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1,552 results for “Patches”
FixMe: An Incremental Lightweight Method for Vulnerability Data Collection for Security Patch Prediction
<div> <div>This repository has the FixMe dataset and the source code for extracting the new dataset. is a lightweight approach for collecting code patches based on analyzing the commits of various version control systems. The practical framework is designed to generate patches across a wide array of programming languages. This open-source tool streamlines the process of gathering vulnerability records from the Common Vulnerabilities and Exposures (CVE) database through an incremental approach. By embracing an incremental methodology, we expedite the acquisition of data, ensuring the inclusion of newly identified vulnerabilities and their corresponding patch pairs. Our methodology involves extracting security issues, obtaining vulnerability-fixing commits, and retrieving relevant source code from various projects. The extracted dataset by the FixMe tool supports for the automated patch prediction, automated program repair, commit classification, vulnerability prediction and more.</div> </div>
Baltimore forest patch point-sample overstory and understory vegetation, 2018-2020
Urban forest patches, including woodland interiors and bounding edge habitat, result from secondary succession and fragmentation of more extensive forested landscapes in the eastern USA. Management regimes, surrounding land use, and successional processes lead to distinct environments and contribute to local and regional heterogeneity. However, many woodlands are degraded due to frequent disturbance, aggressive exotic species, and heavy browsing, which stress canopies, reduce regeneration, and may reduce ecosystem services. Effective management requires rapid, repeatable assessment of forest composition, structure, and condition at the scale of local decision-making. We present and apply a protocol for characterizing urban woodlands that generates new insight into the status of urban woodlands and baseline data for change detection over time. Samples of overstory composition, ground cover, surface soil measurements, and the Schumacher Vine Encroachment Index were collected at 845 points across each of 47 patches across Baltimore, Maryland. Simple citywide summaries allowed characterization of Baltimore’s urban overstories as overwhelmingly native, though dominated by a range of successional conditions. By contrast, we found that ground layers were predominantly exotic, with abundant invasives or ruderal native species benefitting from disturbed conditions. Seven overstory types were distinguished, the majority under threat from aggressive vines. Most soils showed little evidence of compaction, but variable organic content. Distributed data allowed crosspatch comparison as well as within-patch analyses along edge-to-interior gradients. Species diversity, nativity, and overstory basal area all increased towards woodland interiors, whereas soil compaction and vine encroachment decreased. Structural and compositional shifts in both overstory and ground layer species revealed indicators of edge (15.2-18.7 m) and interior (> 41.5 m) conditions, as well as evidence of tran
Data and script for "On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"
<p>Data and R script necessary to replicate the results of Riva et al. 2024 ("On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"; minor revisions, Biological Conservation).</p>
Patch-burn grazing impacts forage resources in subtropical humid grazinglands
Subtropical humid grazing lands represent a large global land use and are important for livestock production, as well as supplying multiple ecosystem services. Patch-burn grazing (PBG) management is applied in temperate grazing lands to enhance environmental and economic sustainability; however, this management system has not been widely tested in subtropical humid grazing lands. The objective of this study was to determine how PBG affected forage resources, in comparison with the business-as usual full-burn (FB) management in both intensively managed pastures (IMP) and seminative (SN) pastures in subtropical humid grazing lands. We hypothesized that PBG management would create patch contrasts in forage quantity and nutritive value in both IMP and SN pastures, with a greater effect in SN pastures. A randomized block design experiment was established in 2017 with 16 pastures (16 ha each), 8 each in IMP and SN at Archbold Biological Station’s Buck Island Ranch in Florida. PBG management employed on IMP and SN resulted in creation of patch contrast in forage nutritive value and biomass metrics, and recent fire increased forage nutritive value. Residual standing biomass was significantly lower in burned patches of each year, creating heterogeneity within both pasture types under PBG. PBG increased digestible forage production in SN but not IMP pastures. These results suggest that PBG may be a useful management tool for enhancing forage nutritive value and creating patch contrast in both SN and IMP, but PBG does not necessarily increase production relative to FB management. The annual increase in tissue quality and digestible forage production in a PBG system as opposed to once every 3 yr in an FB system is an important consideration for ranchers. Economic impacts of PBG and FB management in the two different pasture types are discussed, and we compare and contrast results from subtropical humid grazing lands with continental temperate grazing lands.
Comparing the impacts of patch-burn grazing on vegetation in two northern tallgrass prairies
The management practice of patch-burn grazing varies grazing pressure across a site by rotating burn locations, thereby creating spatial heterogeneity in vegetation height and density (structure). Patch-burn grazing increases the range of habitats available for different wildlife species, but it may also unintentionally affect plant invasion and plant biodiversity. We evaluated the effects of patch-burn grazing on plant communities in two northern tallgrass prairies. Both dry-mesic prairie sites were in Minnesota, USA, on similar soils and undergoing invasion by the non-native, cool-season grass smooth brome (Bromus inermis). The sites had different cattle stocking rates and burning practices (3 or 5 burn units). We established 15-20 pairs of plots per site with a fence around one member of each pair. We measured vegetation structure, native and non-native plant richness, smooth brome frequency, and frequency-weighted mean coefficients of conservatism (mean C) over 5-6 years across two treatments: patch-burn grazing and burning-without-grazing. At the site with a lower stocking rate and more burn units, grazing promoted spatial heterogeneity by reducing vegetation structure 18-65 percentage points in some units and some years. In both treatments, native richness increased 15% over 6 years, but smooth brome frequency increased over 200%, suggesting that adjustments in management are needed to suppress smooth brome. At the site with a higher stocking rate and fewer burn units, grazing reduced vegetation structure 37-78 percentage points in all units and all years, but native richness was maintained over time. Grazing also increased non-native richness 38 percentage points and reduced mean C by 6 percentage points over 2 years. Smooth brome frequency increased 2% over 2 years in both treatments. Patch-burn grazing at this site may have increased richness of annual or biennial non-native plant species. At both sites, long-lived perennials may drive the resilience of nat
PBG02 Aboveground primary productivity within permanent and rotating grazing exclosures in the Patch-Burn Grazing Experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set includes measurement of aboveground net primary productivity based upon end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation within permanent (“ungrazed”) and rotating (“grazed) sections of grazing exclosures to determine long term effects of bison grazing on aboveground primary production. The permanent section of the exclosure is fixed, while the movable section is rotated every 6 years among the four possible compass coordinates relative to the fixed se
PBG03 Disk pasture meter measurements to estimate plant standing biomass in the Patch-Burn Grazing experiment at Konza Prairie
"PBG" datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This dataset includes both the annual calibration data for the disk pasture meter measurements (PBG031) and the actual disk pasture meter measurements in the PBG experiment (PBG032). Measurements were taken at a total of 64 transects (8 watersheds x 4 sites per watershed x 2 pasture meter transects per site). Each cattle-grazed watershed (designated as of May 2011 C3A, C3B, C3C, and C1A) and the four Shane cattle-grazed watersheds (C1B, C3SA, C3SB, and C3SC) includes 4 plant composition sampling transects (A-D). Pasture meter measurements were taken alo
PBG04 Reproductive effort of Big Bluestem, Indiangrass and Little Bluestem in the Patch-Burn Grazing Experiment at Konza Prairie
'PBG' datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. PBG041 includes data on flowering stem height (m) of three dominant prairie grasses: Andropogon gerardii (ANGE), Sorghastrum nutans (SONU), and Schizachyrium scoparium (ANSC). PBG042 includes flowering stem density (no. per sq. m) and mass (grams per sq. m) for the same grass species.
PBG05 Response of bird abundance to the Patch-Burn Grazing experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on variation in avian abundance, diversity, and nesting activity between patch-burned and uniformly-burned pastures at Konza Prairie Biological Station. Three watershed units (C3A, C3B, C3C) constitute 'patches' that are alternately burned in a 3-year rotation within a single, fenced pasture (i.e., patch-burn grazing). Two additional watersheds serve as controls: a grazed, annually/uniformly-burned watershed (C1A) and an ungrazed, annually/uniformly-burned watershed (1D). Eight, 300-m line transects were established in each watersh
PBG06 Cattle grazing and cattle performance in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on monitoring (1) the dynamics of cattle grazing on each of two sets of three pastures burned each year on a rotating basis and (2) cattle performance including cow weight gain, body condition, and reproductive performance and calf weight gains.
PBG07 Grasshopper species abundances in the Patch-Burn Grazing experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1(1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis.
PBG08 Grasshopper density survey in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring grasshopper density using ring count method (Onsager 1977*) at watersheds C03A, C03B, C03C, C01A, C3SA, C3SB, C3SC, and C01B. Grazing intensity is estimated (Joern 2005) at each site at time of density measurements to model how grasshopper populations respond to grazing.
PBG10 Soil physical and chemical characteristics in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring bulk density, soil organic matter, pH, cation exchange capacity, soil cations (Ca++, Mg++, Na+), phosphorous and total Kjeldahl nitrogen of soils at the vegetation transects in C3SA, C3SB, C3SC. C1SB, C3A, C3B, C3C, and C1A.
PBG11 Stream water chemistry for the Shane Creek drainage basin in the Patch-Burn Grazing experiment at Konza Prairie
PBG datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1 (1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis. This data set focuses on measuring Nitrate, ammonium, total N, soluble reactive P, total P, and dissolved organic C in four streams draining watersheds with 1 (N01B), 2 (N02B), 4 (N04D), and 20 (N20B) year target burn frequencies.
PBG01 Plant species composition in the Patch Burning-grazing Experiment at Konza Prairie
‘PBG’ datasets are associated with a long-term, large-scale study that is addressing the effects of fire-grazing interactions in the context of a Patch-Burn Grazing management system designed to promote grassland heterogeneity. Effects of patch-burn grazing management on plant and animal diversity and the nature and variety of wildlife habitat are being assessed in two replicate management units, each consisting of three pastures (watersheds) designated C03A/C03B/C03C and C3SA/C3SB/C3SC. In each patch-burn grazing unit, one watershed is burned and two that are left unburned in a given year. The burning treatments are rotated annually so that each pasture is burned every third year. Each patch-burn grazing unit is paired with an annually-burned pasture for comparison with traditional grazing systems (C01A and C1SB). All grazing units are stocked with cow/calf pairs from approximately 1 May until 1 Oct at a stocking density equal to 3.2 ha per cow/calf. To examine the impact of patch burning and grazing in all 8 units, we monitor changes in plant species composition, residual biomass, grassland bird populations, insect populations, small mammal populations, soil nutrients, and stream water quality1(1C3SA/C3SB/C3SC unit only). The KSU Department of Animal Science monitors cattle performance, including weight gain and body condition to assess the economic feasibility of using patch-burn management on a widespread basis.
Kelp metapopulations: Semi-annual time series of giant kelp patch area, biomass and fecundity in southern California, 1996 - 2006
These data describe the patch-scale canopy biomass and population fecundity of giant kelp, Macrocystis pyrifera, in southern California, USA, from 1996¬ to 2007. Biomass of the surface canopy was estimated using diver-calibrated Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus satellite imagery. Fecundity was estimated from canopy biomass pixel data using a seasonally-adjusted relationship between the diver-measured density of giant kelp spore-bearing tissue and the Landsat estimate of canopy biomass density using data collected across five years at the San Clemente Artificial Reef, located offshore of San Clemente, California, USA. Landsat pixel-scale estimates of giant kelp biomass and fecundity were summed across space for each giant kelp patch and averaged across time separately with two semesters each year (January–June and July–December). The location and area of each giant kelp patch are also provided. These data were described in <ulink url="http://dx.doi.org/10.1890/15-0283.1">Castorani, M. C., D. C. Reed, F. Alberto, T. W. Bell, R. D. Simons, K. C. Cavanaugh, D. A. Siegel and P. T. Raimondi. Connectivity structures local populations dynamics: a long-term empirical test in a large metapopulation system. Ecology. DOI: 10.1890/15-0283.1</ulink> These data are part of the NSF collaborative project: The effect of inbreeding on metapopulation dynamics of the giant kelp, Macrocystis pyrifera (funded wholly or part by NSF Awards OCE-1233283, 1233288, 1233839).
Kelp metapopulations: Semi-annual time series of spore dispersal times among giant kelp patches in southern California, 1996 - 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among patches in southern California, USA, from 1996 to 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations among patches were estimated for 6-month periods (January - June and July - Dececember, 1996 - 2006) using minimum mean transit times connecting source and destination connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS). Minimum transport times between giant kelp patches were assumed to be proportional to minimum transport times between ROMS cells and the alongshore distance between giant kelp patches
Efficient Geometric Algorithms Using Osculating Toroidal Patches
<p>The four objects used for Hausdorff distance computation have been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel ( <a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/ </a>).</p> <p>The algorithm can be applied to any objects, and the specific models that are used for computation in the thesis are given and created by IRIT.</p> <p>Model is provided in OBJ format.</p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
HiRISE Image Patches Obscured by Atmospheric Dust
<p><strong>Overview</strong></p> <p>The purpose of this dataset is to train a classifier to detect "dusty" versus "not dusty" patches within browse-resolution HiRISE observations of the Martian surface. Here, "dusty" refers to images in which the view of the surface has been obscured heavily by atmospheric dust.</p> <p>The dataset contains two sets of 20,000 image patches each from EDR (full resolution) and RDR ("browse" resolution) non-map-projected ("nomap") HiRISE images, with balanced classes. The patches have been split into train (n = 10,000), validation (n = 5,000), and test (n = 5,000) sets such that no two patches from the same HiRISE observation appear in more than one of these subsets. There could be some noise in the labels, but a subset of the validation images have been manually vetted so that label noise rates can be estimated. More details on the dataset creation process are described below.</p> <p> </p> <p><strong>Generating Candidate Images and Patches</strong></p> <p>To begin constructing the dataset, the paper "The origin, evolution, and trajectory of large dust storms on Mars during Mars years 24–30 (1999–2011)," by Wang and Richardson (2015), was used to compile a set of time ranges for which global or regional dust storms were known to be occurring on Mars. All HiRISE RDR nomap browse images acquired within these time ranges were then inspected manually to determine sets of images that were (1) almost entirely obscured by dust and (2) almost entirely clear of dust. Then, 10,000 patches from the two subsets of images were extracted to form the "dusty" and "not dusty" classes. The extracted patches are 100-by-100 pixels, which roughly corresponds to the width of one CCD channel within the browse image (the width of the raw EDR data products that are stitched together to form a full RDR image). Some small amount of label noise is introduced in this process, since a patch from a mostly dusty image might happen to contain a clear view of the ground, and a patch from a mostly non-dusty image might contain some dust or regions on the surface that are featureless and appear like dusty patches. A set of "vetting labels" is included, which includes human annotations by the author for a subset of the validation set of patches. These labels can be used to estimate the apparent label noise in the dataset.</p> <p>Corresponding to the RDR patch dataset, a set of patches are extracted from the same set of EDR images for the "dusty" and "not dusty" classes. EDRs are raw images from the instrument that have not been calibrated or stitched together. To provide some form of normalization, EDR patches are only extracted from the lower half of the EDRs, with the upper half being used to perform a basic calibration of the lower half. Basic calibration is done by subtracting the sample (image column) averages from the upper half to remove "striping," then computing the 0.1<sup>th</sup> and 99.9<sup>th</sup> percentiles of the remaining values in the upper half and stretching the image patch to 8-bit integer values [0, 255] within that range. The calibration is meant to implement a process that could be performed onboard the spacecraft as the data is being observed (hence, using the top half of the image acquired first to calibrate the lower half of the image which is acquired later). The full resolution EDRs, which are 1024 pixels wide, are resized down to 100-by-100 pixel patches after being extracted so that they roughly match the resolution of the patches from the RDR browse images.</p> <p> </p> <p><strong>Archive Contents</strong></p> <p>The compressed archive file contains two top-level directories with similar contents, "edr_nomap_full_resized" and "rdr_nomap_browse." The first directory contains the dataset constructed from EDR data and the second contains the dataset constructed from RDR data.</p> <p>Within each directory, there are "dusty" and "not_dusty" directories containing the image patches from each class, "manifest.csv," and "vetting_labels.csv." The vetting labels file contains a list of manually labeled examples, along with the original labels to make it easier to compute label noise rates. The "manifest.csv" file contains a list of every example, its label, and whether it belongs to the train, validation, or test set.</p> <p>An example ID encodes information about where the patch was sampled from the original HiRISE image. As an example from the RDR dataset, the ID "003100_PSP_004440_2125_r4805_c512" can be broken into several parts:</p> <ul> <li>"003100" is a unique numerical ID</li> <li>"PSP_004440_2125" is the HiRISE observation ID</li> <li>"r4805_c512" means the patch's upper left corner starts at the 4805<sup>th</sup> row and 512<sup>th</sup> column of the original observation</li> </ul> <p>For the EDR dataset, the ID "200000_PSP_004530_1030_RED7_1_r9153" is broken down as follows:</p> <ul> <li>"200000" is a unique numerical ID</li> <li>"PSP_004530_1030" is the HiRISE observation ID</li> <li>"RED7" is the CCD ID</li> <li>"1" is the CCD channel (either 0 or 1)</li> <li>"r9153" means that the patch is extracted starting at the 9153<sup>rd</sup> row (since all columns of the 1024-pixel EDR are used, no column is specified; it is implicitly always 0)</li> </ul> <p><strong>Original Data</strong></p> <p>The original HiRISE EDR and RDR data is available via the Planetary Data System (PDS), hosted at <a href="https://hirise-pds.lpl.arizona.edu/PDS/">https://hirise-pds.lpl.arizona.edu/PDS/</a></p>
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