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1,891 results for “Field data”
Figs 9 – 16 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 9 – 16. Live specimens, Pholcus sabah Huber, 2011 (9 – 10), Ph. lintang Huber, sp. nov. (11 – 12) and Ph. erawan Huber, 2011 (13 – 16). 9. ♂ from Poring, Sabah. 10. ♂ from Sepilok, Sabah. 11 – 12. ♂ and ♀ from Bako, Sarawak. 13. ♂ from Khao Nan, Thailand. 14. Male from Erawan, Thailand. 15 – 16. ♂ and ♀ with egg-sac from Gunung Jerai, Malaysia.
Figs 102 – 109 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 102 – 109. Live specimens, Pholcus krabi Huber, sp. nov. (102 – 105), Ph. narathiwat Huber, sp. nov. (106 – 107), and Ph. kipungit Huber, sp. nov. (108 – 109). 102 – 104. ♂ and ♀ from Khao Sok, Thailand. 105. ♀ with egg-sac from Phanom Bencha, Thailand. 106 – 107. ♂ and ♀ from Hala Bala, Thailand. 108 – 109. ♂ and ♀ from Poring, Sabah.
Figs 63 – 67 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 63 – 67. Pholcus khaolek Huber, sp. nov., ZFMK Ar 15029 – 30. 63 – 64. Left male palp, prolateral and retrolateral views. 65. Male chelicerae, frontal view. 66 – 67. Cleared female genitalia, ventral and dorsal views. Abbreviations: a = appendix; b = genital bulb; e = embolus; p = procursus; u = uncus. Scale lines: 0.5 mm.
Figs 29 – 33 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 29 – 33. Pholcus ubin Huber, sp. nov., ZFMK Ar 15011. 29 – 30. Left male palp, prolateral and retrolateral views (arrow points at distinctive dorsal process; asterisk marks dorsal elongation of male palpal tarsus). 31. Male chelicerae, frontal view. 32 – 33. Cleared female genitalia, ventral and dorsal views. Abbreviations: a = appendix; b = genital bulb; e = embolus; p = procursus; u = uncus. Scale lines: 0.3 mm (31); 0.5 mm (29 – 30, 32 – 33).
Figs 1 – 8 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 1 – 8. Live specimens, Pholcus halabala Huber, 2011 (1 – 6) and Ph. ubin Huber, sp. nov. (7 – 8). 1. ♂ from MacRitchie, Singapore. 2. ♂ from Gunung Jerai, Malaysia. 3. ♀ with egg-sac from Gunung Liang, Malaysia. 4. Juvenile in web with silk tufts, Upper Selatar, Singapore. 5. ♀ with egg-sac from Hala Bala, Thailand. 6. ♀ with egg-sac from Ulu Dong, Malaysia. 7 – 8. ♂ and ♀ from Pulau Ubin, Singapore.
Figs 18 – 28. Pholcus halabala Huber, 2011 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 18 – 28. Pholcus halabala Huber, 2011, ZFMK Ar 15008. 18. Male ocular area, frontal view. 19. Modified hairs on male ocular area, dorsal view. 20. Male prosoma, frontal view. 21 – 22. Right procursus (and trochanter apophysis), retrolateral and retrolatero-distal views (arrows point at distal pocket). 23. Modified hairs on male ocular area. 24. Left bulbal processes, prolateral (slightly ventral) view. 25. Male ALS. 26. Male gonopore. 27. Comb-hairs on male tarsus 4. 28. Male distal cheliceral apophysis. Abbreviations: a = appendix; AME = anterior median eyes; e = embolus; p = procursus; tr = trochanter; u = uncus. Scale lines: 10 µm (25, 27); 20 µm (28); 30 µm (23); 40 µm (26); 80 µm (24); 100 µm (19, 21 – 22); 200 µm (18); 300 µm (20).
Figs 58 – 62 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 58 – 62. Pholcus kuhapimuk Huber, sp. nov., ZFMK Ar 15027. 58 – 59. Left male palp, prolateral and retrolateral views. 60. Male chelicerae, frontal view. 61 – 62. Cleared female genitalia, ventral and dorsal views. Abbreviations: a = appendix; b = genital bulb; e = embolus; p = procursus; u = uncus. Scale lines: 0.3 mm (61 – 62); 0.5 mm (58 – 60).
Figs 49 – 56 in The Southeast Asian Pholcus halabala species group (Araneae, Pholcidae): new data from field observations and ultrastructure
Figs 49 – 56. Live specimens, Pholcus kuhapimuk Huber, sp. nov. (49 – 51), Ph. khaolek Huber, sp. nov. (52 – 53), and Ph. sudhami Huber, 2011 (54 – 56). 49 – 51. ♂, penultimate instar ♂, and ♀ with egg-sac from Wat Kuhapimuk, Thailand. 52 – 53. ♀ with spiderlings and ♂ from Khao Lek, Thailand. 54 – 55. ♂ from Tham Phraya Nakhon, Thailand. 56. ♂ from Erawan, Thailand.
Calibration and Measurements during a field campaign in Rende_Data from SirGalahad, Tekran SPeciation Unit, UT-3000 analyzers
Open the record for dataset details and reuse information.
Hydraulic tomography and thermal tracer test data from a fractured-porous field site in Goettingen (Germany)
<p>These data are supplementary material for:</p> <p>Römhild, L., Ringel, L. M., Liu, Q., Hu, L., Ptak, T., & Bayer, P. (2024). Hybrid discrete fracture network inversion of hydraulic tomography data from a fractured-porous field site. Water Resources Research, 60, e2023WR036035. https://doi.org/10.1029/2023WR036035</p> <p>The data set comprises hydraulic tomography and thermal tracer test field data from a porous-fractured site in Göttingen (Germany). The repository contains a schematic figure of the experimental setup and subfolders for the two different experiments. For more information about the data, we refer to the readme files in the repository, and the soon-to-be-published paper (see above).</p>
Well log data grane oil field well 14
<p>ML dataset for steering decsions, Grane oil field, Norweagean</p>
Supplemental Data for Cell Reports Physical Science article "Probing transference and field-induced polymer velocity in block copolymer electrolytes"
<p>The data and Jupyter notebook uploaded here is Supplemental Information for the article:</p><p><strong>Probing transference and field-induced polymer velocity in block copolymer electrolytes </strong></p><p>Coauthored by:</p><p>Michael D. Galluzzo, Hans-Georg Steinrück, Christopher J. Takacs, Aashutosh Mistry, Lorena S. Grundy, Chuntian Cao, Suresh Narayanan, Eric M. Dufresne, Qingteng Zhang, Venkat Srinivasan, Michael F. Toney, and Nitash P. Balsara.</p><p>Journal: Cell Reports Physical Science</p><p>Notes:</p><ul><li>This depository includes the experimental data used in Figure 2, 3, and 4 of the main text and an additional data set.</li><li>The Jupyter notebook "velocity_Echem_Data.ipynb' can be used to visualize the data in the .csv files provided in the folder 'echem' and 'XPCS_fits'.</li><li>The folder 'echem' contains the raw electrochemical data obtained from the two XPCS experiments discussed in the main text and an additional experiment set.</li><li>The folder 'XPCS_fits' contains the results of fitting the autocorrelation functions at each spatial position in the cell at each time point for the two XPCS experiments discussed in the main text and an additional experiment set. </li><li>The additional experiment included here (reffered to as Cell P in the Jupyter notebook) is not discussed in the main text, however it demonstrates that the second 'hump' in velocity (see Figure 3 and S5) that is observed after switching the direction of polarization was replicated in a separate experiment.</li></ul><p> </p>
Data on heat tolerance from: Field and laboratory studies reveal interacting effects of stream oxygenation and warming on aquatic ectotherms
<p>Heat tolerance data on two widespread Eurasian mayflies, <i>Ephemera danica</i>, Müller 1764 and <i>Serratella ignita</i> (Poda 1761).</p><p>Methods thermal tolerance experiments</p><p>Mayfly nymphs for laboratory experiments were collected in spring (early May) from Torrington River, Devon, UK, ranging in fresh weight between 15 and 128 mg (<i>E. danica</i>) and between 2.0 and 11.6 mg (<i>S. ignita</i>). Nymphs were maintained in the laboratory at 10 ± 1 °C in a 12 L:12 D regime in aquaria containing artificial pond water, buffered and diluted to reflect the pH and conductivity of the field site. Before recording critical temperatures, all species were acclimated for at least 7 days to laboratory conditions.</p><p>To assess critical thermal maxima (<i>CT</i>max), we employed previously described methods (Verberk & Bilton, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13240#gcb13240-bib-0051">2011</a>; Verberk & Calosi, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13240#gcb13240-bib-0054">2012</a>). Individual nymphs (<i>n</i> = 18 for <i>E. danica</i> and <i>n</i> = 27 for <i>S. ignita</i>) were placed in flow-through chambers and water was supplied to these chambers from a header tank after having passed through a tubular counter-current heat exchanger. Water in the header tank was of the same composition as that used to maintain animals and was bubbled with a mixture of 20% oxygen and 80% nitrogen, obtained using a gas-mixing pump (Wösthoff, Bochum, Germany). Individuals were left resting for 1 h at the equilibration temperature of 10 °C, after which temperature in the experimental chambers was increased by 0.25 °C min−1, using a Grant R5 water bath with a GP200 pump unit (Grant Instrument Ltd, Cambridge, UK), connected to the heat exchanger. Temperatures were logged using a HH806AU digital thermometer (Omega Engineering Inc., Stamford, CT, USA). Different sized flow-through chambers were used for each species. <i>E. danica</i> was placed in larger chambers (70 × 70 × 30 mm) and provided with sand as burrowing substrate, which they readily used. <i>S. ignita</i> was placed in smaller cylindrical chambers (6 mm in diameter, 20 mm long) and their behaviour was observed under a magnifying glass. The amount of water passing through these flow-through chambers was matched to their size. For the larger chambers containing <i>E. danica</i>, water was supplied to five chambers (total volume of 0.735 l) at a flow rates of 0.031–0.033 l per second, resulting in a refresh rate of 22–24 s. For the smaller chambers with <i>S. ignita</i>, water was supplied to each chamber individually at 0.21–0.22 ml per second, resulting in a refresh rate of 10–11 s.</p><p><i>CT</i>max is defined as the point at which an animal loses its ability to escape from conditions that will lead to its death (Lutterschmidt & Hutchison, <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13240#gcb13240-bib-0027">1997</a>). During progressive warming, nymphs of <i>E. danica</i> first emerged from their burrowed position and began swimming (at about 6 °C below <i>CT</i>max). Loss of equilibrium occurred next as nymphs fell upon their backs, which was followed by the onset of spasms. After that, gill movement was no longer coordinated and faltered and this endpoint could be most reliably determined and is here taken as <i>CT</i>max. Similarly<i>, S. ignita</i> stopped ventilation and movement at <i>CT</i>max. Below <i>CT</i>max, larvae were inactive, until near the end of the trials, when they began to crawl, lose equilibrium and gill beating became intermittent shortly before stopping altogether at <i>CT</i>max.</p><p><i>CT</i>max was assessed at hypoxic (5 kPa), normoxic (20 kPa) and hyperoxic (60 kPa) conditions. Different levels of oxygenation were achieved by changing the oxygen–nitrogen gas mixture obtained using the gas-mixing pump (Wösthoff). The gas mixture was adjusted 10 min after placing the animals in the small flow-through chambers, to allow for gradual exposure to hypoxic and hyperoxic conditions during the 1 h resting period. To prevent equilibration with the atmosphere, the header tank was sealed using an 18 mm thick expanded polystyrene sheeting and other openings were closed off with plastic material. During the 1 h resting period, oxygen levels in the outflow water from the chambers were measured approximately every 15 min, to verify that the oxygen levels had stabilized to hypoxic, normoxic and hyperoxic conditions at the onset of warming. Because some equilibration with the atmosphere could not be prevented, nominal output values from the gas mixer were slightly more extreme (3 kPa for hypoxia and 65 kPa for hyperoxia) in order to achieve the desired oxygen conditions in the test chambers.</p>
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Data supporting the publication "Observation of Landau levels and chiral edge states in photonic crystals through pseudomagnetic fields induced by synthetic strain"
<p>Data supporting the publication "Observation of Landau levels and chiral edge states in photonic crystals through pseudomagnetic fields induced by synthetic strain".</p> <p>Version 2: updated Figure 1c1</p>
Data for: Above-ground dry mass and fruit geometries of 31 field muskmelon plants
<p>We collected a total of 751 fruits from 31 <em>Cucumis melo </em>L. var. <em>agrestis</em> Naud. plants, digitized the side profiles of these fruits, and applied an explicit re-expression of Preston's equation (referred to as EPE hereinafter) to fit the planar coordinates of each fruit profile. By using the estimated parameters, we calculated the volume and surface area of each fruit. The black_white.zip file saves the black and white .bmp images at 600 dpi for the 751 fruits profiles. The edge_data.zip file includes the planar coordinates of each of the 751 fruits, and the EPE_results.csv file includes the estimated parameters and goodness-of-fit of the EPE, and the fruit surface area and volume calculated by the surface area and volume formulas of the solid of revolution based on the EPE for each fruit. In addition, the EPE_results.csv file also provides the measured volumes of the 751 fruits using the graduated cylinders. The MPG.csv file includes the total leaf dry mass, total stem dry mass, and total fruit volume of 31 <em>C. melo</em> var. <em>agrestis </em>plants.</p>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
Data and code for "Quasiparticle effects in magnetic-field-resilient 3D transmons"
<p>This folder contains all data files and Jupyter notebooks needed to recreate the figures of our publication, <br>"Quasiparticle effects in magnetic-field-resilient 3D transmons",<br>written by J. Krause, G. Marchegiani, L. M. Janssen, G. Catelani, Yoichi Ando, and C. Dickel</p> <p>Measurements were done with quantify-core (https://quantify-os.org/docs/quantify-core/)</p> <p>Required python packages are quantify-core (tested to run with version 0.7.4) and all its dependencies, as well as cmcrameri (colormaps used for plots).<br>Qutip is used for master equation solving and Hamiltonian modeling (used version 4.7.5) and hmmlearn (version 0.3.2) for fitting gaussian hidden-markov-models.<br>From a clean conda environment one should just install the above 4 packages and it should work.</p> <p>Content:<br>Data/ <br>Contains the plotted measurement data used in the figures of the paper mostly in csv format.<br>The datasets are loaded and plotted in the respective notebooks for all figures. </p> <p>Data/quantify_datasets/<br>Contains a few quantify datasets (raw data format of quantify measurements)</p> <p>Data/20220710_parity_paper_device_afm/<br>Contains .tiff images and .txt files with AFM data of the device</p> <p>Figures/<br>Contains all paper figures as .pdf files</p> <p>Fits/transmon_spectrum_including_EJ_harmonics<br>Contains some auxiliary fit results so the fits of flux arcs do not have to be repeated.</p> <p>Fits/Gap_vs_Bpar<br>Contains numerical results for the superconducting gap Delta as a function of magnetic field for aluminum films of different thickness.<br>Used to check the cavity data and not used for other modeling.<br>Imported in Jupyter_notebooks/models_transmon_spectrum_including_higher_harmonics.py</p> <p>Jupyter_notebooks/<br>Contains the jupyter notebooks with the code that creates the figures including the relevant calculations and fits and the loading of the data.<br>The notebook name gives the figure(s) it creates.<br>There are several auxiliary .py files and two notebooks that do not make paper figures:</p> <p>Jupyter_notebooks/fit_params.py<br>Contains the fit parameters for the quasiparticle modeling and the transmon spectrum</p> <p>Jupyter_notebooks/models_qp_dynamics.py<br>Contains the functions for quasiparticle modeling vs field and temperature.</p> <p>Jupyter_notebooks/models_transmon_spectrum_including_higher_harmonics.py<br>Contains the models for the field dependent spectrum</p> <p>Jupyter_notebooks/models_cavity_transmon_interaction.py<br>Contains qutip model for the fitting of the transmon-cavity Hamiltonian to estimate the bare-dressed changes.</p> <p>Jupyter_notebooks/fit_quasiparticle_model.ipynb<br>Notebook that shows how the quasiparticle model can be fit to the data<br>Takes a bit longer to execute</p> <p>Jupyter_notebooks/fit_cavity_transmon_interaction.ipynb<br>Looks at a resonator-transmon model to understand the bare-dressed difference and estimate the coupling.<br>Not really used in the paper, except to estimate G. </p>
Data archive for the peer-reviewed journal article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements"
<p>This data archive accompanying the article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements", which was accepted in April 2024 in the peer-reviewed journal <strong><em>Science of the Total Environment</em></strong>. This data archive contains the processed OPvDTT measurements, chemical speciation of PM2.5, and source contribution used in the manuscript.</p>
Field data of soil greenhouse gas fluxes from SOMMIT long-term experiments
<p>This is a database of field data of soil greenhouse gas fluxes and ancillary data from long-term experiments (LTEs) that participated in the task 1 of the work package 3 from SOMMIT. As of November 2024 (V 1.0) six LTEs have contributed with data: "ACBB Estrées-Mons, France", "Rutzendorf_17, Austria", "Maintainance of organic orchards, Italy", "Fagna, Italy", "ULBF-Ljubljana, Slovenia" and "Grabow 2, Poland". All LTEs are referenced according to the LTE Index from <a href="https://doi.org/10.5281/zenodo.7598122" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7598122</a>. The data includes soil greenhouse gas fluxes (N2O, CO2 and CH4) under different management practices, as well as ancillary data that might be useful to explain the observed fluxes. The database includes metadata on how soil greenhouse flux data and ancillary data were collected. Individual soil gas flux estimates are also expressed in CO2 equivalents [mg CO2-eq m-2 h-1], thereby providing a dataset on the contribution of soil CO2, N2O and CH4 fluxes to the soil global warming potential. This product is part of the EJP SOIL internal project SOMMIT, and serves as deliverables WP3.2 and WP3.3.</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.