also included in the data release as a zip folder called VelocityMappingCode.zip. Further information on the individual functions (*.m files) included within this folder is available in the metadata file associated with this data release. The code is provided as is and is intended for research purposes only. Users are advised to thoroughly read the metadata file associated with this data release to understand the appropriate use and limitations of the data and code provided herein.

How to set the input parameters: an example.

In the second column the instantaneous effects are neglected both for targets and conditioning. In the third column we set instantaneous effects for some drivers and the respective targets. For example, when the target is 1, instantaneous effects are taken into account for driver 2 (first two rows, right column, parameter idDrivers) and conditioning variable 3 (first row, right column, parameter idOtherLagZero).Example of the parameters required to define the methods for an experiment on 5 variables.

This package contains the datasets and supplementary materials used in the IPIN 2021 Competition.

Contents:

IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition

01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors.

02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.

03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results.

Please, cite the following works when using the datasets included in this package:

Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the American River near Fair Oaks, CA, acquired in October 2020. Field measurements of water depth available through another data release (Legleiter, C.J., and Harrison, L.R., 2022, Field measurements of water depth from the American River near Fair Oaks, CA, October 19-21, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P92PNWE5) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: American_mean-spec.tif, American_mean-depth.tif, American_NN-depth.tif, and American-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m and the figure included on this landing page provides a flow chart illustrating the four different neural network-based depth retrieval methods. As examples of the resulting models, MATLAB *.mat data files containing the best-performing neural network model for each site are provided below, along with a file that lists the PlanetScope image identifiers for the images that were used for each site. To develop and test this new NNDR approach, the method was applied to satellite images from three rivers across the U.S.: the American, Colorado, and Potomac. For each site, field measurements of water depth available through other data releases were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: X_mean-spec.tif, X_mean-depth.tif, X_NN-depth.tif, and X-single-image.tif, where X denotes the site name. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item, along with a flow chart illustrating the four different neural network-based depth retrieval methods. To develop and test this new NNDR approach, the method was applied to satellite images from the Colorado River near Lees Ferry, AZ, acquired in March and April of 2021. Field measurements of water depth available through another data release (Legleiter, C.J., Debenedetto, G.P., and Forbes, B.T., 2022, Field measurements of water depth from the Colorado River near Lees Ferry, AZ, March 16-18, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9HZL7BZ) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: Colorado_mean-spec.tif, Colorado_mean-depth.tif, Colorado_NN-depth.tif, and Colorado-single-image.tif. In addition, to assess the robustness of the Mean-spec and NN-depth methods to the introduction of a large pulse of sediment by a flood event that occurred partway through the image time series, depth maps from before and after the flood are provided in the files Colorado_Mean-spec_after_flood.tif, Colorado_Mean-spec_before_flood.tif, Colorado_NN-depth_after_flood.tif, and Colorado_NN-depth_before_flood.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over large spatial extents and with the kind of frequency and regularity required to support monitoring programs. Remote sensing methods could provide a viable alternative means of mapping river bathymetry (i.e., water depth). The purpose of this study was to develop and test new, spectrally based techniques for estimating water depth from satellite image data. More specifically, a neural network-based temporal ensembling approach was evaluated in comparison to several other neural network depth retrieval (NNDR) algorithms. These methods are described in a manuscript titled "Neural Network-Based Temporal Ensembling of Water Depth Estimates Derived from SuperDove Images" and the purpose of this data release is to make available the depth maps produced using these techniques. The images used as input were acquired by the SuperDove cubesats comprising the PlanetScope constellation, but the original images cannot be redistributed due to licensing restrictions; the end products derived from these images are provided instead. The large number of cubesats in the PlanetScope constellation allows for frequent temporal coverage and the neural network-based approach takes advantage of this high density time series of information by estimating depth via one of four NNDR methods described in the manuscript: 1. Mean-spec: the images are averaged over time and the resulting mean image is used as input to the NNDR. 2. Mean-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is averaged to obtain the final depth map. 3. NN-depth: a separate NNDR is applied independently to each image in the time series and the resulting time series of depth estimates is then used as input to a second, ensembling neural network that essentially weights the depth estimates from the individual images so as to optimize the agreement between the image-derived depth estimates and field measurements of water depth used for training; the output from the ensembling neural network serves as the final depth map. 4. Optimal single image: a separate NNDR is applied independently to each image in the time series and only the image that yields the strongest agreement between the image-derived depth estimates and the field measurements of water depth used for training is used as the final depth map. MATLAB (Version 24.1, including the Deep Learning Toolbox) source code for performing this analysis is provided in the function NN_depth_ensembling.m available on the main landing page for the data release of which this is a child item. To develop and test this new NNDR approach, the method was applied to satellite images from the Potomac River near Brunswick, MD, acquired in July and August of 2021. Field measurements of water depth available through another data release (Duda, J.M., Greise, A.J., and Young, J.A., 2020, Potomac River ADCP Bathymetric Survey, October 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9GOZZYX) were used for training and validation. The depth maps produced via each of the four methods described above are provided as GeoTIFF files, with file name suffixes that indicate the method employed: Potomac_mean-spec.tif, Potomac_mean-depth.tif, Potomac_NN-depth.tif, and Potomac-single-image.tif. The spatial resolution of the depth maps is 3 meters and the pixel values within each map are water depth estimates in units of meters.

This package contains the datasets and supplementary materials used in the IPIN 2023 Competition. Contents

Track-3_TA-2023.pdf: Technical annexe describing the competition (Version 2) 01 Logfiles: This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors. 02 Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization. 03 Evaluation: This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results. We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competition Citation Policy Please cite the following works when using the datasets included in this package: Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2023Competition Track 3 (Smartphone-based, off-site), Zenodo 2023http://dx.doi.org/10.5281/zenodo.8362205 Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.8362205 Contact For any further questions about the database and this competition track, please contact: Joaquín Torres-Sospedra Centro ALGORITMI,Universidade do Minho, Portugalinfo@jtorr.es - jtorres@algoritmi.uminho.pt Antonio R. Jiménez Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain antonio.jimenez@csic.es Antoni Pérez-NavarroFaculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spainaperezn@uoc.edu Acknowledgements We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points. We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days. We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition. This work and competition belong to the IPIN 2023 Conference in Nuremberg (Germany). Parts of this work received the financial support received from projects and grants:

ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072) GeoLibero (from CYTED) INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE) MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE) TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER) SmartLoc(CSIC-PIE Ref.201450E011) LORIS (TIN2012-38080-C04-04)

This package contains the datasets and supplementary materials used in the IPIN 2024 Competition.

Contents

Track-3_TA-2024.pdf: Technical annex describing the competition (Version 1)

01 Logfiles: This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors.

02 Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization.

03 Evaluation: This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results.

We provide additional information on the competition at: https://competition.ipin-conference.org/2024/call-for-competition

Citation Policy

Please cite the following works when using the datasets included in this package:

Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2024Competition Track 3 (Smartphone-based, off-site), Zenodo 2024http://dx.doi.org/10.5281/zenodo.13931119

Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.13931119

Contact

For any further questions about the database and this competition track, please contact:

Joaquín Torres-Sospedra Departament d'Informatica, Universitat de València, 46100 Burjassot, SpainValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, 46022 Valencia, SpainJoaquin.Torres@uv.es - info@jtorr.es Antonio R. Jiménez Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain antonio.jimenez@csic.es

Antoni Pérez-NavarroFaculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spainaperezn@uoc.edu

Acknowledgements

We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points.

We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.

We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition.

This work and competition are part of the IPIN 2023 Conference in Nuremberg (Germany) and the IPIN 2024 Conference in Hong Kong.

Parts of this work received the financial support received from projects and grants:

POSITIONATE (CIDEXG/2023/17, Conselleria d’Educació, Universitats i Ocupació, Generalitat Valenciana)

ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072)

GeoLibero (from CYTED)

INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE)

MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)

TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)

SmartLoc(CSIC-PIE Ref.201450E011)

LORIS (TIN2012-38080-C04-04)

Abstract: Sonnengestützte Fotosynthesen stellen einen Weg dar die Herausforderungen zu meistern, die aus der Notwendigkeit fossile Energieträger in der Weltwirtschaft zu ersetzen resultieren. Die Fortschritte im Bereich solarer Fotosynthesen hängen dabei stark von neuen Prozessdesigns ab. Deren Entwicklung setzt verlässliche Methoden zur Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken und die Fähigkeit Strahlungstransport in Fotoreaktoren akkurat abbilden zu können voraus. Das Gebiet der Fotoreaktionstechnik ist jedoch ein wenig entwickeltes Feld, in dem es an verlässlichen und offen zugänglichen Methoden für Strahlungstransportsimulationen und standardisierten Fotoreaktoren für die Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken fehlt. Mit dem vorliegenden Datensatz wird sowohl ein Satz von CAD Dateien zur Herstellung (via 3D Druck) eines Fotoreaktors für die akkurate Bestimmung von Quantenausbeuten und Fotoreaktionskinetiken als auch eine umfassende, MATLAB®-basierte Toolbox für Strahlungstransportsimulationen bereitgestellt. Der vorgeschlagene Fotoreaktor weist eine isophotonische Reaktionszone auf, was bedeutet, dass die Reaktionszone nur kleine Gradienten in der lokalen volumetrischen Photonenabsorptionsrate aufweist. Die Toolbox erlaubt die Berechnung von Strahlungstransporteffizienzen im isophotonischen Fotoreaktor und stellt damit die Basis für eine sinnvolle Datenauswertung in Experimenten mit dem vorgeschlagenen Fotoreaktor dar. Über diesen konkreten Anwendungsfall der Toolbox hinaus, kann die Toolbox auch für andere Anwendungen im Bereich Strahlungstransportsimulationen eingesetzt werden. Diese reichen von der Auswertung von Versuchen zur Bestimmung von optischen Transporteigenschaften über die Auslegung von Lichtquellen hin zur Optimierung von Fotoreaktoren. Der bereitgestellte Datensatz kann damit nicht nur die Arbeit von Materialwissenschaftlerinnen im Bereich der Entwicklung von Fotokatalysatoren mit hohen Quantenausbeuten unterstützen, sondern kann auch im Rahmen der Arbeit von Chemieingenieurinnen eingesetzt werden, die die Entwicklung von effizienten Fotoreaktoren und Lichtquellen für spezifische Fotokatalysatoren oder Anwendungsfälle vorantreiben. Abstract: Solar driven photocatalysis represents one way to address challenges arising from the need to substitute fossil energy carriers in the world’s economy. The developments in the field of photocatalysis heavily depend on new process designs whose development require methods for the determination of quantum yields and photoreaction kinetics as well as the ability to map radiation transport in complex photoreactors. However, the field of photoreaction engineering is an underdeveloped field lacking reliable and open access tools for radiation transport simulations and standardized photoreactors for quantum yield and photoreaction kinetic measurements. With this data set both a set of CAD files for the facile fabrication of an isophotonic photoreactor for the determination of quantum yields in gas, liquid, and multi-phase photoreactions via additive manufacturing as well as a comprehensive MATLAB®-based toolbox for radiation transport simulations in photoreactors are given. The proposed photoreactor is designed in a way that its reaction volume is isophotonic, which means that the reaction volume shows low gradients in the local volumetric rate of photon absorption. The toolbox allows the determination of radiation transport efficiencies within the isophotonic photoreactor and therewith provides the basis for meaningful data evaluation of experiments conducted with the isophotonic photoreactor. Beyond this concrete use case of the provided toolbox, the toolbox can also be employed for radiation transport simulations in many other use cases. Those range from the evaluation of experiments aiming for the determination of optical properties over light source design to the optimization of photoreactors. The data set therewith not only can support the further development of high quantum yield materials by material scientists in the field of photocatalysis but also can be used by chemical engineers working on new, high efficiency photo reactors or sophisticated light sources especially designed for specific photocatalysts and/or use cases. TechnicalRemarks: The data set comprises (a) all CAD files that are needed to print an isophotonic photoreactor for the precise determination of quantum yields in gas, liquid, and multi-phase photoreactions and (b) a MATLAB® toolbox named phoRex that allows the determination of spectral radiation transport efficiencies (= transport efficiencies from the light source of the isophotonic photoreactor into the reaction volume) as well as other radiation transport related performance metrices via a Monte Carlo ray tracing approach. For details on the reactor design and the simulation environment please refer to the corresponding publication (DOI: 10.1016/j.cej.2022.139204). The toolbox requires a working MATLAB® installation (2018 or later) including the MATLAB parallel computing toolbox. Installation of the toolbox is in accordance with the standard MATLAB® procedure for the installation of new toolboxes. After installation, phoRex provides an environment for Monte Carlo ray tracing simulations mapping radiation transport in 3D in channel-like geometries, for instance photoreactors. For the simulation of the isophotonic photoreactor a comprehensive live script example is given with the file example.mlx comprised in the toolbox. The example guides through the usage of the provided code in the context of quantum yield determination using the proposed isophotonic photoreactor. Further, the comprised pre-processing script preProcessQY.m lines out how simulations are set up in the provided Monte Carlo ray tracing environment. This code example can be employed to understand how to set up own simulation cases for other use cases than the simulation of the isophotonic photoreactor proposed for the accurate determination of quantum yields. For detailed information on the code structure of the Monte Carlo ray tracing approach itself, the author refers to the extensively commented source code given with the class definitions of the toolbox.

This dataset contains magnetic field maps of brain tissue collected with a Quantum Diamond Microscope (QDM). Relevant details and the results based on this data are presented and discussed in a journal paper titled "Magnetic Dipole Imaging of Brain Tissue". There are three sets of QDM data in this repository, one for each set of samples: human brain samples, rat brain samples, and a magnetotactic bacteria sample. Each set contains folders for each sample and field of view (FOV). Rat brain data is further subdivided into polishes (see paper for details). For human and rat brain data, each folder contains two subfolders for the repeated map pairs (image 1 and 2). In each final subfolder, the common files are: B111dataToPlot.mat: Nearly raw QDM data containing remanence and induced magnetic field maps in the 111 diamond crystallographic direction. Resolution is 1.175 µm (no binning). Bz_uc0.mat: Bz data (vertical magnetic field component) computed from B111 data. Bz_uc0_sat.png: PNG image of Bz map with saturated color range (typically 2e-7 T). Provided for convenience. Can be replotted from Bz_uc0.mat. laser.jpg: optical light image of the same FOV as the QDM magnetic field map, illucidated by the QDM laser. ledImg.png: optical light image of the same FOV as the QDM magnetic field map, illucidated by the LED. QDM data was processed using QDMlab: https://github.com/HarvardPaleomag/QDMlab [Volk, M. W., Fu, R. R., Trubko, R., Kehayias, P., Glenn, D. R., & Lima, E. A. (2022). QDMlab: A MATLAB toolbox for analyzing quantum diamond microscope (QDM) magnetic field maps. Computers & Geosciences, 167, 105198.].

Current version: 5.63 (15-12-2015)Our Matlab toolbox for pRF mapping analysis. Uses SPM8 (12 should also be okay) and FreeSurfer functionality for preprocessing. Also requires Statistics Toolbox, Optimization Toolbox, and Curve Fitting Toolbox (not strictly necessary) for Matlab.An extensive documentation "cookbook" is included. Please contact Sam (sampendu.wordpress.com) for any questions but please be advised that we are not able to provide tech support for people we don't collaborate with.As of version 5.63, we included a new tutorial explaining how to delineate visual areas using the DelineationTool in MatLab and giving advice on what to do with tricky retinotopic maps.