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The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma
<p>The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass Cytometry<sup>TM</sup> (IMC<sup>TM</sup>) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.</p>
Ring-shear experiments with strain markers
<p><strong>Experiment overview</strong></p> <table> <caption> </caption> <thead> <tr> <th scope="col">ID</th> <th scope="col">Shearing velocity [mm/min]</th> <th scope="col">Effective normal stress [kPa]</th> </tr> </thead> <tbody> <tr> <td>mdt4</td> <td>7.00</td> <td>6</td> </tr> <tr> <td>mdt5</td> <td>0.70</td> <td>6</td> </tr> <tr> <td>mdt6</td> <td>0.07</td> <td>6</td> </tr> <tr> <td>mdt7</td> <td>7.00</td> <td>50</td> </tr> <tr> <td>mdt8</td> <td>0.70</td> <td>50</td> </tr> <tr> <td>mdt9</td> <td>0.07</td> <td>50</td> </tr> </tbody> </table> <p><br> <strong>Apparatus and material</strong></p> <p>These experiments were conducted in a ring-shear apparatus at the Department of Geoscience, Aarhus University, with a sample chamber volume of 14,48 l. The till was Mid-Danish Till deposited at the Last Glacial Maximum, and sampled at Knud Strand, Denmark. All shearing commenced under water-saturated conditions, and I used BB-gun pellets as strain markers.</p> <p>Please see the following publication for more technical details on the ring-shear apparaturs:</p> <blockquote> <p>Damsgaard, A., D. L. Egholm, J. A. Piotrowski, S. Tulaczyk, N. K. Larsen, and K. Tylmann (2013), Discrete element modeling of subglacial sediment deformation, J. Geophys. Res. Earth Surf., 118, 2230–2242, doi:10.1002/2013JF002830.</p> </blockquote> <p><br> <strong>Data files and format</strong></p> <p><em>Sensor data during shear</em></p> <p>These data files are named in the style "<ID>-stress-shear.tsv", where<br> ID refers to the experiment ID seen in the table above.</p> <p>The values are tab-separated, with the following columns and units:</p> <ol> <li>Shearing distance [mm]</li> <li>Normal stress [kPa]</li> <li>Shear stress [kPa]</li> <li>Dilation [mm]</li> </ol> <p>Important!: The shear stress readings are unreliable due to sensor failure.</p> <p><em>Strain marker data</em></p> <p>These data files are named in the style "<ID>-shear-disp.tsv". The values are tab-separated, with the following columns and units:</p> <ol> <li>Marker position along shear direction [mm]</li> <li>Marker depth [mm]</li> </ol> <p><br> <strong>Author and funding</strong></p> <p>Anders Damsgaard <anders@adamsgaard.dk>, Department of Geoscience, Aarhus University</p> <p>This dataset is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 897967.</p>
EO-based area monitoring markers computed over the Lithuanian pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2021 until 26-07-2021 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the two subsequent geopackage files, (i) the <em>"test_area_markers_summary.gpkg"</em> and (ii) <em>"crop_marker_summary_full_country.gpkg", which</em> were computed for the Lithuanian pilot region<em>. </em> Descriptions are given below.</p> <p><strong>Test area markers dataset: </strong>There is a total of 59995 FOIs in the test areas. Out of these, markers are computed on **54809** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><em><strong>Description of the information contained in the corresponding </strong></em><strong><em>"test area markers"</em> dataset</strong></caption> <thead> <tr> <th scope="col">Attribute Table name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID </td> <td>Reference ID of the polygon </td> </tr> <tr> <td>LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>N_VALID_OBS</td> <td>Number of valid Sentinel-2 observations used for computing the markers</td> </tr> <tr> <td>HOMOGENEITY_PROBA</td> <td>The probability that the FOI is homogeneous</td> </tr> <tr> <td>N_MW_OBS_MAY_OCT</td> <td>Number of detected mowing observations between May and October</td> </tr> <tr> <td>N_BS_OBS_APR_OCT</td> <td>Number of detected mowing observations between April and October</td> </tr> <tr> <td>SIMILARITY_SCORE</td> <td>The normalised ([0, 100]) representation of the χ<sup>2</sup> value of the NDVI time-series difference between the target FOI and the neighbouring FOIs</td> </tr> <tr> <td>DISTANCE_SCORE</td> <td>The normalised ([0, 100]) representation of the Median Euclidean distance between the NDVI time-series of the target FOI and the neighbouring FOIs having assumed crop type declared</td> </tr> <tr> <td>MEAN_NDVI</td> <td>Μean NDVI value of all valid observations of the FOI</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>Predicted crop group</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudo-probability of the crop group prediction</td> </tr> </tbody> </table> <p><strong>Crop marker full country dataset: </strong> There is a total of **1127455** FOIs for the full country GSAA dataset. Out of these, markers are computed on **1042671** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><em><strong>Description of the information contained in the corresponding </strong></em><strong><em>"crop marker full country"</em> dataset</strong></caption> <thead> <tr> <th scope="col">Attribute Table name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID</td> <td>Reference ID of the polygon</td> </tr> <tr> <td>LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>Predicted crop group</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudo-probability of the crop group prediction </td> </tr> </tbody> </table>
EO-based area monitoring markers computed over the Cypriotic pilot region (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-08-2020 until 05-10-2021 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry really consists of a single crop or if there are multiple things growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the geopackage file <em>"markers_summary.gpkg", which</em> was computed for the Cypriotic pilot region. Descriptions are given below.</p> <p><strong>Markers summary dataset</strong></p> <table> <caption><em><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></em></caption> <thead> <tr> <th scope="col">Attribute name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>POLY_ID</td> <td>Reference ID of the polygon </td> </tr> <tr> <td>PLOT_ID_CROP</td> <td>The reference ID of the FOI </td> </tr> <tr> <td>PLOT_NAME</td> <td>The reference name of the FOI </td> </tr> <tr> <td>APPLICANT_NUMBER</td> <td>The ID of the FOI applicant</td> </tr> <tr> <td>LABEL</td> <td>Crop identifier column, based on `CROP_CODE` column in the GSAA dataset.</td> </tr> <tr> <td>CROP_DESC</td> <td>Crop name in Greek (Original crop name) </td> </tr> <tr> <td>CROP_ENG</td> <td>The English name of the crop on the FOI</td> </tr> <tr> <td>LAND_USE</td> <td> <p><strong>Land use types:</strong> 1-ARABLE LAND; 2-PERMANENT CROPS; 3-GRASSLAND (PERMANENT PASTURE); 5-AFFORESTED LAND (NON-AGRICULTURAL); 6-GREENHOUSE); 71-NON-ELIGIBLE (ABANDONED AT LEAST 2 YEARS);72 - NON-ELIGIBLE (FOREST/LOW WILD UNWANTED VEGETATION); 73NON-ELIGIBLE (NON PLANTED/BARREN/ROCKY); 81 - BUILT-UP (BUILDING/WAREHOUSE - MAN CONSTRUCTED); 82 - BUILT-UP (PARK/HOUSE YARD)</p> </td> </tr> <tr> <td>N_VALID_OBS</td> <td>Number of valid Sentinel-2 observations used for computing the markers</td> </tr> <tr> <td>HOMOGENEITY_PROBA</td> <td>The probability that the FOI is homogeneous</td> </tr> <tr> <td>MEAN_NDVI</td> <td>Mean NDVI value of all valid observations of the FOI</td> </tr> <tr> <td> N_MW_OBS_AUG20_OCT21</td> <td>Number of detected mowing observations between August 2020 and October 2021</td> </tr> <tr> <td>N_BS_OBS_AUG20_OCT21</td> <td>Number of detected mowing observations between August 2020 and October 2021</td> </tr> <tr> <td>BS_COUNT_MAR_START_MAR_END</td> <td>Number of bare-soil observations between March 1 2021 and April 1 2021</td> </tr> <tr> <td>BS_COUNT_MAR_START_APR_MID</td> <td>Number of bare-soil observations between March 1 2021 and April 15 2021</td> </tr> <tr> <td>BS_COUNT_MAR_APR</td> <td>Number of bare-soil observations between March 1 2021 and May 1 2021</td> </tr> <tr> <td>SIMILARITY_SCORE</td> <td>The normalised ([0, 100]) representation of the χ<sup>2</sup> value of the NDVI time-series difference between the target FOI and the neighboring FOIs</td> </tr> <tr> <td>DISTANCE_SCORE</td> <td>The normalised ([0, 100]) representation of the Median Euclidean distance between the NDVI time-series of the target FOI and the neighboring FOIs having assumed crop type declared</td> </tr> <tr> <td>PREDICTED_LAND_GROUP</td> <td>The FOI label as predicted by the land group model using crop groupings based on land use.</td> </tr> <tr> <td>LAND_GROUP_PREDICTION_SCORE</td> <td>The pseudoprobability of the crop-group prediction as assigned by the land-group model using crop groupings based on land use. A score close to 1 indicates that the model is very confident in the prediction a score close to 0 indicates that the model is not confident in the prediction.</td> </tr> <tr> <td>PREDICTED_CROP</td> <td>The FOI label as predicted by the crop group model using crop groupings proposed by CAPO.</td> </tr> <tr> <td>CROP_PREDICTION_SCORE</td> <td>The pseudoprobability of the crop-group prediction as it is assigned by the crop-group model using crop groupings proposed by PA. A score close to 1 indicates that the model is very confident in the prediction a score close to 0 indicates that the model is not confident in the prediction.</td> </tr> <tr> <td>PREDICTED_CROP_V39</td> <td>The FOI label as predicted by the crop group model using crop groupings to 39 groups.</td> </tr> <tr> <td>CROP_PREDICTION_SCORE_V39</td> <td>The pseudoprobability of the crop-group prediction as assigned by the crop-group model using crop groupings to 39 groups.</td> </tr> <tr> <td>PREDICTED_CROP_V41</td> <td>The FOI label as predicted by the crop group model using crop groupings to 41 groups</td> </tr> <tr> <td>CROP_PREDICTION_SCORE_V41</td> <td>The pseudoprobability of the crop-group prediction as assigned by the crop-group model using crop groupings to 41 groups.</td> </tr> </tbody> </table> <p><br> </p>
Super-resolution EO-based area monitoring markers computed over the Lithuanian pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over defined pilot areas over the Lithuanian pilot country, enhanced by features raised from the Super-resolution models. It involved the implementation of matching marking and data fusion deep learning algorithms, which attempt to support the extraction of useful information from highly variable inputs. This will further allow the distinction of landscape features, which would otherwise not be available in initially acquired Sentinel-2 data. The use of VHR data (Copernicus Contributing Missions) in combination with drone imagery will enhance the super-resolution modelling capabilities, enabling the augmentation of the training dataset (spatio-temporal scale) and subsequently leading to increased model performance.<br> The goal was to enhance the outputs of the area monitoring markers and especially in the monitoring of small (i.e. 100m<sup>2</sup>), narrow and elongated parcels. </p> <p>For the needs of DIONE, the aforementioned data were explored and the following area-based monitoring markers were calculated from 01-01-2022 until 01-08-2022 providing tailored information for the needs of the National Paying Agency of Lithuania. </p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of one geopackage file, the "S2SR-study-geopackage.gpkg"<em>, which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <p><strong>Super-resolution Markers dataset: </strong>Markers were computed for 16872 FOIs that contain less than 1 Sentinel-2 pixel, using signals from 2022-01-01 until 2022-08-01. </p> <table> <caption><strong>Description of the information contained in the corresponding "Super-Resolution markers" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name </th> <th scope="col">Description </th> </tr> </thead> <tbody> <tr> <td>CROP_LABEL</td> <td>Reference ID of the polygon </td> </tr> <tr> <td>POLY_ID </td> <td>Declared crop group</td> </tr> <tr> <td>crop_group_prediction_1_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_1_classification_score</td> <td>The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>crop_group_prediction_2_classification</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>crop_group_prediction_2_classification_score</td> <td>The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>distance_classification</td> <td>Most similar crops according to the distance marker</td> </tr> <tr> <td>distance_classification_score</td> <td>Distance marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> <tr> <td>mowing_event_count</td> <td>Number of detected mowing events in the observation period</td> </tr> <tr> <td>similarity_classification</td> <td>Most similar crops according to similarity marker</td> </tr> <tr> <td>similarity_classification_score</td> <td>Similarity marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim</td> </tr> </tbody> </table>
EO-based area monitoring markers computed over the Cypriotic pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-08-2021 until 03-09-2022 and provides tailored information on the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of the geopackage file <em>"markers_summary.gpkg", </em>which was computed for the Cypriotic pilot region. Descriptions are given below.</p> <p><strong>Markers summary dataset</strong></p> <table> <caption><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td> POLY_ID </td> <td>Unique polygon identifier</td> </tr> <tr> <td>OBS_ALL</td> <td>Number of all available observations</td> </tr> <tr> <td>OBS_VALID</td> <td>Number of all valid observations</td> </tr> <tr> <td>CROP_CODE</td> <td>Crop identifier</td> </tr> <tr> <td>N_PIXEL</td> <td>Number of S2 pixels within FOI</td> </tr> <tr> <td>PLOTIDCROP</td> <td>PLOT_ID_CROP</td> </tr> <tr> <td>BS_APR_END</td> <td>Number of bare-soil observations between 2022-03-01 and 2022-04-30 </td> </tr> <tr> <td>BS_FEB_JUN</td> <td>Number of bare-soil observations between 2022-02-01 and 2022-06-30</td> </tr> <tr> <td>BS_JAN_APR</td> <td>Number of bare-soil observations between 2022-01-01 and 2022-04-30</td> </tr> <tr> <td>BS_JAN_MAR</td> <td>Number of bare-soil observations between 2022-01-01 and 2022-03-31</td> </tr> <tr> <td>BS_OCT_DEC</td> <td>Number of bare-soil observations between 2021-10-01 and 2021-12-31</td> </tr> <tr> <td>BS_OCT_SEP</td> <td>Number of bare-soil observations between 2021-10-01 and 2022-09-30</td> </tr> <tr> <td>P_CROP</td> <td>The FOI label as predicted by the crop group model</td> </tr> <tr> <td>CR_P_SCORE</td> <td>The pseudoprobability of the crop-group prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>D_GROUP</td> <td>Declared crop group</td> </tr> <tr> <td>DIST_CROP</td> <td>Most similar crop according to the distance marker</td> </tr> <tr> <td>DIST_SC00</td> <td>Distance marker score of a most similar crop</td> </tr> <tr> <td>DIST_SCORE</td> <td>Distance marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> <tr> <td>HOM_CLASS</td> <td>Homogeneous/Heterogeneous</td> </tr> <tr> <td>HOM_SCORE</td> <td>Homogeneity probability assigned by the homogeneity marker</td> </tr> <tr> <td>P_LAND_GR</td> <td>The FOI label as predicted by the land group model using crop groupings based on land use</td> </tr> <tr> <td>LGRP_SCORE</td> <td>The pseudoprobability of the crop-group prediction. A score close to 1 indicates that the model is very confident in the prediction.</td> </tr> <tr> <td>D_LAND</td> <td>Declared land group</td> </tr> <tr> <td>M_NDVI_A_J</td> <td>Mean NDVI value in interval 2022-04-01 till 2022-07-31</td> </tr> <tr> <td>M_NDVI_F_M</td> <td>Mean NDVI value in interval 2022-02-01 till 2022-03-31</td> </tr> <tr> <td>M_NDVI_J_A</td> <td>Mean NDVI value in interval 2022-01-01 till 2022-04-30 </td> </tr> <tr> <td>MW_FEB_JUN</td> <td>Number of mowing events between 2022-02-01 and 2022-06-30</td> </tr> <tr> <td>MW_OCT_SEP</td> <td>Number of mowing events between 2021-10-01 and 2022-09-30</td> </tr> <tr> <td>MW_ALL</td> <td>Number of mowing events in the observation period</td> </tr> <tr> <td>SIM_CROP</td> <td>Most similar crop according to similarity marker</td> </tr> <tr> <td>SIM_SC00</td> <td>Similarity marker score of a most similar crop</td> </tr> <tr> <td>SIM_SCORE</td> <td>Similarity marker score of a FOI when compared to nearby FOIs with the same claim. A value close to 100 indicates that a FOI is not similar to other FOIs with the same claim.</td> </tr> </tbody> </table> <p> </p>
EO-based area monitoring markers computed over the Lithuanian pilot region (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2022 until 04-07-2021 and provides tailored information for the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers.</p> <ol> <li> <p><strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI)</p> </li> <li> <p><strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation</p> </li> <li> <p><strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel</p> </li> <li> <p><strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest)</p> </li> <li> <p><strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims</p> </li> <li> <p><strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI</p> </li> <li> <p><strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI</p> </li> </ol> <p>This dataset is comprised of one geopackage file, the <em>"markers_summary.gpkg", which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below.</p> <p><strong>Markers summary dataset: </strong>There is a total of **1074460** FOIs for the complete country GSAA dataset. Out of these, markers are computed on **996028** FOIs that contain more than 1 Sentinel-2 pixel.</p> <table> <caption><strong>Description of the information contained in the corresponding "markers summary" dataset</strong></caption> <thead> <tr> <th scope="col">Attribute name </th> <th scope="col">Description </th> </tr> </thead> <tbody> <tr> <td>POLY_ID </td> <td>Reference ID of the polygon</td> </tr> <tr> <td>CROP_LABEL</td> <td>Declared crop group</td> </tr> <tr> <td>S2_all_observations_count</td> <td>Count of all observations </td> </tr> <tr> <td>S2_valid_observations_count</td> <td>Count of all valid observations</td> </tr> <tr> <td>S2_pixel_count</td> <td>Number of S2 pixels within FOI</td> </tr> <tr> <td>declared_as</td> <td>Declared crop group (v1)</td> </tr> <tr> <td>classification_score</td> <td>The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>classification </td> <td>The FOI label as predicted by the crop group (v1) model</td> </tr> <tr> <td>crop_declared_as_2</td> <td>Declared crop group (v2)</td> </tr> <tr> <td>crop_classification_score_2</td> <td>The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction</td> </tr> <tr> <td>crop_classification_2</td> <td>The FOI label as predicted by the crop group (v2) model</td> </tr> <tr> <td>mowing_event_count</td> <td>Number of mowing events detected</td> </tr> </tbody> </table>
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Database of Medieval Nubian Identity Markers (version 1.0)
<p>The Database of Medieval Nubian Identity Markers (DBMNIM) has been created in the framework of the project <em>IaM NUBIAN. Identity and Memory in Christian Nubia: A study on strategies of (self-)presentation and preservation of the past in medieval African society.</em></p> <p>The DBMNIM is a relational database which fits into a bigger structure of a database of written sources originating from the Middle Nile Valley in Middle Ages, under continuous development by myself. The basic component of this structure is the Database of Medieval Nubian Texts (DBMNT), available online at <a href="http://www.dbmnt.uw.edu.pl/">www.dbmnt.uw.edu.pl</a>, from which the DBMNIM draws the basic metadata for any given text included in the database.</p> <p>The DBMNIM has a three-level structure, represented by three interconnected tables: “Identity markers”, “IM Variants”, and “IM References”, and contains a variety of data that allow the recognition of identity(-ies) of persons occurring in the sources. For the time being, only textual identity markers are covered in the DBMNIM, but the database is designed to include also non-textual markers (graphic signs and representations, contextual information).</p> <p>The database has been designed in FileMaker Pro 16 and the upload includes the original database file in the .fmp12 format. However, in order to ensure a wide accessibility, the contents of the database has been exported into .xml, .csv, and .xlsx files. For detailed information on the structure and contents of the DBMNIM, see the attached DBMNIM_upload_documentation_1_0.pdf file.</p>
Patient reported outcome measures, load-induced blood marker kinetics, and ambulatory knee load in patients with medial compartment knee osteoarthritis
<p>The goal of this study was (i) to quantify the mechanoresponse of this array of potential blood markers for joint pathology (COMP, MMP-1, MMP-3, MMP-9, CPII, C2C, C2C/CPII, ADAMTS-4, PRG-4, IL-6 and resistin) to a walking stress test in patients with knee OA and to determine the correlation (ii) among the kinetics of these blood markers, (iii) with accumulated knee load during the walking stress, and (iv) with patient reported osteoarthritis outcome and QoL.</p> <p>The dataset includes 24 patients with knee osteoarthritis scheduled to receive high tibial osteotomy. All participants completed questionnaires, and a walking stress test with six blood samples analyzed using enzyme-linked immunosorbent assays for cartilage oligomeric matrix protein (COMP), matrix metalloproteinases (MMP)-1, -3, and -9, epitope resulting from cleavage of type II collagen by collagenases (C2C), type II procollagen (CPII), interleukin (IL)-6, proteoglycan (PRG)-4, A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-4, and resistin, and gait analysis. Joint load was computed from gait analysis data and musculoskeletal modelling in AnyBody Modeling System (AnyBody Technology A/S). Discrete loading parameters were extracted for each step using an inhouse algorithm written in Matlab.</p> <p>The detailed experimental protocol of the umbrella study has been described in Mündermann A, Vach W, Pagenstert G, Egloff C, Nüesch C. Assessing in vivo articular cartilage mechanosensitivity as outcome of high tibial osteotomy in patients with medial compartment osteoarthritis: Experimental protocol. Osteoarthr Cartil Open. 2020 Feb 24;2(2):100043. doi: 10.1016/j.ocarto.2020.100043. PMID: 36474590; PMCID: PMC9718245. The study is registered on clinicaltrials.gov (identifier NCT02622204). The method for computing joint loading has been described in detail in De Pieri E, Nüesch C, Pagenstert G, Viehweger E, Egloff C, Mündermann A. High tibial osteotomy effectively redistributes compressive knee loads during walking. J Orthop Res. 2022 Jun 22. doi: 10.1002/jor.25403. Epub ahead of print. PMID: 35730475.</p>
Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum
<p>Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species <em>Fagopyrum esculentum</em> and <em>F. tataricum</em> (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).</p>
Sediment Elevation Change (Feldspar Marker Horizon Method) from Northeastern Florida Bay (FCE) from 1996 to Present
The long term goal of the South Florida Water Management District's research program focuses on the following central objective: Regional processes mediated by water flow control population and ecosystem level dynamics at any location within the coastal Everglades landscape. This phenomenon is best exemplified in the dynamics of an estuarine oligohaline zone where fresh water draining phosphorus-limited Everglades marshes mixes with water from the more nitrogen-limited coastal ocean. Data in this data package are specifically to monitor soil elevation change relative to current sea level.
PIE LTER, marsh surface marker horizon measurements at each Surface Elevation Table (SET) site, Rowley, MA.
Marker horizon depth measurements at each Surface Elevation Table (SET) site. Marker horizons are commonly used in conjunction with the SET. Marker horizons measure vertical accretion which predominantly incorporates surface processes.
Short term accretion measured using marker horizon of feldspar at Space For Time locations.
This dataset provides a survey of short term accretion across the PIE LTER at both high and low marsh sites. A marker horizon plot was established and sampled annually to determine depth of accretion since deployment.
SBC LTER: Reef: Microsatellite markers for the giant kelp, Macrocystis pyrifera
These data were published as a technical note: Alberto, F., A. C. Whitmer, N. C. Coelho, M. Zippay, E. Varela-Alvarez, P. T. Raimondi, D. C. Reed and E. A. Serrão. 2009. Microsatellite markers for the giant kelp Macroystis pyrifera. Conservation Genetics. 10:1915-1917 doi: 10.1007/s10592-009-9853-9 Abstract: We report the isolation and characterization of 16 microsatellite loci to study the population genetics of the giant kelp, Macrocystis pyrifera. Markers were obtained by screening a genomic library enriched for microsatellite motifs. Of the 37 primer pairs defined, 16 amplified clean polymorphic microsatellites and are described. These loci identified a number of alleles ranging from three to forty (mean= 16.5, and gene diversity ranging from 0.469 to 0.930 (mean= 0.774). The isolation and characterization of these highly polymorphic markers will greatly benefit much needed studies on the molecular ecology of this important macroalga. These data report the isolation and characterization of 16 microsatellite loci to study the population genetics of the giant kelp, Macrocystis pyrifera. 180 samples of blade tissue were collected at Carpinteria Reef, Santa Barbara Channel, CA (USA) in July 2006. Sequence data are available from GenBank. These data were published as a Technical Note in the journal Conservation Genetics in 2009.
SBC LTER: Reef: Geospatial structure of microsatellite markers for the giant kelp, Macrocystis pyrifera, Santa Barbara CA, 2009
Data package includes data and R code for the examination of geospatial genetic structure (SGS) and simulation of inbreeding in giant kelp (Macrocystis pyrifera) from the Santa Barbara Channel, California. Data are reported for microsatellite markers from individual giant kelp plants from Carpinteria Reef, Mohawk Reef and Goleta Bay, collected during September 2009. Relative X and Y coordinates (meters) were recorded for each specimen sampled. R code consists of two scripts, a) to calculate mean number of alleles per locus, and observed and expected heterozygosity, and b) to simulate self-fertilization and sibling/cousin relationships. These data were presented in: Johansson, Mattias L, Raimondi, Peter T, Reed, Daniel C, Coelho, Nelson C, Serrão, Ester A, Alberto, Filipe A. In press. Looking into the black box: simulating the role of self-fertilization and mortality in the genetic structure of Macrocystis pyrifera. Molecular Ecology, 22:4842–4854. These data are also available from: Johansson ML, Raimondi PT, Reed DC, Coelho NC, Serrão EA, Alberto FA (2013) Data from: Looking into the black box: simulating the role of self-fertilization and mortality in the genetic structure of Macrocystis pyrifera. Dryad Digital Repository. doi:10.5061/dryad.s1b07.
Increased brain reactivity to gambling unavailability is a marker of problem gambling
Open the record for dataset details and reuse information.
Marker files for assessing Prochlorococcus genome completness
<p>Use these files with CheckM to assess Prochlorococcus genome completeness:</p> <p><em>pro-marker-checkm-refined.txt</em></p> <p>List of custom PFAM and TIGRFAM identifiers used to assess genome completeness in <em>Prochlorococcus</em> using checkM</p> <p><em>pro-marker-checkm-refined.ms</em></p> <p>CheckM marker file of custom PFAM and TIGRFAM identifiers used to assess genome completeness in <em>Prochlorococcus</em>.</p> <p><em>pro-marker-checkm-refined.hmm</em></p> <p>HMM file of custom Prochlorococcus markers for use with CheckM and hmmer3</p>
Data for: Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer
<p>The expression and regulation of matrisome genes - the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors - is of paramount importance for the many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.</p>
IMU and marker-based optical motion capture from a humanoid robot
<p>The motion capture contains walking trials from the lower body of the humanoid robot Reem-C from Pal Robotics (Barcelona, Spain). Seven IMUs were attached on the foot, lower leg, upper leg and pelvis segments. IMU data was collected at 100 Hz. Moreover, the robot motion was captured with a marker-based optical system (Qualisys AB, Göteborg, Sweden) at 150 Hz. The focus of the dataset was mainly walking. There are three trials, each with a length of about 6.5 minutes.<br>The dataset contains the definition of the skeleton (segment lengths and coordinate locations), the actual IMU readings and the pose or kinematics from the optical system.</p>
ScienceDex guides
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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.