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1,393 results for “Trace”
Trace Gas Fluxes and Soil N Dynamics in Simulated Hurricane Experiment at Harvard Forest 1989-1991
This study examined the fluxes of greenhouse gases between soils and the atmosphere in the Simulated Hurricane Experiment. The abstract from the published paper (see Methods) is reproduced below. "Fluxes of nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) between soils and the atmosphere were measured monthly for one year in a 77-year-old temperate hardwood forest following a simulated hurricane blowdown. Emissions of CO2 and uptake of CH4 for the control plot were 4.92 MT C ha-1 y-1 and 3.87 kg C ha-1 y-1, respectively, and were not significantly different from the blowdown plot. Annual N2O emissions in the control plot (0.23 kg N ha-1 y-1) were low and were reduced 78% by the blowdown. Net N mineralization was not affected by the blowdown. Net nitrification was greater in the blowdown than in the control, however, the absolute rate of net nitrification, as well as the proportion of mineralized N that was nitrified, remained low. Fluxes of CO2 and CH4 were correlated positively to soil temperature, and CH4 uptake showed a negative relationship to soil moisture. Substantial resprouting and leafing out of downed or damaged trees, and increased growth of understory vegetation following the blowdown, were probably responsible for the relatively small differences in soil temperature, moisture, N availability, and net N mineralization and net nitrification between the control and blowdown plots, thus resulting in no change in CO2 or CH4 fluxes, and no increase in N2O emission."
CFCs and Radiatively Important Trace Species at Harvard Forest EMS Tower 1996-2005
Measurements of 13 ozone-depleting and/or greenhouse gases are taken above the forest canopy at Harvard Forest, downwind of the New York City - Washington, D. C. corridor, every 25 minutes using a four-channel gas chromatographic system called FACTS (Forest and Atmosphere Chromatograph of Trace Species). The species measured are H2, CO, CH4, methyl chloroform (CH3CCl3), chloroform (CHCl3), carbon tetrachloride (CCl4), CFC-11 (CCl3F), CFC-12 (CCl2F2), CFC-113 (C2Cl3F3), halon-1211 (CBrClF2), perchlorethylene (C2Cl4), nitrous oxide (N2O), and sulfur hexafluoride (SF6). Observations began in January 1996 and are continuing.
Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition (2016-2017)
<p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations in the Indian and Pacific sectors of the Southern Ocean from the Antarctic Circumnavigation Expedition, 2016-2017.</p> <p>Dissolved trace metal (Fe, Ni, Cu, Zn, Cd, Pb) concentrations measured on seawater samples from the Southern Ocean. Samples were collected with a trace metal clean rosette system to a maximum depth of 1000 m during Legs 1 and 2 of the Antarctic Circumnavigation Expedition (ACE), 2016-2017. Samples were filtered through Akropak Supor filters (0.2 um) in a class 100 clean container, acidified to pH ≤ 2 and stored until analysis (>6 months). Samples from Leg 1 (TMR Casts 3-7) were collected during a transect from Cape Town, South Africa to Hobart, Australia. Samples from Leg 2 (TMR casts 8-20) were collected during a transect from Hobart, Australia to Punta Arenas, Chile. Data cover environments near subantarctic and Antarctic islands (TMR 3, 4, 13-15), in the Mertz Glacier Polynya (TMR 11-12) and near the Antarctic Peninsula (TMR 18), as well as meridional transects to and from the Antarctic continent (TMR 7-12, TMR 18-20).</p>
Fe chemical speciation collected using trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Fe chemical speciation of filtered seawater data are presented in this dataset, resulting from samples collected from a trace metal rosette on board the Antarctic Circumnavigation Expedition (ACE). During the austral summer of 2016/2017, seawater samples were collected from the Atlantic and Indian Ocean sectors of the Southern Ocean and dissolved Fe concentration, iron-binding organic ligands concentration and the conditional stability constant of Fe’ are presented here.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_fe_chemical_speciation.csv, data file, comma-separated values</li> <li>figure1.pdf, metadata, portable document format</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This Fe chemical speciation dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Hydrolysable carbohydrate data collected from the trace metal rosette in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Hydrolysable carbohydrate (referred to as TPZT from the analytical methodology used) is part of the labile pool of dissolved organic carbon that is excreted by most (micro)organisms or released by continental margins/sediments. It is a carbon source for heterotrophic bacteria. These carbohydrates could also potentially bind iron and act as an iron binding ligand.</p> <p>This data is used to explore the nature of iron ligands and relate to biological and chemical oceanography.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_hydrolysable_carbohydrates_tpzt_data.csv, data file, comma-separated values</li> <li>ace_hydrolysable_carbohydrates_tpzt_data_visual_summary.png, metadata, portable network graphics</li> <li>README.txt, metadata, text format</li> <li>data_file_header.txt, metadata, text format</li> <li>change_log.txt</li> </ul> <p><strong>Dataset license</strong></p> <p>This hydrolysable carbohydrate dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><strong>Change log</strong></p> <p>v1.1 - permissions changed to open access (CC BY 4.0 license) and small changes</p> <ul> <li>add license to README.txt</li> <li>format of data_file_header.txt</li> <li>add Frictionless Data schema files</li> </ul> <p>v1.0 - initial release of dataset</p>
PsPM-TC: SCR, ECG, EMG and respiration measurements in a discriminant trace fear conditioning task with visual CS and electrical US.
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG) and respiration measurements. Also included are CS and US information, keypress responses and keypress response times from 18 healthy unmedicated participants (8 males and 10 females aged 23.89+/-2.52 years) participating in a classical (Pavlovian) discriminant trace fear conditioning task. CS were a red and a blue rectangle presented for 3 seconds. US consisted of 0.5 s square electric pulses with 0.2 ms duration and 10 Hz frequency. SOA between the CS onset and US was 4 s. The ITI was randomly determined on each trial to be 7, 9, or 11 s.</p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Dynamic Traces from CEA Curie
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the dynamic trace utilizing <a href="https://www.cs.huji.ac.il/labs/parallel/workload/l_cea_curie/index.html">Curie's workload</a> carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per target fair share value that the user that we track its usage. Within, we have a file with the name indicating if the workflow is wrapped or not, the type, vertical or horizontal, and the instant of submission. This file is in <a href="https://www.cs.huji.ac.il/labs/parallel/workload/swf.html">SWF</a> format. Under the results folder, we have the same organizaion: each .trace is the raw file produced by the simulator. </p> <p>The workload log from the CEA Curie system was graciously provided by Joseph Emeras (<a href="mailto:Joseph.Emeras@imag.fr">Joseph.Emeras@imag.fr</a>).</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1977 (Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density.)
Title: Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density. <br>Species: Homo sapiens <br>Number of samples: 1116 <br>Number of named analytes: 41 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=46 <br>
Humic acid like concentration in seawater samples, collected from the trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Humic acid like concentration (abbreviated HA) measured with respect to the Suwannee River Fulvic acid standards (µmol SRFA equivalent per litre).</p> <p>Seawater samples were collected from trace metal rosette (TMR) deployments at different depths in the water column during the Antarctic Circumnavigation Expedition (ACE). Humic acid like data from legs 1 and 2, from TMR cast numbers 3 to 16, were analysed by electrochemistry following standard additions of Suwannee River Fulvic Acid (standard 1, IHSS). This data is to support iron ligands and iron bioavailability as well as hydrolysable saccharides (TPZT) data, also collected during ACE.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_humics_data.csv, data file, comma-separated values</li> <li>ace_humics_data_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This humics dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
A multi-level network tool to trace wasted water from farm to fork and backward
<p>NETFLOW - Network-based 13 Evaluation Tool for Food LOss and Waste<br>V 0.1</p> <p>####################################################################################################################################################</p> <p><br>Authors:<br>Francesco Semeria - Politecnico di Torino - francesco.semeria@polito.it<br>Marta Tuninetti - Politecnico di Torino<br>Luca Ridolfi - Politecnico di Torino</p> <p>####################################################################################################################################################</p> <p>CONTENT OF THIS ARCHIVE</p> <p>The listed files contain output data from the NETFLOW tool and assess the impact on water resources of food loss and waste (FLW) for wheat an its main derived products (flour, bran, pasta and bread).</p> <p>In particular, they quantify such impact offering two perspectives: <br> 1. supply-side, from FLW associated to food consumption backwards to the countries of production;<br> 2. utilisation-side, from the countries of production forward to the countries where FLW occurs.</p> <p>It should be noted that the two perspectives allow to identify two different aspects of the FLW issue.</p> <p><br>List of files:</p> <p>data_fig2_ita_supply_vw.xlsx = output data regarding the supply network of Italy.<br>data_fig3_usa_utilisation_vw.xlsx = output data regarding the utilisation network of the United States.<br>data_fig4_global_supply_vw.xlx = output data regarding the global supply network.</p> <p><br>Modelling scripts are currently available upon request.</p>
DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours
<p>Latest description of this data set: <a href="https://github.com/MRC-Harwell/cytometer/blob/main/DATA.md">Data.md at cytometer project</a></p> <pre># Publications related to the data The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005). The histology and mouse measures were generated as part of the Small et al. 2018 study: > Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018. The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper: > Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: [10.1101/2021.06.03.444997](https://www.biorxiv.org/content/10.1101/2021.06.03.444997v1.full). # Data protocols ## Histology and laboratory measures To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice. We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE). The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales. ## White adipose tissue segmentation For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively). Full segmentation of all whole slides was performed with script [klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py). In this case, the segmentation contours were grouped by tiles in the output AIDA annotation `.json` file (one contour per cell, one file per slide). Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation `.json` file with a single tile with script [klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py](https://github.com/MRC-Harwell/cytometer/blob/39358ed1d79df07d1d522b98728c7efd745513f7/scripts/klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py) (one contour per cell, one file per slide). # List of directories and files ## Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433) ### `deepcytometer_pipeline_v8.zip` (60.6 MB) Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper. There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation * `klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) * `klf14_b6ntac_exp_0094_generate_extra_training_images.pickle`: training dataset description * **'file_list'**: list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation * **'idx_test'**: 10 lists with file indices for testing in 10-fold cross-validation * **'idx_train'**: 10 lists with file indices for training in 10-fold cross-validation * **'fold_seed'**: seed number used for the random number generator to assign file indices to folds * `klf14_b6ntac_exp_0098_filename_area2quantile.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0098_full_slide_size_analysis_v7.py` using the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation. * `klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz`: quantile colour maps calculated in `klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py` using the whole Klf14 data set with v8 of the pipeline, and used in later experiments. * `klf14_training_colour_histogram.npz`: statistics from Klf14 histology images to be used in colour correction * **'xbins_edge'**, **'xbins'**: edges and centres of the bins used for histogram calculations * **'hist_r_q1'**, **'hist_r_q2'**, **'hist_r_q3'** * **'hist_g_q1'**, **'hist_g_q2'**, **'hist_g_q3'** * **'hist_b_q1'**, **'hist_b_q2'**, **'hist_b_q3'**: density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram * **'mode_r'**, **'mode_g'**, **'mode_b'**: modes for RGB channels (this corresponds to the most typical background colour in the histology images) * **'mean_l'**, **'mean_a'**, **'mean_b'**: mean intensity for L*a*b channels of the image * **'std_l'**, **'std_a'**, **'std_b'**: intensity standard deviations for L*a*b channels of the image * `klf14_exp_0112_training_colour_histogram.npz`: other statistics from Klf14 histology images to be used in colour correction * **'p'**: vector of quantile values used in ECDF calculations * **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'**: all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte * **'f_ecdf_to_val_r_klf14'**, **'f_ecdf_to_val_g_klf14'**, **'f_ecdf_to_val_b_klf14'**: linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction * **'mean_klf14'**, **'std_klf14'**: mean and standard deviation of the **'val_r_klf14'**, **'val_g_klf14'**, **'val_b_klf14'** vectors There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments * `klf14_b6ntac_exp_0101_cnn_dmap_model.h5`: Keras weights for the **EDT CNN** (Histology to Euclidean Distance Transform regression) * `klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5`: Keras weights for the **Correction CNN** (Segmentation Correction regression) * `klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5`: Keras weights for the **Contour CNN** (EDT to Contour detection) * `klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5`: Keras weights for the **Tissue CNN** (Pixel-wise tissue classifier) ### `histology.7z` (29.1 GB) 165 H&E histology whole slides from Hamamatsu scanner (`.ndpi`). ### `klf14.7z` (2.3 GB) Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments. * `klf14_b6ntac_meta_info.csv`: Klf14 mice metadata * **Animal Identifier**, **id:** unique ID for each mouse * **ko_parent:** heterozygous parent of origin for the KO allele (father, PAT or mother, MAT) * **sex:** female or male * **genotype:** wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het) * **BW:** body weight (g) * **SC:** subcutaneous depot weight (g) * **gWAT:** gonadal depot weight (g) * **Liver:** livel weight (g) * **cull_age:** age at time of culling (days) * **BW_alive:** body weight measured before culling * **BW_alive_date:** age at time of BW_alive measure * **mother:** unique ID for mouse's mother * **mother_genotype:** mouse's mother genotype * `klf14_b6ntac_training`: Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experiments * `ndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]`: * **ndpifile:** name of the whole slide file (e.g. `KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00`) * **row_YYYYYY:** Y-coordinate of the top-left corner of the sampling window, in pixels * **col_XXXXXX:** X-coordinate of the top-left corner of the sampling window, in pixels * **.tif:** TIFF file with the histology sampling window * **.xcf:** Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp) * **.svg:** SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file * `klf14_b6ntac_training_v2`: Same as `klf14_b6ntac_training`, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by others * `klf14_b6ntac_training_non_overlap`: Directory with intermediate images to train the networks. These images are generated by script [`klf14_b6ntac_training_non_overlap`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0077_generate_non_overlap_training_images.py) * `klf14_b6ntac_training_augmented`: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by script [`klf14_b6ntac_exp_0078_generate_augmented_training_images.py`](https://github.com/MRC-Harwell/cytometer/blob/main/scripts/klf14_b6ntac_exp_0078_generate_augmented_training_images.py) * `klf14_b6ntac_seg`: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g. `klf14_b6ntac_exp_0076_generate_training_images.py`). Of little interest for most users * `klf14_b6ntac_results`: Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users ## Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005) ### `aida_data_Klf14_v8_images.7z` (16.9 GB) Histology images converted to DeepZoom so that they can be visualised with [AIDA](https://github.com/alanaberdeen/AIDA). To use this, decompress this file and put the resulting `images` directory in your `AIDA/dist/data/` directory. ### `aida_data_Klf14_v8_annotations.7z` (18 GB) White adipocyte segmentations in AIDA annotation `.json` files (one contour per cell, one file per whole slide). Each slide has the following files: * `SLIDENAME.json`: Soft link to the annotations file that we want to associate to slide `SLIDENAME.ndpi`, e.g. `SLIDENAME` = `KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06` * `SLIDENAME.lock`: Empty file used to tell the pipeline that `SLIDENAME.ndpi` has already been processed or is being currently processed * `SLIDENAME_coarse_mask.npz`: File with the coarse tissue segmentation of `SLIDENAME.ndpi` and the internal state of the pipeline (execution times, steps, etc) * `SLIDENAME_exp_0106_auto.json`: Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_auto_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tile * `SLIDENAME_exp_0106_corrected.json`: Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed in * `SLIDENAME_exp_0106_corrected_aggregated.json`: Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile To use this, decompress this file and put the resulting `annotations` directory in your `AIDA/dist/data/` directory. </pre>
Geotagged Digital Traces
<p>This dataset, divided into files by city, contains geotagged digital traces collected from different social media platforms, detailed below.</p> <p>• Tweets - Cheng et al. [1]</p> <p>• Gowalla [2]</p> <p>• Tweets - Lamsal [3]</p> <p>• YELP[4]</p> <p>• Tweets - Kejriwal et al. [5]</p> <p>• Geotagged Tweets [6]</p> <p>• UrbanActivity, [7]</p> <p>• Brightkite [8]</p> <p>• Weeplaces [8]</p> <p>• Flickr [9]</p> <p>• Foursquare [10]</p> <p> </p> <p>Each file is named according to the city to which the digital traces were associated and contains the columns:</p> <ul> <li>Source: contains the name of the source platform</li> <li>Event_date: contains the date associated with the digital trace</li> <li>Lat: latitude of the digital trace</li> <li>Lng: length of the digital trace</li> </ul> <p>The definition of city/town used is provided by Simplemaps [11], which considers a city/town any inhabited place as determined by U.S. government agencies. The location of cities and their respective centers were obtained from the World Cities Database provided by the same company.</p> <p>A specific group of these cities was utilized for the research presented in the article submitted to Sensors Journal:</p> <p>Muñoz-Cancino, R., Rios, S. A., & Graña, M. (2023). Clustering cities over features extracted from multiple virtual sensors measuring micro-level activity patterns allows to discriminate large-scale city characteristics. Sensors, Under Review. </p> <p>Comprehensive guidelines and the selection criteria can be found in the abovementioned article.</p> <p> </p> <p> </p> <p>References</p> <p>[1] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: A content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM '10, page 759{768, New York, NY, USA, 2010. Association for Computing Machinery.<br> [2] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '11, page 1082{1090, New York, NY, USA, 2011. Association for Computing Machinery.<br> [3] Yunhe Feng and Wenjun Zhou. Is working from home the new norm? an observational study based on a large geo-tagged covid-19 twitter dataset, 2020.<br> [4] Yelp Inc. Yelp Open Dataset, 2021. Retrieved from https://www.yelp.com/dataset. Accessed October 26, 2021.<br> [5] Mayank Kejriwal and Sara Melotte. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas, January 2021.<br> [6] Rabindra Lamsal. Design and analysis of a large-scale covid-19 tweets dataset. Applied Intelligence, 51(5):2790{2804, 2021.<br> [7] Geraud Le Falher, Aristides Gionis, and Michael Mathioudakis. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In 9th AAAI Conference on Web and Social Media - ICWSM 2015, Oxford, United Kingdom, May 2015.<br> [8] Yong Liu, WeiWei, Aixin Sun, and Chunyan Miao. Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, page 739{748, New York, NY,USA, 2014. Association for Computing Machinery.<br> [9] Hatem Mousselly-Sergieh, Daniel Watzinger, Bastian Huber, Mario Doller, Elood Egyed-Zsigmond, and Harald Kosch. World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging. In Proceedings of the 5th ACM Multimedia Systems Conference, MMSys '14, page 47{52, New York, NY, USA, 2014. Association for Computing Machinery.<br> [10] Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129{142, 2015.<br> [11] Simple Maps. Basic World Cities Database, 2021. Retrieved from https://simplemaps.com/data/world-cities. Accessed September 3, 2021.</p>
Dissolved trace element concentration profiles of micronutrients (Mn, Ni, Cu, Zn, Co) and contaminants (Cd, Pb) in seawater from discrete bottle samples from CCE Process Cruises in the California Current System, 2021 - 2025 (ongoing).
Dissolved trace element is sampled from the trace metal clean rosette. The sample is collected by filtering seawater through a 0.2µm PES filter. The seawater sample is then acidified to pH~1.8 using ultra clean hydrochloric acid and subsequently analyzed using sector-field inductively coupled plasma-mass spectrometry, scanning in low and medium resolution, with either standard curve or isotope dilution methods. The samples are used to develop a description of the distribution of dissolved trace elements in the CCE region.
Trace Gas Fluxes on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1991 to 2019)
Dataset Abstract Trace gases (nitrous oxide, methane, and carbon dioxide) have been measured on the LTER Main Site since 1991 and on Successional and Forest sites since 1993. Trace gas fluxes are measured twice monthly or monthly until the ground freezes using permanently-installed, in-situ static chambers. CH4 and N2O are analyzed with gas-chromatography and CO2 with an infrared gas analyzer. Soil moisture and temperature are measured during sampling. original data source http://lter.kbs.msu.edu/datasets/16
Canopy Trimming Experiment (CTE) trace gases
This data set provides the monthly trace effluxes measured across the soil-atmosphere interface from five soil surface chambers in all the CTE plots. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
RECAP Artificial Data Traces
<p>The objective of the work package "Data Collection, Visualization and Analysis" of RECAP is to provide the necessary tools for managing and refining the data needed for the rest of the work packages. This includes the collection as well as the generation of data.</p> <p>Within this work package, the task of Artificial Workload Generation is responsible for the generation of a collection of datasets with artificial workloads, that complement the real data traces collected from industrial partners. Moreover, because publicly available workload data is scarce we provide the data as public data sets.</p> <p>This document is a companion report to deliverable which is of type “dataset”. The aim of the report is to describe the collection of datasets that constitute D5.3 and the mathematical techniques (structural time series models, generative adversarial networks, and workload based on traffic propagation) by which one can artificially generate and/or augment such datasets.</p> <p>The datasets described include real data traces collected by industrial partners and artificial data traces generated by the use of statistical models and neural networks. Each published data set can be used by the scientific and industrial community as a starting point for the modelling and experimental validation of distributed edge and cloud applications, facilitating the repeatability of the results.</p> <p> </p>
Long-Term Tracing of Indoor Solar Harvesting
<p><strong>Dataset Information</strong></p> <p>This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland.</p> <p>The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure.</p> <p>The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets:</p> <ul> <li><code>V_in</code> - Converter input/solar panel output voltage, in volt</li> <li><code>I_in</code> - Converter input/solar panel output current, in ampere</li> <li><code>V_bat</code> - Battery voltage (emulated through circuit), in volt</li> <li><code>I_bat</code> - Net Battery current, in/out flowing current, in ampere</li> <li><code>Ev_left</code> - Illuminance left of solar panel, in lux</li> <li><code>Ev_right</code> - Illuminance left of solar panel, in lux</li> <li><code>P_amb</code> - Ambient air pressure, in pascal</li> <li><code>RH_amb</code> - Ambient relative humidity, unit-less between 0 and 1</li> <li><code>T_amb</code> - Ambient temperature, in centigrade Celsius</li> </ul> <p>The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file <code>abstract.pdf</code>.</p> <blockquote> <p>L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019.</p> </blockquote> <p><strong>Folder Structure and Files</strong></p> <ul> <li><code>processed/</code> - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position <code>posXX</code> and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named <code>yyyy_mm_processed.tar</code>, where <code>yyyy</code> and <code>mm</code> represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>raw/</code> - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named <code>yyyy_mm_raw.tar</code>, where <code>yyyy</code> and <code>mm</code>represent the year and month the data was published. A separate file lists the exact content of each archive (see below).</li> <li><code>LICENSE</code> - License information for the dataset.</li> <li><code>README.md</code> - The README file containing this information.</li> <li><code>abstract.pdf</code> - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it.</li> <li><code>raw_import.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_import.ipynb">open in nbviewer</a>] - Jupyter Python notebook to import, merge, and filter the raw dataset from the <code>raw/</code> folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the <code>processed/</code>folder.</li> <li><code>raw_preview.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/raw_preview.ipynb">open in nbviewer</a>] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions.</li> <li><code>processing_python.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_python.ipynb">open in nbviewer</a>] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract.</li> <li><code>processing_r.ipynb</code> [<a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3715472/files/processing_r.ipynb">open in nbviewer</a>] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level.</li> </ul> <p><strong>Dataset File Lists</strong></p> <p><em>Processed Dataset Files</em></p> <p>The list of the processed datasets included in the <code>yyyy_mm_processed.tar</code> archive is provided in <code>yyyy_mm_processed.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><em>Raw Dataset Files</em></p> <p>A list of the raw measurement files included in the <code>yyyy_mm_raw.tar</code> archive(s) is provided in <code>yyyy_mm_raw.files.md</code>. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums.</p> <p><strong>Dataset Revisions</strong></p> <p><em>v1.0 (2019-08-03)</em></p> <p>Initial release.<br> Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are <code>2019_08_raw.tar</code> and <code>2019_08_processed.tar</code>.<br> For position <em>pos06</em>, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file <code>indoor1_p27.rld</code>).</p> <p><em>v1.1 (2019-09-09)</em></p> <p>Revision of the processed dataset v1.0 and addition of the final dataset abstract.<br> Updated processing scripts reduce the timestamp drift in the processed dataset, the archive <code>2019_08_processed.tar</code> has been replaced.<br> For position <em>pos06</em>, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (<code>indoor1_p27.rld</code> data inconsistency).</p> <p><em>v2.0 (2020-03-20)</em></p> <p>Addition of new data.<br> Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are <code>2020_03_raw.tar</code> and <code>2020_03_processed.tar</code>.</p> <p><strong>Dataset Authors, Copyright and License</strong></p> <ul> <li>Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele</li> <li>Contact: Lukas Sigrist (<a href="mailto:lukas.sigrist@tik.ee.ethz.ch">lukas.sigrist@tik.ee.ethz.ch</a>)</li> <li>Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group</li> <li>License: Creative Commons Attribution 4.0 International License (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)</li> </ul> <p><strong>References</strong></p> <p>[1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. <em>Measurement and validation of energy harvesting IoT devices.</em> In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.</p> <p>[2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, <a href="https://rocketlogger.ethz.ch/">https://rocketlogger.ethz.ch/</a>.</p> <p>[3] L. Sigrist. <em>Solar Harvesting and Ambient Tracing Platform</em>, 2019. <a href="https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing">https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing</a></p>
Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations
<p>This repository contains .npy files, loaded like:</p> <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> <p>which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations <a href="https://github.com/jmackereth/galactic-assembly-art.git">(explored in this repository)</a>.</p> <p>the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).</p>
Coordinates tracing 2D outlines of beaks (birds and squid)
<p>Two-dimensional coordinates for lines traced onto images of beaks.</p> <ul> <li>This is a .zip archive xy coordinates (250 files, .txt); and a list of specimen names (1 file, .csv).</li> <li>All images traced in FIJI.</li> <li>For each specimen, there is a trace of the beak rostrum and a separate trace of the beak bite surface.</li> <li>Each trace file should be a list of xy coordinates that ends at the beak tip. This must be checked/verified/corrected for all files before running any analyses! I recommend visual inspection by plotting each beak dataset as a scatterplot in a color spectrum (rainbow, etc.).</li> <li>These were traced over pixel images, so each file has a different number of xy coordinates (depending on the pixel resolution/image size that was traced).</li> <li>All bird specimen images were downloaded from Phenome10k.org</li> <li>All cephalopod specimens were traced from images published in: <ul> <li>Xavier, J. C. & Cherel, Y. 2009 Cephalopod beak guide for the Southern Ocean. British Antarctic Survey.</li> </ul> </li> </ul>
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.