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1,169 results for “Infrared”
Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy - Dataset and Codes
<p>Here we publish the source data and the codes for simulation and data processing regarding the article "Nanophotonic supercontinuum-based mid-infrared dual-comb spectroscopy" that is published on Optica (<a href="https://doi.org/10.1364/OPTICA.396542">https://doi.org/10.1364/OPTICA.396542</a>).</p>
Remote near infrared identification of pathogens with multiplexed nanosensors - source data file for Nißler et al. 2020 (Nat. Commun.)
<p>source data file for Nißler et al. 2020 (Nat. Commun.)</p> <p>entitled: </p> <p>Remote near infrared identification of pathogens with multiplexed nanosensors</p>
An incandescent metasurface for quasimonochromatic polarized Mid-Wave Infrared emission modulated beyond 10 MHz
<p>README :</p> <p>The codes available in this repository are MATLAB scripts.</p> <p>In order to run some of the scripts, users need to download the RETICOLO library available at doi : 10.5281/zenodo.3610175 Light-in-complex-nanostrucutres/RETICOLO and subsequently add this library to their MATLAB search path.</p> <p>4 folders can be found:</p> <ul> <li>EM_simulations: contains scripts to reproduce the electromagnetic simulations presented in the paper. The codes use functions in the RETICOLO library (therefore, please make sure that the folder containing all RETICOLO functions are known to the MATLAB search path).</li> <li>PolarizedFreqResponse: contains raw datasets and ready-to-use analysis scripts to reproduce frequency response curves of the device presented in the paper. The main script to be run is "main.m".</li> <li>EmissivitySpectra: contains raw datasets and ready-to-use analysis scripts to reproduce the emissivity spectrum of the device presented in the paper.</li> <li>Response_to_pulse_simulation: contains raw datasets and ready-to-use analysis scripts to reproduce the pulse response of the device presented in the paper.</li> </ul> <p>===============================</p> <p>DESCRIPTION :</p> <p>This repository contains all datasets and codes used in the following paper :</p> <p>doi :</p> <p>===============================</p> <p>PAPER ABSTRACT:</p> <p>Incandescent sources such as hot membranes and globars are widely used for mid infrared spectroscopic applications. The emission properties of these sources can be tailored by means of resonant metasurfaces: control of the spectrum, polarization and directivity has been reported. For detection or communication applications, fast temperature modulation is desirable but is still a challenge due to thermal inertia. Reducing thermal inertia can be achieved using nanoscale structures at the expense of a low absorption and emission cross section. Here, we introduce a metasurface that combines nanoscale heaters to ensure fast thermal response and nanophotonic resonances to provide large monochromatic and polarized emissivity. The metasurface is based on platinum and silicon nitride and can sustain high temperatures. We report a peak emissivity of 0.8 and an operation up to 20 MHz, six orders of magnitude faster than commercially available hot membranes.</p>
An incandescent metasurface for quasimonochromatic polarized Mid-Wave Infrared emission modulated beyond 10 MHz
<p>README</p> <p>===============================</p> <p>DESCRIPTION :</p> <p>This repository contains all datasets and MATLAB codes used in the following paper :</p> <p>Wojszvzyk, Léo, Anne Nguyen, Anne-Lise Coutrot, Cheng Zhang, Benjamin Vest, et Jean-Jacques Greffet. « An Incandescent Metasurface for Quasimonochromatic Polarized Mid-Wave Infrared Emission Modulated beyond 10 MHz ». <em>Nature Communications</em> 12, n<sup>o</sup> 1 (5 mars 2021): 1492. <a href="https://doi.org/10.1038/s41467-021-21752-w">https://doi.org/10.1038/s41467-021-21752-w</a>.</p> <p>In order to run some of the scripts, users need to download the RETICOLO library available at doi : 10.5281/zenodo.3610175 Light-in-complex-nanostrucutres/RETICOLO and subsequently add this library to their MATLAB search path.</p> <p>4 folders can be found:</p> <ul> <li>EM_simulations: contains scripts to reproduce the electromagnetic simulations presented in the paper. The codes use functions in the RETICOLO library (therefore, please make sure that the folder containing all RETICOLO functions (RETICOLO_V8/reticolo_allege) are known to the MATLAB search path).</li> <li>PolarizedFreqResponse: contains raw datasets and ready-to-use analysis scripts to reproduce frequency response curves of the device presented in the paper. The main script to be run is "main.m".</li> <li>EmissivitySpectra: contains raw datasets and ready-to-use analysis scripts to reproduce the emissivity spectrum of the device presented in the paper.</li> <li>Response_to_pulse_simulation: contains raw datasets and ready-to-use analysis scripts to reproduce the pulse response of the device presented in the paper.</li> </ul> <p>===============================</p> <p>PAPER ABSTRACT:</p> <p>Incandescent sources such as hot membranes and globars are widely used for mid infrared spectroscopic applications. The emission properties of these sources can be tailored by means of resonant metasurfaces: control of the spectrum, polarization and directivity has been reported. For detection or communication applications, fast temperature modulation is desirable but is still a challenge due to thermal inertia. Reducing thermal inertia can be achieved using nanoscale structures at the expense of a low absorption and emission cross section. Here, we introduce a metasurface that combines nanoscale heaters to ensure fast thermal response and nanophotonic resonances to provide large monochromatic and polarized emissivity. The metasurface is based on platinum and silicon nitride and can sustain high temperatures. We report a peak emissivity of 0.8 and an operation up to 20 MHz, six orders of magnitude faster than commercially available hot membranes.</p>
Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR
<p>Lightcurves and spectra presented in the paper "Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR", Karambelkar et al. 2021.</p>
Predicting Photosynthetic Capacity in Tobacco (Nicotiana tabacum) Using Shortwave Infrared Spectral Reflectance
<p>These files contains the data used for analysis in the (currently under-review) publication "<strong>Predicting Photosynthetic Capacity in Tobacco (</strong><strong><em>Nicotiana tabacum</em></strong><strong>) Using Shortwave Infrared Spectral Reflectance" </strong> in the Journal of Experimental Botany. </p>
Spectral data presented in Hinrichs J L, Lucey P G. Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil.
<p>In this dataset, we present the spectral data in paper: Hinrichs, J. L., & Lucey, P. G. (2002). Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil. <em>Icarus</em>, <em>155</em>(1), 169-180.</p>
Data from: Bio-inspired imager improves sensitivity in near-infrared fluorescence image-guided surgery
Image-guided surgery can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky and costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities for distinguishing cancerous from healthy tissue. Here we introduce an innovative design, comprising an artificial multispectral sensor inspired by the Morpho butterfly's compound eye, which can significantly improve image-guided surgery. By monolithically integrating spectral tapetal filters with photodetectors, we have realized a single-chip multispectral imager with 1000× higher sensitivity and 7× better spatial co-registration accuracy compared to clinical imaging systems in current use. Preclinical and clinical data demonstrate that this technology seamlessly integrates into the surgical workflow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes. Due to its low manufacturing cost, our bio-inspired sensor will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care.
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset (Version 2).
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This is an update of the previous zenodo.231216 upload. This update includes revised PDF atlases for Be59 and IC348 (with revised vales of the SED slope and [3.6] and [4.5]-band magnitudes).</p>
Data for "Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra"
<p>This record contains neuroevolution potential (NEP) and tensor neuroevolution potential (TNEP) models (nep*.txt) for molecular water species, liquid water as well as barium zirconate, along with training data<i> (*</i>.zip). The models were constructed using GPUMD 3.9 (https://gpumd.org/).</p>
Data from: An infrared, Raman, and X-ray database of battery interphase components
<p>Further technological advancement of both lithium-ion and emerging battery technologies can be catalyzed by an improved understanding of the chemistry and working mechanisms of the solid electrolyte interphases (SEIs) that form at electrochemically active battery interfaces. However, collecting and interpreting spectroscopy results of SEIs is difficult for several reasons, including the chemically diverse composition of SEIs. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten suggested SEI chemical constituents relevant to both lithium-ion and emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres afforded by custom designed sample holders. The data library presented in this work (and online repository) alleviates challenges with locating related work that is either diffusely spread throughout the literature, or is non-existent, and provides energy storage researchers streamlined access to vital SEI-relevant data that can catalyse future battery research efforts.</p>
Data from: Charcoal analysis for temperature reconstruction with infrared spectroscopy
<p>The duration and maximum combustion temperature of vegetation fires are important fire properties with implications for ecology, hydrology, hazard potential, and many other processes. Directly measuring maximum combustion temperature during vegetation fires is difficult. However, chemical properties of charcoal formed as a by-product of fire reflect key chemical transformations associated with temperatures. Therefore, they could be used indirectly to determine the maximum combustion temperature of vegetation fires. To evaluate the reliability of charcoal chemistry as an indicator of maximum combustion temperature, we studied the chemical properties of charcoal formed through two laboratory methods at measured temperatures. Using a muffle furnace, we generated charcoal from the woody material of ten different tree and shrub species at seven distinct peak temperatures (from 200 °C to 800 °C in 100 °C increments). Additionally, we simulated more natural combustion conditions by burning woody material and leaves of four tree species in a combustion facility instrumented with thermocouples, including thermocouples inside and outside of tree branches. Charcoal samples generated in these controlled settings were analyzed using Fourier Transform Infrared (FTIR) spectroscopy to characterize their chemical properties. The Modern Analogue Technique (MAT) was employed on FTIR spectra of muffle furnace charcoal to assess the accuracy of inferring maximum pyrolysis temperature. The MAT modeltemperature matching accuracy improved from 46% for all analogues to 81% when including ±100 ℃. Furthermore, we used MAT to compare charcoal created in the combustion facility with muffle furnace charcoal. Our findings indicate that the spectra of charcoals generated in a combustion facility can be accurately matched with muffle furnace-created charcoals of similar temperatures using MAT, and the accuracy improved when comparing the maximum pyrolysis temperature from muffle furnace charcoal with the maximum inner temperature of the combustion facility charcoal. This suggests that charcoal produced in a muffle furnace may be representative of the inner maximum temperatures for vegetation fire-produced charcoals. FTIR spectroscopy is a promising tool for determining maximum fire temperature from charcoals of vegetation and prescribed fires and may have implications for fossil charcoal from palaeoecological records. </p>
Surface plasmons-phonons for mid-infrared hyperspectral imaging
<p>Dataset for the hyperspectral imaging of spike proteins of the severe acute respiratory syndrome coronavirus (SARS-CoV) using the synergistic plasmon-phonon hyperspectral bioimaging system.</p>
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Data for: "A partial near infrared guide star catalog for Thirty Meter Telescope Operations"
<p>This .zip file contains the infrared guide star catalogs (IRGSC) generated using optical data (in g, r, i, z, and y bands) from the PANSTARRS DR2 for twenty test fields selected across the TMT's observable sky. The IRGSC is generated by computing the near infrared (J, H and K band) magnitudes of the stellar sources using the Kurucz and Phoenix stellar atmospheric models. Each source in the IRGSC is likely to be stellar and contains the respective PANSTARRS and GAIA object identifiers , their astrometric information from Gaia DR3, the optical and computed NIR magnitudes, the model parameters, the scale factor and the metric d_dev that determines the goodness-of-fit of the model to the data. In addition, the test fields also contain the infrared guide star catalog validated using the UKIDSS DR11 data and plots that show the comparison between the computed NIR magnitudes and observed UKIDSS NIR magnitudes.</p> <p>Inside this .zip file, there is also another directory that contains the IRGSC generated for additional PANSTARRS Medium Deep Survey fields with readily available PANSTARRS 3pi survey data. Some plots are included that show the efficiency of star-galaxy classification and completeness of the 3-pi survey data for these fields.</p> <p>More details and the code to generate IRGSC can be found <strong><a href="https://github.com/sshah1502/irgsc/tree/main">here</a></strong></p>
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>
Machine-learning Generated Catalog of Long Period Variables from Palomar Gattini-Infrared Lightcurves
<pre>A catalog of LPVs obtained from a decision tree classifier trained on features extracted from Palomar Gattini-IR J band <br>lightcurves as described in Suresh et. al. 2024 (https://arxiv.org/abs/2402.08000).<br><br>Description of columns<br><br>1. 'Name' - Unique identifier for each LPV; internal designation of the object<br>2. 'RA' - Right ascension<br>3. 'Dec' - Declination<br>4. 'num_detections' - number of detections<br><br>Calculated features:<br>5. 'von_neumann_score' - von-Neumann score <br>6. 'gp_score' - Gaussian process regression fit score<br>7. 'slope_min' - minimum slope of the lightcurve<br>8. 'slope_max' - maximum slope of the lightcurve<br>9. 'ptp' - peak-to-peak amplitude <br>10. 'nflips' - number of flips<br>11. 'max_rate' - maximum point-wise slope <br>12. 'min_rate' - minimum point-wise slope 13. 'lcdur' - observation baseline<br>14. 'per_diff_90_50' - difference between 90th and 50th percentiles of J band magnitude<br>15. 'per_diff_95_50' - difference between 95th and 50th percentiles of J band magnitude <br>16. 'J' - Stetson J index<br>17. 'K_s' - Stetson K index, calculated as single band data<br>18. 'K_m' - Stetson K index, calculated as multi-band data <br>19. 'L_s' - Stetson L index, calculated as single band data 20. 'L_m' - Stetson L index, calculated as multi-band data<br>21. 'phase_chisq' - chi sq of sinusiod fit to phase folded lightcurve<br>22. 'phase_redchi' - reduced chi sq of sinusiod fit to phase folded lightcurve <br>23. 'phase_aic' - akaike information criterion of sinusiod fit to phase folded lightcurve <br>24. 'phase_bic' - bayesian information criterion of sinusiod fit to phase folded lightcurve 25. 'bestperiod' - best fit Lomb-Scargle period <br>26. 'bestperiod_1' - second most likely Lomb-Scargle period<br>27. 'bestperiod_2' - third most likely Lomb-Scargle period<br>28. 'LSscore' - Lomb-Scargle score of best period <br>29. 'LSscore_2' - Lomb-Scargle score corresponding to second best period 30. 'LSscore_3' - Lomb-Scargle score corresponding to third best period <br>31. 'amplitude' - J band amplitude <br>32. 'meanF' - mean predicted value from Lomb-Scargle fit <br>33. 'chi2' - chi sq of sinusoidal fit to lightcurve<br>34. 'redchi2' - reduced chi square of sinusoidal fit to lightcurve<br>35. 'rednullchi2' - reduced chi square of linear fit to lightcurve 36. 'nullchi2' - chi square of linear fit to lightcurve<br>37. 'maxperiod' - maximum period from Lomb-Scargle fit <br>38. 'linear_slope' - slope of linear fit to lightcurve<br>39. 'linear_intercept' - y-intercept of linear fit to lightcurve<br>40. 'J_mag' - mean J band magnitude<br>41. 'LSratio' - ratio of Lomb-Scargle score of two most promiment peaks in <br> Lomb-Scargle periodogram<br>42. 'chi2ratio' - ratio of reduced chi square of sinusoidal fit and reduced <br> chi square of linear fit to lightcurve <br>43. 'period_ratio' - ratio of best fit Lomb-Scargle period and <br> maximum Lomb-Scargle period<br>44. 'prob_lpv_sum' - machine-learning classifier LPV score (obtained by summing probability of LPV and type II LPV)<br><br>Color information: 45. 'w1mpro' - WISE W1 mag<br>46. 'w1sigmpro' - WISE W1 error <br>47. 'w2mpro' - WISE W2 mag<br>48. 'w2sigmpro' - WISE W2 error <br>49. 'w3mpro' - WISE W3 mag<br>50. 'w3sigmpro' - WISE W3 error 51. 'w4mpro' - WISE W4 mag<br>52. 'w4sigmpro' - WISE W4 error<br>53. 'j_m' - 2MASS J mag <br>54. 'j_msigcom' - 2MASS J error<br>55. 'h_m' - 2MASS H mag <br>56. 'h_msigcom' - 2MASS H error<br>57. 'k_m' - 2MASS K mag 58. 'k_msigcom' - 2MASS K error<br>59. 'J-H' - 2MASS J-H<br>60. 'J-K' - 2MASS J-K 61. 'W1-W2' - WISE W1-W2 <br>62. 'W3-W4' - WISE W3-W4 <br>63. 'W1-W4' - WISE W1-W4</pre>
Fourier transformed infrared reflectance (FTIR) spectra of peat soils collected from the top and bottom of peatland erosion gullies
<p>Peat soil was randomly collected from gullies within two eroding blanket bogs. Balmoral (BAM) is on a large high-altitude plateau blanket bog in the eastern part of the Cairngorms National Park, Scotland, UK (56.93° N, − 3.16° E, 642 m asl) and Glensaugh (GSA) is an upland livestock farm with sections of and blanket bog peatland in the Grampian foothills (56.55° N, 2.33° E, 412 m asl). Both sites have undergone extensive degradation and peat erosion, and both have, in some parts, recently undergone restoration practices, including bunding and reprofiling.</p> <p>Peat samples were collected at Glensaugh and Balmoral as follows. At Glensaugh, peat at the top 1 cm of exposed gully sides (approximately 10-20cm from the vegetated surface) and at the gully bottom were taken, air dried and passed on for FTIR analysis. These gullies correspond to four erosion pin measurement areas and their corresponding peat sediment trap areas at Glensaugh. At Balmoral, the same approach was taken except six ‘gully top’ and ‘gully bottom’ sites were randomly selected and not geographically paired in the same way at Glensaugh.</p> <p>Samples were air dried and finely ball milled, prior to FTIR analysis. FTIR spectra were recorded using a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) and OPUS 7.2 software. To record the FTIR spectra, each of the samples were placed, in turn, on a Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system. Data points in the range of 4000-400 cm-1 were recorded with a resolution of 4 cm-1 and average of 64 scans. A spectrum of the empty sampling accessory, with the same resolution and number of scans, was recorded as the background spectrum before each measurement. </p> <p>Since the penetration depth for the DATR accessory is different for each wavelength and is directly proportional to the wavelength of the incident light (The higher the wavenumber the lower the penetration), an ATR correction was applied to the spectra to correct this effect, using the OPUS software. No correction was required for water vapour and CO2 as the spectrometer is continuously purged with dry air.</p> <p> </p> <p>In the dataset, columns correspond to the following:</p> <p>Site: Balmoral or Glensaugh</p> <p>Gully Position: Top or bottom</p> <p>Gully Number: Replicate gullies within the site</p> <p>Sample date: Date</p> <p>Sample ID: Unique identifier</p> <p>Remaining columns: Reflectance at a given wavelength</p>
Data from: Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground-dwelling invertebrates
<p>Ground-dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground-dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground-dwelling invertebrates has not been strongly considered during survey design. In this study, data from 6 ICTs with two shooting areas (A1, 38.48 cm<sup>2</sup>; A2, 400 cm<sup>2</sup>) were used to investigate ground-dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20:00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1,420 h, invertebrates within 9 taxa, 2,447 individuals, and 112,909 ind./m<sup>2</sup> were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground-dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground-dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground-dwelling invertebrates. This study further provides an automatic tool and high-quality data for biodiversity monitoring and protection in farmlands.</p>
"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment
<p>This repository contains all needed data sets for the contribution in Journal of Sensors and Sensor Systems "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div> title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div> volume = {14},</div> <div> issn = {2194-8771},</div> <div> shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div> url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div> doi = {10.5194/jsss-14-37-2025},</div> <div> abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div> language = {English},</div> <div> number = {1},</div> <div> journal = {Journal of Sensors and Sensor Systems},</div> <div> author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div> month = feb,</div> <div> year = {2025},</div> <div> note = {Publisher: Copernicus GmbH},</div> <div> pages = {37--46},</div> <div>}</div> </div>
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.