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Fig. 6. Clionolithes palmatus Clarke, 1908. A in Taming an ichnotaxonomical Pandora's box: revision of dendritic and rosetted microborings (ichnofamily: Dendrinidae)
Fig. 6. Clionolithes palmatus Clarke, 1908. A. Original drawing of the lectotype, a natural cast in a shell of Loxopteria dispar Sandberger & Sandberger, 1857 from the Upper Devonian of New York State, USA (reproduced from Clarke 1908: pl. 12, fig. 1). B. The lectotype in its present state, either slightly damaged or originally idealised. C. Original drawing of the paralectotype, a natural cast in a Loxonema danai Clarke, 1904 from the type locality (reproduced from Clarke 1908: pl. 12, fig. 2). D. Significantly damaged paralectotype. E–F. A number of natural casts in a shell of Leptostrophia Hall & Clarke, 1892 from the Devonian of Victoria, Australia, including the holotype (close-up in F) and several paratypes of junior synonym Clionolithes sollei Talent, 1963 (compare with Talent 1963: pl. 9, figs 3–8). G. SEM of a specimen cast in epoxy resin by Plewes (1996) from the Devonian of Iowa, USA, illustrating the morphological range within one trace, comprising sheet-like fans and galleries terminating in fine ramifications (reproduced from Plewes 1996: pl. 1, fig. 1).
Fig. 14 in Taming an ichnotaxonomical Pandora's box: revision of dendritic and rosetted microborings (ichnofamily: Dendrinidae)
Fig. 14. Calcideletrix fastigata (Radtke, 1991) comb. nov. A–C. SEM of the lectotype (together with fungal trace Saccomorpha clava Radtke, 1991) in an epoxy cast of an Oligocene Pecten bivalve shell from the Niederrheinische Bucht, Germany. Close-up in B illustrates the fine rhizoidal connections to the substrate surface. Close-up in C shows framboidal pyrite spheres (fossil fungal spores?) embedded within the resin. D–F. Large paralectotype in a cast of an Oligocene Ostrea bivalve shell from the same locality. Close-up in E exhibits the alternating bifurcation pattern of very fine peripheral galleries. Closeup in F shows the centre of the trace with further framboidal pyrite aggregates; epoxy resin was hindered in penetrating the cavity due to calcite spar (now dissolved).
Fig. 5. Clionolithes radicans Clarke, 1908. A in Taming an ichnotaxonomical Pandora's box: revision of dendritic and rosetted microborings (ichnofamily: Dendrinidae)
Fig. 5. Clionolithes radicans Clarke, 1908. A. Original illustration of the lectotype from the Devonian of Mansfield, Pennsylvania, USA (reproduced from Clarke 1908: pl. 11, fig. 1). B–C. Overview and close-up of a shell of the brachiopod Atrypa bearing the lectotype and a number of further specimens. D. Holotype of junior synonym Olkenbachia hirsuta Solle, 1938 from the Devonian near Koblenz, Germany, reproduced from Solle (1938: fig. 2). E–F. Overview and SEM close-up of one of the paratypes of junior synonym Olkenbachia hirsuta (compare with Solle 1938: fig. 4).
Fig. 1 in Taming an ichnotaxonomical Pandora's box: revision of dendritic and rosetted microborings (ichnofamily: Dendrinidae)
Fig. 1 (page 7). Morphological characters of dendrinid microborings with respect to overall shape and symmetry of the trace, its vertical profile, openings to the substrate surface, orientation of entrance tunnel (where present), branching pattern and surface texture (in order of decreasing relevance as ichnotaxobase).
Ngwi languages comparative tone box
<p>This is a comparative tone box of the Ngwi branch. Previous research has identified consonantal effects on pitch as important precursors to the phonologization of rising tones. However, evidence from tonal evolution studies in East and Southeast Asian languages suggests that the high register (i.e., tones above mid pitch) is an additional source of rising. The current study examines the plausibility of the high > rising pathway by testing two hypotheses in a ‘dialect laboratory’: (1) that a rising tone in one language will correspond to a high register tone in a closely related dialect or language, and (2) that consonantal conditioning is not required for a rising tone to emerge. These hypotheses are tested on transcribed tone data from 14 Ngwi (Loloish, Tibeto-Burman) language clusters (82 language varieties in total), organized into a comparative tone box.</p> <p>This study examines the correspondence patterns of rising tones across a database of 82 Ngwi languages and dialects, grouped into 14 lower-level clusters. The data consist of the language varieties’ current reflexes of PN tone categories, compiled from published and unpublished sources (see Appendix 1 for a complete listing). To facilitate comparison, the tonal reflexes are arranged in a tone box, adapted from Gedney (1972), with each row representing a separate initial consonant type, grouped together by PN tone category, and each column representing a different Ngwi language variety.</p> <p>The results support both hypotheses. The findings imply that rising tones evolve from high register tones, with or without consonant conditioning.Previous research has identified consonantal effects on pitch as important precursors to the phonologization of rising tones. However, evidence from tonal evolution studies in East and Southeast Asian languages suggests that the high register (i.e., tones above mid pitch) is an additional source of rising. The current study examines the plausibility of the high > rising pathway by testing two hypotheses in a ‘dialect laboratory’: (1) that a rising tone in one language will correspond to a high register tone in a closely related dialect or language, and (2) that consonantal conditioning is not required for a rising tone to emerge. These hypotheses are tested on transcribed tone data from 14 Ngwi (Loloish, Tibeto-Burman) language clusters (82 language varieties in total), organized into a comparative tone box. The results support both hypotheses. The findings imply that rising tones evolve from high register tones, with or without consonant conditioning.</p>
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
How to transport veterinary drugs in insulated boxes to avoid thermal damage by heating or freezing
<p><strong>Documentation of the empiric data, measured in the heating and cooling chamber to determine the thermal constant for the investigated transport boxes.</strong></p> <p> </p> <p><strong>Background: </strong>The transport of veterinary drugs must comply with the general standards for drug storage. Although many vehicles are equipped with active heating and/or cooling devices assuring recommended storage conditions, simple insulated transport boxes are also often used. In this study, measurements for typical transport boxes were performed under laboratory conditions by the use of a climate chamber for a temperature of -20°C and 45°C to investigate the impact of box size, insulation material, liquid vs. dry filling products, filling degree and other parameters on the thermal performance of insulated boxes. Model calculations and instructions are presented to predict the retention time of recommended drug storage temperatures.</p> <p><strong>Results: </strong>The measurements and the model calculations showed that the loading of the transport boxes with additional water bottles to increase the heat capacity is appropriate to prolong the retention time of the recommended temperature range of the drugs. Insulated transport boxes are not suitable to store drugs over a period of more than approximately 12 hours. For practical use, a recipe is presented to measure the thermal properties of a transport box and the related retention time for which the recommended storage temperatures can be assured.</p> <p><strong>Conclusions: </strong>The following principles for drug transportation in vehicles are recommended: (1) Before transfer into boxes, drugs should always be thermally preconditioned (2) Increase the filling degree of the boxes with thermally preconditioned water bottles or re-usable thermal packs will increase the heat capacity. Do not deep-freeze the bottles or packs below 0°C to avoid drug freezing due to contact. (3) Open the lid of the boxes only to uncase drugs that are immediately needed. (4) The bigger the box and the higher the filling degree, the longer the retention time of the transport box. (5) Wherever possible, place the drug box at a cool site inside the vehicle. (6) The monitoring of the inside temperature of the transport boxes is recommended. <br> By the proper use of such transport boxes the recommended temperatures can be maintained over one working day.</p> <p> </p>
Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B in Head geometric morphometrics of two Chagas disease vectors from Venezuela
Figure 2. Box-plot head centroid size. A. Rhodnius prolixus instars. B. Triatoma maculata instars. Abbreviation: I—First instar; II— Second instar; III—Third instar; IV—Fourth instar; V—Fifth instar; F—Adult female; M—Adult male.
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle
Fig. 3. Box plot showing a in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study
Fig. 3. Box plot showing a comparison of the angle at the intersection of the R4+5 vein and the middle line for all species used in the analysis.
TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass
<p><strong>Introduction</strong></p> <p>During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (<a href="http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00246.1">Rotach et al. (2017)</a> and <a href="https://fileshare.uibk.ac.at/f/9f1101851849439483de/">i-Box WIKI</a> for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements with a spatio-temporal resolution of 0.127 m and 1 s were added to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (<a href="https://doi.org/10.1007/s10546-021-00618-0">Pfister et al. 2021</a>) and can reveal processes within the boundary layer during the morning and evening transition (<a href="https://doi.org/10.1029/2020GL092238">Fritz et al. 2021</a>).</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://doi.org/10.15203/99106-003-1">Serafin et al. (2020)</a> and in <a href="https://doi.org/10.1175/bams-d-21-0232.1">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Location</strong></p> <p>The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341°N, 11.62219°E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level.</p> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods:</p> <ul> <li>08.06.-14.06.2022</li> <li>28.06.-18.07.2022</li> </ul> <p><strong>3. Instrument details</strong></p> <p><em><strong>Distributed temperature sensing</strong></em></p> <p>For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels at 1~Hz with a spatial resolution of 0.127~m. The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable (900 µm outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 µm with 50 µm core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's provided double-ended mode (<a href="https://doi.org/10.3390/s20082235">des Tombe et al. 2020</a>, <a href="https://doi.org/10.5194/essd-14-885-2022">Lapo et al. 2022</a>). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described in <a href="https://doi.org/10.3390/s20082235">des Tombe et al. (2020)</a> and implemented in the <em>dtscalibration</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">des Tombe et al. 2022</a>) and all processing was completed using the <em>pyfocs</em> software package (<a href="https://doi.org/10.5281/zenodo.7111585">Lapo and Freundorfer 2020</a>) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files.</p> <p>After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath.</p> <p>Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data is only a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber.</p> <p>The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22.</p> <p><em><strong>i-Box tower</strong></em></p> <p>Full site description and all data exceeding the DTS observations can be found on <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a>. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data:</p> <ul> <li>EC flux level 1:<br>Combination of ultrasonic anemometer CSAT3 (orientation from North: 30°) and infrared gas analyzer EC150 from Campbell Scientific</li> <li>EC flux level 2:<br>Ultrasonic anemometer CSAT3 (orientation from North: 30°) from Campbell Scientific</li> <li>low-frequency data: <ul> <li>pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl</li> <li>radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp & Zonen, Delft, Netherlands) at 2 m agl</li> <li>temperature profile: Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m</li> <li>wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m</li> </ul> </li> </ul> <p>For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here:</p> <ul> <li>EC fluxes processing:<br>Averaging interval of 30 min performed by the software <a href="https://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/">EdiRe</a><br>Processing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m</li> <li>EC flux Quality Criteria (QC) flags: <ul> <li>-1: all data</li> <li>0: excluding instrument malfunction</li> <li>1: additionally skewness within range (-2 to 2) and kurtosis <8 following Vickers and Mahrt 1997</li> <li>2: additionally exclude non-stationary data</li> </ul> </li> <li>EC flux Flags: <ul> <li>0: data ok</li> <li>1: data not ok (see description of individual flag for further details)</li> </ul> </li> </ul> <p><strong>4. Data file structure</strong></p> <p><em><strong>Zip folders</strong></em></p> <p>Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given:</p> <ul> <li>DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip</li> <li>EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip</li> <li>Low-frequency data (1 min): InnDEX22_low_frequency_data.zip</li> </ul> <p><em><strong>File format</strong></em></p> <p>Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter.</p> <p><strong>6. Contact</strong></p> <p>Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set.</p> <p><em><strong>Acknowledgements</strong></em></p> <p>Special thanks to the "Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22.</p> <p><strong>7. References</strong></p> <p>Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., & Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. <em>Geophysical Research Letters</em>, 48, e2020GL092238. <a href="https://doi.org/10.1029/2020GL092238">https://doi.org/10.1029/2020GL092238</a></p> <p>des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. <em>Sensors</em>, 20, 2235. <a href="https://doi.org/10.3390/s20082235">https://doi.org/10.3390/s20082235</a></p> <p>des Tombe, Bas François, & Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. <em>Boundary-Layer Meteorol</em> 180, 187–202. <a href="https://doi.org/10.1007/s10546-021-00618-0">https://doi.org/10.1007/s10546-021-00618-0</a></p> <p>Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.7111585">https://doi.org/10.5281/zenodo.7111585</a></p> <p>Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, <em>Earth Syst. Sci. Data</em>, 14, 885–906 <a href="https://doi.org/10.5194/essd-14-885-2022">https://doi.org/10.5194/essd-14-885-2022</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. <em>Innsbruck University Press</em>. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. <em>Bulletin of the American Meteorological Society</em>, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs.
Fig. 7. Box plots comparing average counts per 10 in Trypanosomiasis: An emerging disease in Alpine swift (Tachymarptis melba) nestlings in Switzerland?
Fig. 7. Box plots comparing average counts per 10 HPF of granulocytes, mononuclear cells, and thrombocytes between positive (n = 20) and negative (n = 20) 45- day-old Alpine swift nestlings sampled in 2022.
Pockmark Bounding Box Detection and Segmentation Labels
<p>This dataset contains 256x256 pixel jpeg images of gridded depth values as well as binary masks for pixels containing pockmarks (these jpegs are merged together with the depth image on the left and the mask on the right, making a 512x256 image). These are contained within the subdirectory 'PockmarkMaskAnnotations'.</p> <p>Additionally, this dataset contains a csv containing bounding box annotations (label, bounding box coordinates in terms of image pixels, and a unique integer for the label) of pockmarks in each image. These are contained within the subdirectory 'PockmarkBoxAnnotations'.</p> <p>Together, these annotations can be used to construct either a bounding box object detector, a bounding box and mask object detector, or a semantic segmentation model.</p> <p>These were the labels used for the experiments described in Lundine et al., 2023. See this reference to find original data sources to the collected bathymetry data.</p> <p>Lundine, M., Brothers, L., Trembanis, A., Deep learning-based pockmark detection: implications for quantitative seafloor characterization, Geomorphology, 2023, Volume 421, 108524, <a href="https://doi.org/10.1016/j.geomorph.2022.108524">https://doi.org/10.1016/j.geomorph.2022.108524</a>.</p> <p> </p>
bioRxiv 10k figure bounding boxes
<p>This dataset contains figure bounding boxes corresponding to the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>It provides annotations in two formats:</p> <ul> <li><a href="https://cocodataset.org/#format-data">COCO format (JSON)</a></li> <li>JATS XML with <a href="https://github.com/kermitt2/grobid/blob/0.7.0/doc/Coordinates-in-PDF.md">GROBID's "coords" attribute</a></li> </ul> <p>The COCO format contains bounding boxes in rendered pixel units, as well as PDF user units. The latter uses field names with the "pt_" prefix.</p> <p>The "coords" attribute uses the PDF user units.</p> <p>The dataset was generated by using an algorithm to find the figure images within the rendered PDF pages. The main algorithm used for that purpose is <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform">SIFT</a>. As a fallback, <a href="https://docs.opencv.org/4.5.3/d4/dc6/tutorial_py_template_matching.html">OpenCV's Template Matching</a> (with multi scaling) was used. There may be some error cases in the document. Very few documents were excluded, were neither algorithm was able to find any match for one of the figure images (six documents in the train subset, two documents in the test subset).</p> <p>Figure images may appear next to a figure description, but they may also appear as "attachments". The latter usually appears at the end of the document (but not always) and often on pages with dimensions different to the regular page size (but not always).</p> <p>This dataset itself doesn't contain any images. The PDF to render pages can be found in the <a href="https://doi.org/10.5281/zenodo.3873702">bioRxiv 10k dataset</a>.</p> <p>The dataset is intended for training or evaluation purposes of the semantic Figure extraction. The evaluation score would be calculated by comparing the extracted bounding boxes with the one from this purpose. (example implementation <a href="https://github.com/elifesciences/sciencebeam-judge">ScienceBeam Judge</a>)</p> <p>The dataset was created as part of <a href="https://elifesciences.org/">eLife</a>'s <a href="https://github.com/elifesciences/sciencebeam">ScienceBeam</a> project.</p>
3D model of a box-type structure under a small cairn near the Bear Trap in Northwest Greenland
<p>This dataset consists of files that can be used to view a high-resolution 3D model of a box-type structure under a small cairn in the vicinity of ‘The Bear Trap’. The interior of the stone box appears to have been completely empty. Similar small stone box structures have been identified and discussed by Schedermann (e.g. 1990: 159) for Arctic Small Tool tradition (ASTt) sites on Skraeling Island and by McGee (1979) for Port Refuge in the Canadian High Arctic. Similar and equally enigmatic features have also been described by Knuth (1966/67: 203) for far north and northeast Greenland. They have been alternatively attributed to numerous PalaeoEskimo cultural complexes, but without dated materials from the site current attribution of the feature’s function, significance or date are not possible.</p> <p>The 3D model was created from 280 digital photographs that were processed usingAgisoft Metashape Pro v1.7. More information is provided in processing report and the readme file that accompanies this dataset. </p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts. </p> <p>Knuth, Egil. (1966/67) The ruins of the Musk Ox Highway. <em>Folk</em> 8-9: 191-219.</p> <p>McGee, Robert. (1979) <em>The Palaeoeskimo occupations at Port Refuge, High Arctic Canada</em>. National Museum of Man Mercury Series. Archaeological Survey of Canada Paper No. 92. Ottawa: National Museums of Canada.</p> <p>Schledermann, Peter. (1990) <em>Crossroads to Greenland: 3000 years of prehistory in the Eastern High Arctic</em>.Calgary: The Arctic Institute of North America of the University of Calgary.</p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to <em>Arctic Anthropology</em></p>
Used portable batteries with bounding boxes
<p>This dataset contains images and labels of used portable batteries. Battery types are:</p> <ol> <li>Lithium-ion (LIION, class 0) </li> <li>Lithium polymer (LIPO, class 1) </li> <li>Lead-acid (PB, class 2)</li> <li>Nickel-cadmium (NICD, class 3)</li> <li>Nickel-metal hydride (NIMH, class 4)</li> </ol> <p>Each image contains multiple batteries of the same type. The file name defines the type (e.g. LIPO_IMG_4920.JPG is an image with multiple lithium polymer batteries). Each image file is accompanied by a label file (*.txt) with identical name except the file name extension. Each row in the label file contains a class label (0 to 4) followed by bounding box coordinates in YOLO format.</p> <p>This dataset was produced in Horizon 2020 funded project <a href="https://trinityrobotics.eu">TRINITY</a>.</p>
IPCC AR6 Relative Sea Level Projection P-Boxes
<p><strong>Description</strong></p> <p>This data set contains detailed elements of the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections for all of the p-boxes described in AR6 WG1 9.6.3 (under ar6-regional-pboxes.zip), as well as a variant excluding the AR6 estimates of background sea level change (under ar6-regional_novlm-pboxes.zip).</p> <p>Most users will not want this dataset, but rather the dataset at https://doi.org/10.5281/zenodo.5914709. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p> <p> </p>
Types of Custom Boxes For Bath Bombs
<p><strong>There are many types of bath bomb boxes available, which are given below:</strong></p> <p><strong>Boxes By Style</strong></p> <p><em><strong>1. Boxes With Transparent Window</strong></em></p> <p>Some <a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Boxes For Bath Bombs</strong></a> feature a transparent window, allowing customers to check the product before buying it. </p> <p><em><strong>2. Boxes With Colored Ribbons</strong></em></p> <p>Others have colored ribbons to enhance the presentation of Bath Bombs Boxes Wholesale. <br> Regardless of the type of box you choose, it must meet your brand guidelines. These tips will help you create the most appealing packaging for your bath bombs. </p> <p><strong>Examples Of Bath Bomb Boxes</strong></p> <ul> <li><em><strong>Custom Printed Boxes For Bath Bombs</strong></em></li> </ul> <p><a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Custom Bath Bomb Boxes</strong></a> for bath bombs are an excellent way to show off your products. They're durable and attractive, so customers will want to keep them around. You can add window panes and even your company's logo or product-related information to these boxes. </p> <p><strong>Show Your Brand UP</strong></p> <p>A unique box can give your brand a distinct advantage over your competition. While a typical bath bomb box can be purchased anywhere, a unique and beautiful box can make your bath bombs stand out among the crowd.</p> <p>Whether your product is a luxury item or an essential item, a beautiful box will help your brand shine. Mailer bath bomb boxes are an environmentally-friendly choice, and they're versatile and easy to tailor. </p> <p><a href="https://www.thecosmeticboxes.com/product/bath-bomb-boxes/"><strong>Eco Friendly Bath Bomb Packaging</strong></a> is ideal for products that don't need external packaging and need to stay dry for transport. They're also lightweight and cheap to ship compared to other types of packaging. Order free samples and try out different designs to make your bath bombs more visually appealing.</p>
Figure 1 in Eriophyoid (Trombidiformes: Eriophyoidea) mite species associated with boxes worldwide with a new record of Eriophyes canestrinii (Nalepa, 1890) from Iran
Figure 1. Schematic drawings of Eriophyes canestrinii (Nalepa, 1890) – AD. Prodorsal shield; AL. Lateral view of anterior body region; CG. Female coxigenital region; em. Empodium; GM. Genital region, Male; IG. Internal female genitalia; LO. Lateral view of annuli; L1. Leg I; PM. Lateral view of posterior opisthosoma. Scale bar: 10
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.