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Figure 3 in A revision of Ophidiaster davidsoni de Loriol and Pellat 1874 from the Tithonian of Boulogne (France) and its transfer from the Valvatacea to the new forcipulatacean genus Psammaster gen. nov.
Figure 3. Arrangement of the body wall and arm ossicles of Psammaster davidsoni (a, b, c, d) and the ambulacral groove (e, f). Photographs (a, c, e) and interpretation drawings (b, d, f) of the lectotype NHMUK PI E 53996. Dashed lines indicate uncertain contour of the ossicles. Coloured areas indicate ossicle homology. In orange: the central ossicle; in red: the madreporite; in green: carinals; in pink: abactinals; in blue: superomarginals; in yellow: inferomarginals; in brown: actinals; in grey: ambulacrals; in purple: adambulacrals; in teal: adambulacrals spines. Scale bars: 5 mm.
Figure 2 in A revision of Ophidiaster davidsoni de Loriol and Pellat 1874 from the Tithonian of Boulogne (France) and its transfer from the Valvatacea to the new forcipulatacean genus Psammaster gen. nov.
Figure 2. Disc of the lectotype NHMUK PI E 53996, (a) photography and (b) interpretation drawing. Dashed lines indicate uncertain contour of the ossicles. Coloured areas indicate ossicle homology. Orange: the central; red: the madreporite; light green: carinals; dark green: primary radials; light blue: superomarginals; dark blue: primary interradials; pink: abactinals. Scale bars: 5 mm.
Figure 1 in A revision of Ophidiaster davidsoni de Loriol and Pellat 1874 from the Tithonian of Boulogne (France) and its transfer from the Valvatacea to the new forcipulatacean genus Psammaster gen. nov.
Figure 1. The lectotype NHMUK PI E 53996, in (a) abactinal and (b) actinal views, and (c, d) the paralectotype NHMUK PI E 1499, representing at least two individuals preserved as three separate elements, (c) NHMUK PI E 1499 i, and (d) NHMUK PI E 1499 ii and iii. Scale bars: 1 cm.
Figure 4 in A revision of Ophidiaster davidsoni de Loriol and Pellat 1874 from the Tithonian of Boulogne (France) and its transfer from the Valvatacea to the new forcipulatacean genus Psammaster gen. nov.
Figure 4. Strict consensus of the 61 most parsimonious trees found. Character matrix from Fau and Villier (2020) is available in the Supplement. Asteriidae (n = 12 taxa) and Zoroasteridae (n = 4 taxa) were grouped for clarity.
FIGURE. 3 in Aphelandra verticillata transferred to Holographis (Acanthaceae: Acantheae)
FIGURE. 3. Examples of pollen of Holographis (including H. verticillata) and Aphelandra. A, B. Holographis pallida Leonard & Gentry (Daniel 3382 from Sonora): A. Apertural view; B. Interapertural view. C, D. Holographis verticillata: C. Apertural view (Daniel et al. 3295 from Michoacán); D. Interapertural view (Moore 5503 from Guerrero). E, F. Aphelandra castaneifolia Britton (Daniel & Wood 10175 from Bolivia): E. Apertural view; F. Interapertural view. Scales = 5 µm.
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Dataset related to the publication "Anomalous radiative transfer in heterogeneous media"
<p>This repository contains Monte Carlo results for anomalous light transport in a spherical geometry with homogeneous (<code>Hom</code>), heterogeneous layered (<code>Lay</code>), and homogeneous layered (<code>VHom</code>) configurations.</p> <p>Part of these data is described in the publication:</p> <p>F. Tommasi et al. "Anomalous radiative transfer in heterogeneous media" <em>Advanced Theory and Simulations</em> (2024) <a href="https://doi.org/10.1002/adts.202400182">https://doi.org/10.1002/adts.202400182</a></p> <p><br>Data is organized in two types of csv files:</p> <ul> <li><code>filename.csv</code> containing information on the average path length and total path length distribution</li> <li><code>filename_Fluence.csv</code> containing information on the fluence rate and radiance at each spherical layer boundary</li> </ul> <p>Filenames contain information on the simulation parameters used:</p> <table> <tbody> <tr> <td><code>ISO</code></td> <td>isotropic scattering</td> </tr> <tr> <td><code>K0_X</code></td> <td>value of the k coefficient of the Generalized Pareto Distribution used in the Monte Carlo simulation (<code>X</code> = 0.3 or 0.7)</td> </tr> <tr> <td><code>1e7</code></td> <td>number of total trajectories considered</td> </tr> <tr> <td><code>DS</code>, <code>noDS</code></td> <td>simulation performed used the proposed set of rules for anomalous transport in bounded media (<code>DS</code>) or with the standard set of rules (<code>noDS</code>).</td> </tr> <tr> <td><code>Hom</code></td> <td>homogeneous sphere configuration</td> </tr> <tr> <td><code>NLay</code></td> <td>number of layers used in the layered configurations</td> </tr> <tr> <td><code>mism, no_mism</code></td> <td>configuration with (<code>mism</code>) or without (<code>no_mism</code>) refractive index mismatch with the environment or between layers</td> </tr> <tr> <td><code>Vhom_layN</code></td> <td>layered homogeneous sphere configuration with N layers</td> </tr> <tr> <td><code>mus_const</code>, <code>mus_step</code></td> <td>simulations performed using a constant scattering coefficient (<code>mus_const</code>) or different values across different layers (<code>mus_step</code>)</td> </tr> </tbody> </table> <p> </p> <p>The header of each output file contains general information on the simulation settings as detailed below:</p> <ul> <li>The number of layers</li> <li>The radius of each layer</li> <li>The refractive index of each layer and of the external region</li> <li>The critical angle for entering and exiting the sphere</li> <li>Reduced scattering coefficient of each layer</li> <li>Scattering function</li> <li>Type of illumination source</li> <li>Absorption coefficient of the sphere</li> </ul> <p>Simulations for different scattering strengths are classified based on the optical thickness <code>Taud</code> of the sphere, corresponding to the product between the sphere diameter (10 mm) and the reduced scattering coefficient. Since we have results only for isotropic scattering, <code>Taud</code> is also the product of diameter and scattering coefficient.<br>For a layered sphere, <code>Taud</code> is given by the sum of the products between the scattering coefficient and radial thickness of each layer.<br>The symbol <code>Taua</code> is also used to denote the product between the sphere and the absorption coefficient. Since we have considered only non-absorbing media, this value is always zero.<br>Each simulation is carried out for different values of the reduced scattering coefficient in the layers, keeping their reciprocal ratios fixed, resulting in the following values of Taud: 1E-3, 2E-3, 5E-3, 1E-2, 2E-2, 5E-2, 1E-1, 2E-1, 5E-1, 1, 2, 5, 10, 20, 50, 100, which, in the case of the homogeneous sphere, correspond to reduced scattering coefficients comprised between 1E-4 and 10.<br>The standard error (<code>SE</code>) is also provided, based on the results of 100 independent simulations with 1E5 trajectories each, for a total of 1E7 total trajectories considered.</p> <h3>Structure of the pathlength files (<code>filename.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li><code>Ksc_Max</code>: maximum number of scattering events in the medium</li> <li><code>de_max(mm)</code>: maximum pathlength followed by photons</li> <li>Number of Photons lost (always zero, all trajectories are collected)</li> <li>Total CW Exiting Radiation and Standard Error (SE)</li> <li>Mean Pathlength for Total Exiting Radiation and SE</li> <li>Solutions of the RTE for the non-absorbing sphere with Lambertian illumination given in terms of the mean pathlength in the sphere and partial pathlength in each layer</li> <li>Partial Mean Pathlengths for in each layer for the non-absorbing case</li> <li>Standard Error on Partial Mean Pathlengths in each layer</li> <li>Discrepancy and SE for the Pathlengths for the non-absorbing case</li> <li>Relative SE is shown for the Partial Mean Pathlengths in the non-absorbing case </li> <li>Total Spread Function (mm-1) versus the pathlength of emerging photons l (mm) for each Taud value</li> </ul> <h3>Structure of the fluence files (<code>filename_Fluence.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li>Mean pathlength and partial pathlength</li> <li>CW fluence at each layer boundary</li> <li><code>SE</code>: standard error for the CW fluence</li> <li>Discrepancy and SE of the CW fluence with respect to the expected value from the invariance properties. This is reported for each layer interface</li> <li>Relative Error of the CW fluence</li> <li><code>CW FLUX_P</code> at each layer boundary</li> <li>Distribution of CW radiance at each layer boundary for all <code>Taud</code> values. The distribution expected from the invariance property is also listed in the columns adjacent to the Monte Carlo results. Radiance data is reported as a function of the angle at each layer boundary</li> </ul> <p>Additional empty fields in the files refer to output quantities that can be optionally calculated during the simulations. These options were not selected for this study.</p> <p>For convenience, we list below which files were used to prepare each Figure (some files are used multiple times for different Figures):</p> <h3>Figure 2</h3> <p><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_noDS.csv</code></p> <h3>Figure 3</h3> <p><code>Lay/ISO_10lay_mism_k0_3_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_3_1e7_noDS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_noDS_Fluence.csv</code></p> <h3>Figure 4</h3> <p><code>Hom/ISO_Hom_no_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code></p> <h3>Figure 5</h3> <p><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay2_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay4_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay8_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay16_mism_k0_7_1e7_DS.csv</code></p>
Transfer function measurements for simulating environmental noise at hearable microphones
<p>This dataset is supplementary material to the conference paper "Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments" presented at ICASSP 2024 [1].</p> <p>The dataset consists of impulse response measurements for 18 device users (5 female, 13 male) wearing hearable devices in both ears. <br>The dataset was recorded in a sound-proof listening room using the Hearpiece prototype device (closed vent variant) [2] with a sampling frequency of 44.1 kHz.<br>Impulse responses were measured with exponential sweeps from 80 Hz to 22.05 kHz with a duration of 3s played from 8 loudspeakers arranged in a circle of approximately 1.5m radius. <br>The loudspeakers were located in the horizontal plane around the device users in 45°-steps (azimuth), starting from 22.5° to the right (where 0° is the front from the device users' perspective).</p> <p>The measurements are contained in the folder <code>measurements</code>. Each subfolder contains measurements from a different device user (e.g., <code>VP_01</code>). <br>Each file contains the measurement for one direction, e.g. <code>VP_01/data_0.npz</code> contains the measurement of device user <code>VP_01</code> for 22.5° azimuth, <code>VP_01/data_1.npz</code> is the measurement for the same device user for 22.5°+45° and so on.<br>Measurements of device users where the device could not be inserted, or where the fit did not provide sufficient attenuation of external sounds to the in-ear microphone, were excluded.</p> <p>The impulse responses for two Hearpiece devices (closed vent), the concha and in-ear microphones were measured.<br>A DPA 6060 lavalier clip microphone and a Tbone SC140 cardiod microphone were also included in the measurement as reference channels. </p> <p>The channels of the measurements (counting from 0):</p> <p> 0: Lavalier-microphone clipped to the shirt neck, shirt collar etc. of the device user<br> 1: Reference microphone about 50 cm in front of the device user<br> 2: Left in-ear microphone Hearpiece<br> 3: Left concha microphone Hearpiece<br> 4: Right in-ear microphone Hearpiece<br> 5: Right concha microphone Hearpiece</p> <p><br>The measurement consists of impulse responses from the loudspeaker to the hearable device microphones and reference microphones, and corresponding transfer functions. <br>Measurement metadata is included as well. <br>The measurement files contain a python dictionary with the following fields:</p> <ul> <li><code>test_signal</code>: the signal used for playback, consisting of a pause, the sweep, and another pause</li> <li><code>rec_signal</code>: the recorded signal (sweep played from the loudspeaker, recorded at the microphones)</li> <li><code>sweep</code>: the generated exponential sweep signal without pauses</li> <li><code>T</code>: actual duration of the sweep (~2 Seconds)</li> <li><code>sweep_inv</code>: inverse sweep (inverse w.r.t convolution of the sweep with the system response)</li> <li><code>sweep_inv_spectrum</code>: spectrum of the inverse sweep</li> <li><code>f11</code>: the frequency (in Hz) corresponding to the <code>RampLen</code> of the fade-in at the beginning of the sweep</li> <li><code>T_desd</code>: desired duration of the sweep in seconds (2 Seconds)</li> <li><code>T_rec</code>: recording duration in seconds (3 Seconds)</li> <li><code>start_frequency</code>: Minimum frequency in the measurement / first frequency in the sweep (80 Hz)</li> <li><code>RampLen</code>: Length of the fade-in ramp applied to the beginning of the sweep (based on a Hanning window) (2048 Samples)</li> <li><code>pre_pause_len</code>: pause time between starting the measurement and sweep playback (88200 Samples)</li> <li><code>after_pause_len</code>: pause time after sweep playback (44100 Samples)</li> <li><code>n_repetitions</code>: Number of repetitions for the measurement (1)</li> <li><code>n_channels</code>: Number of recorded channels including loopback (7 = 4 Hearpiece, 2 reference, 1 loopback)</li> <li><code>coh_mat</code>: Mean Squared Coherence per channel (between the measured sweep and the playback sweep signal), has shape (frequencies up to <code>samplerate</code>/2 x channels)</li> <li><code>ir_loopback</code>: the measured impulse response of the loopback channel, used to measure and compensate system delay from audio interface</li> <li><code>ir_mic</code>: the measured impulse responses of the hearable and reference microphones, with shape (samples, channels)</li> <li><code>tf_mic</code>: the measured transfer functions between the loudspeaker and the hearable and reference microphones, with shape (frequencies up to <code>samplerate</code>/2, channels)</li> <li><code>system_delay</code>: the measured system delay from the audio interface (position of the peak of the correlation between playback sweep and loopback sweep signals)</li> <li><code>samplerate</code>: The sampling rate used for the measurements (44100 Hz)</li> </ul> <p>This dataset is compatible with the German own voice recordings available at <a href="../records/10844599" target="_blank" rel="noopener">https://zenodo.org/records/10844599</a> (same participants+device insertion and measurement setup).</p> <p>The example script <code>generate_indiv_noise_dataset.py</code> can be used to augment a single-channel noise dataset to obtain simulated individual hearable noise signals,<br>similar to [1] but using impulse responses directly as filters instead of first computing relative transfer functions and then applying them in the STFT domain.</p> <p><br>[1] M. Ohlenbusch, C. Rollwage, S. Doclo: "Multi-microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments". In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, South Korea, Apr. 2024, pp. 416-420.<br>[2] F. Denk, M. Lettau, H. Schepker, S. Doclo, R. Roden, M. Blau, J.-H. Bach, J. Wellmann, and B. Kollmeier: "A One-Size-Fits-All Earpiece with Multiple Microphones and Drivers for Hearing Device Research". In: Proc. AES International Conference on Headphone Technology. San Francisco, USA, Aug. 2019.</p>
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
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Elastic electron scattering cross sections of ethanol in the energy range 30 eV to 800 eV: Differential (DCS), Integral (ICS) and Momentum Transfer Cross Sections (MTCS)
<h3>Cross section datasets on the elastic electron scattering of ethanol from our publication Eur. Phys. J. D 77, 52 (2023)</h3> <p>The elastic differential cross sections (DCS) are given in the energy range 30-800 eV in the full angular range: 30°-150° experimental, 0°-25° and 155°-180° extrapolated experimental data using the IAM-SCAR+I model.</p> <p>The integral elastic (ICS) and momentum transfer cross sections (MTCS) are given for energies 60-800 eV.</p> <p>Additional information can be found in the README.txt or the publication.</p>
Fig. 1 in Development of habitat suitability criteria for Neotropical stream fishes and an assessment of their transferability to streams with different conservation status
Fig. 1. Location of the study sites in the São José dos Dourados River basin, São Paulo State, Brazil (a and b). Forested sites (dark circles) are located within the greatest forest fragments and deforested (gray circles) streams lack arboreal vegetation in their riparian zone and are located in areas dominated by pasture (c).
Fig. 2 in Development of habitat suitability criteria for Neotropical stream fishes and an assessment of their transferability to streams with different conservation status
Fig. 2. Habitat suitability criteria for depth and velocity of 10 species (codes in Table 1) developed for forested streams, considering five classes of depth (D1 = 0 to 0.2 m; D2 = 0.21 to 0.30 m; D3 = 0.31 to 0.40 m; D4 = 0.41 to 0.50 m; D5> 0.51 m) and four classes of velocity (V1 => 0.0 to 0.2 m/s; V2 = 0.21 to 0.40 m/s; V3 = 0.41 to 0.60 m/s; V4> 0.61 m/s). The dotted lines indicate SI = 0.7, above which conditions were considered optimal.
Supplementary data to the paper: Toward a Novel Set of Pinna Anthropometric Features for Individualizing Head-Related Transfer Functions
<p>Supplementary research data to the <a href="https://doi.org/10.5281/zenodo.14338958" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Davide Fantini, Stavros Ntalampiras, Giorgio Presti, and Federico Avanzini. Toward a novel set of pinna anthropometric features for individualizing<br>head-related transfer functions. In <em>Proceedings of the 21th Sound and Music Computing Conference</em>, Porto, Portugal, July 2024.</p> </blockquote> <p>The repository includes the research data generated in the abovementioned paper. In particular, the repository includes:</p> <ul> <li><a href="../api/records/10805885/draft/files/README.md/content" target="_blank" rel="noopener noreferrer">README.md</a>: instructions for the data</li> <li><a href="../api/records/10805885/draft/files/pinna_images.mat/content" target="_blank" rel="noopener noreferrer">pinna_images.mat</a>: pinna depth images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/records/10805885/draft/files/landmarks.mat/content" target="_blank" rel="noopener noreferrer">landmarks.mat</a>: coordinates of the landmarks manually annotated on pinna depth images</li> <li><a href="../api/records/10805885/draft/files/anthropometry.mat/content" target="_blank" rel="noopener noreferrer">anthropometry.mat</a>: anthropometric parameters automatically extracted from manually annotated landmarks</li> <li><a href="../api/records/10805885/draft/files/anthropometry_documentation.pdf/content" target="_blank" rel="noopener">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../records/12698286/files/poster.pdf?download=1">poster.pdf</a>: poster presented at the SMC conference 2024</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
Data from the manuscript: "Few-femtosecond electron transfer dynamics in photoionized donor-pi-acceptor molecules"
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Transfer learning with generative models for object detection on limited datasets
<p>The provided datasets are used for the analysis in the work "Transfer learning with generative models for object detection on limited datasets" (https://doi.org/10.1088/2632-2153/ad65b5). The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine. The provided datasets are built with the help of Gligen and the already existing NuImages, Ozfish and Deepfish datasets. The file "CarGenerated.zip" contains images generated with Gligen and with provided bounding boxes around cars in an urban environment. The file "fishes_on_bkg.zip" provides fish images generated with fishes from Deepfish inpainted with Gligen on generated backgrounds. The file "fish_text.zip" contains images completely generated with Gligen containing fishes with annotated bounding boxes. Finally, the file "oz_masked_512.zip" contains a simpler dataset of copy paste images of Deepfish fishes on Ozfish backrounds. All the files contains the images saved in different folders for training and validation, plus an index file called gt_fish.csv for the bounding boxes.</p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</p> <p> </p>
Supporting Data for "Proton transfer in nonpolar solvents: an approach to generate electrolytes in aprotic media" (Phys. Chem. Chem. Phys., doi:10.1039/c8cp02349b)
<p>Conductivity data for all cation-anion pairs [units given in the header for each column].</p> <p>Small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]) of PLMA48 as a 2 wt % solution in n-dodecande-d26.</p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.