Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Data from: Phenological mismatches mitigate the ecological impact of a biological invader on amphibian communities

Data and code for the manuscript entitled "Phenological mismatches mitigate the ecological impact of a biological invader on amphibian communities" for Ecological Applications (2024).  The raw sequencing data was submitted to NCBI’s Sequence Read Archive (SRA) under BioProject number PRJNA1023717 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1023717?reviewer=rciv7kme2qq5vpcnqqog4vjioo).

opencc-zeroMay 2024View details →

Climate change mitigation potential of widespread cover crop adoption in U.S.

This geospatial dataset represents climate change mitigation benefits from widespread cover crop adoption on U.S. cropland. We simulated changes in soil organic carbon stocks and nitrous oxide fluxes over a 20-year period for baseline cover crop adoption rates (derived from historical adoption rates) and a high cover crop adoption (80%) scenario in the continental U.S. Data were generated using the DayCent ecosystem model driven by cropping histories in the USDA National Resources Inventory (NRI) and associated agricultural management data. Here we present the mean and standard deviation of annual soil organic carbon stock changes and nitrous oxide fluxes for both baseline and high cover crop adoption scenarios on a county level.

opencc-by-4.0Mar 2023View details →

Mitigating urban heat island through neighboring rural land cover: Dataset

A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - Average_LST_GEE.sh; Codes for calculating urban development intensity - UrbanDevelopmentIntensity.py; Regression, Machine Learning, Interpretable Machine Learning Code - AllRegression.py, SHAP.py, ALE.py; a zip file containing the data used in the calculations - OperationalData.rar.

opencc-by-4.0Dec 2017View details →

Factor XII deletion mitigates cerebral microbleed load but not hemodynamic dysfunction in the arcAβ mice

Cerebrovascular dysfunction and a prothrombotic state have been found in patients with Alzheimer’s disease (AD). The factor XII (FXII)-driven activated contact system has been implicated in the vascular pathology and inflammation in AD patients and AD mouse models. Here we investigated the effect of genetic deletion of FXII on AD-related vascular dysfunction using magnetic resonance imaging (MRI). AD mouse line ArcAβ, arcAβ/FXII-/-, FXII-/- and non-transgenic littermates  of 17 months of age (n = 40) were assessed for 1) cerebral microbleeds (CMB) load using susceptibility weighted imaging (SWI) MRI, 2) cerebral blood flow (CBF) using arterial spin labeling, and 3) vascular reactivity by estimating changes in cerebral blood volume (∆CBV) during hypercapnic stimulus using acetazolamide.

opencc-by-4.0Nov 2018View details →

Mitigating Traffic Congestion on I-10 in Baton Rouge, LA: Supply- and Demand-Oriented Strategies & Treatments

Corresponding data set for Tran-SET Project No. 17ITSLSU09. Abstract of the final report is stated below for reference: "The aim of this study is to develop a better understanding of the causes of traffic congestion on I-10 in the Baton Rouge, LA area, particularly at the I-10 Mississippi River Bridge, and to identify treatments and strategies to mitigate congestion at the bridge site. This study developed and calibrated a microsimulation model of I-10 (from Lobdell Highway in Port Allen to Highland Road, I-110 to Florida Street, and I-12 to Walker Road) and investigated several supply- and demand-oriented strategies. This includes: rehabilitation and utilization of the old Mississippi River Bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak period, and ramp metering at the on-ramp west of the I-10 Mississippi River Bridge. The majority of the tested strategies appear to be feasible and effective solutions; however, a combination of supply- and demand-oriented treatments must be implemented to fully relieve congestion on I-10 in Baton Rouge."

Datas for Leveraging ecosystems responses to enhanced rock weathering in mitigation scenarios

This dataset contains the jupyter notebook calibrating the P-cycle emulator based on the ORCHIDEE-CNP outputs produced by Daniel S. Goll. The zip file contains a set of results from the simulations.

Moreno_et_al_2024_Biodiversity impacts of Paris-compliant land-based mitigation scenarios

Land cover areas in 2020 and 2050, charecterisation factors and PSL impacts in 2050 by land cover type and by ecoregion under the five mitigation scenarios modelled.

H2020 ENODISE: GPUP Configuration A Numerical Databases with Mitigation

H2020 ENODISE: GPUP Configuration B Numerical Databases with Mitigation

This dataset contains the numerical prediction for A2 configuration as defined in the H2020 ENODISE project (<a href="https://www.vki.ac.be/index.php/about-enodise">https://www.vki.ac.be/index.php/about-enodise</a>) The dataset corresponds to the Band-Limited Overall Sound Pressure Level (BL OASPL) measurements obtained for Configuration B, focusing on the acoustic performance of propellers under different configurations. Two main setups were tested: <ol> <li>Propeller without Serrations: This configuration represents the baseline case where the propeller operates without any modifications to its trailing edge.</li> <li>Propeller with Serrations: This configuration represents the case where the propeller operates with serrations at the trailing edge of the propeller.</li> </ol> <h1>Setup </h1> <ol> <li> Rotors: The setup involves three rotors: <ul> <li>Rotor_C (Central Rotor)</li> <li>Rotor_L (Left Rotor)</li> <li>Rotor_R (Right Rotor)</li> </ul> The Left and Right Rotors (Rotor_L and Rotor_R) are fixed at a position of x = -0.22m. </li> <li> Axial Locations and Phase Angles: The central rotor (Rotor_C) was moved in the axial direction, and three distinct setups were investigated: <ul> <li>Baseline Case (Δx = 0Db): Rotor_C located at x = -0.22m.</li> <li>Forward Case (Δx = -0.2Db): Rotor_C moved forward to x = -0.26m.</li> <li>Backward Case (Δx = +0.2Db): Rotor_C moved backward to x = -0.18m.</li> </ul> For each axial location, four phase angles were tested: 0°, 45°, 90°, 135°. </li> </ol> <h1>Dataset :</h1> <ol> <li> Total Simulations: <ul> <li>Each propeller configuration (serrated and non-serrated) was tested under the three axial locations and four phase angles, resulting in a total of 12 simulations for each setup.</li> <li>This results in 24 simulations (12 simulations with serrations + 12 simulations without serrations) across all configurations.</li> </ul> </li> <li> Measured Parameters: <ul> <li>Area-averaged Bandlimited Prms: The overall sound pressure level averaged over the area, given in both linear scale and converted to decibels (dB).</li> <li>Maximum Bandlimited Prms: The maximum sound pressure level observed within the band, also provided in both linear scale and dB.</li> </ul> </li> <li>Script <ul> <li>Python script is provided which converts original dataset to log scale </li> </ul> </li> </ol>

opencc-by-4.0Apr 2019View details →

Fig. 2 in The effectiveness of fruit bagging and culling for risk mitigation of fruit flies affecting citrus in China: a preliminary report

Fig. 2. Culling at local purchase station.

opencc-by-4.0Apr 2019View details →

Fig. 3 in The effectiveness of fruit bagging and culling for risk mitigation of fruit flies affecting citrus in China: a preliminary report

Fig. 3. Final packinghouse culling.

opencc-by-4.0Sep 2024View details →

Metadata: In-ovo stimulation trains innate immunity to mitigate Campylobacter jejuni in broiler chickens

Metadata of the paper investigating the physiological and genomic responses of these in-ovo stimulated chickens to Campylobacter jejuni infection.

opencc-by-4.0Oct 2024View details →

Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts - Data

<div> This Origin file contains the supporting processed data for all of the figures from the main paper from the publication 'Investigating Discontinuous X-ray Irradiation as a Damage Mitigation Strategy for [M(COD)Cl]2 Catalysts.  </div>

opencc-by-4.0Oct 2024View details →

Reproduction package for: Using machine learning techniques to mitigate confidentiality violations

This is a reproduction package for the data shown in my master's thesis "Using machine learning techniques to mitigate confidentiality violations"

opencc-by-sa-4.0Nov 2024View details →

Emission library populated with emission rates and effectiveness rates from risk mitigation measures.

This document contains  an emission library populated with emission rates from at least 10-15 industrial activities and effectiveness rates from at least 30 risk mitigation measures (RMMs)”, which includes a literature review of emission factors from diverse sources and a review of effectiveness rates for different risk mitigation measures (RMMs), as well as a description of the methodology applied during the reviews. This document is part of the report of Action B5  of  NANOHEALTH project (LIFE20 ENV/ES/000187).

opencc-zeroJun 2021View details →

Landscape-scale conservation mitigates the biodiversity loss of grassland birds

The decline of biodiversity from anthropogenic landscape modification is among the most pressing conservation problems world-wide. In North America, long-term population declines have elevated the recovery of the grassland avifauna to among the highest conservation priorities. Because the vast majority of grasslands of the Great Plains are privately owned, the recovery of these ecosystems and bird populations within them depend on landscape-scale conservation strategies that integrate social, economic, and biodiversity objectives. The Conservation Reserve Program (CRP) is a voluntary program for private agricultural producers administered by the United States Department of Agriculture that provides financial incentives to take cropland out of production and restore perennial grassland. We investigated spatial patterns of grassland availability and restoration to inform landscape-scale conservation for a comprehensive community of grassland birds in the Great Plains. The research objectives were to 1) determine how apparent habitat loss has affected spatial patterns of grassland bird biodiversity, 2) evaluate the effectiveness of CRP for offsetting the biodiversity declines of grassland birds and 3) develop spatially explicit predictions to estimate the biodiversity benefit of adding CRP to landscapes impacted by habitat loss. We used the Integrated Monitoring in Bird Conservation Regions program to evaluate hypotheses for the effects of habitat loss and restoration on both the occupancy and species richness of grassland specialists within a continuum modelling framework. We found the odds of community occupancy declined by 37% for every 1 Standard Deviation (SD) decrease in grassland availability [loge(km2)] and increased by 20% for every 1 SD increase in CRP land cover [loge(km2)]. There was 17% turnover in species composition between intact grasslands and CRP landscapes, suggesting grasslands restored by CRP retained considerable, but incomplete representation of biodiversity in agricultural landscapes. Spatially explicit predictions indicated absolute conservation outcomes were greatest at high latitudes in regions with high biodiversity, whereas the relative outcomes were greater at low latitudes in highly modified landscapes. By evaluating community-wide responses to landscape modification and CRP restoration at bioregional scales, our study fills key information gaps for developing collaborative strategies, and balancing conservation of avian biodiversity and social well-being in agricultural production landscapes of the Great Plains.

opencc-zeroDec 2020View details →

Plant traits of grass and legume species for flood resilience and N2O mitigation

Flooding threatens the functioning of managed grasslands by decreasing primary productivity and increasing nitrogen losses, notably as the potent greenhouse gas nitrous oxide (N2O). Sowing species with traits that promote flood resilience and mitigate flood-induced N2O emissions within these grasslands could safeguard their productivity while mitigating nitrogen losses. We tested how plant traits and resource acquisition strategies could predict flood resilience and N2O emissions of 12 common grassland species (eight grasses and four legumes) grown in field soil in monocultures in a 14-week greenhouse experiment. We found that grasses were more resistant to flooding, while legumes recovered better. Resource-conservative grass species had higher resistance, while resource-acquisitive grasses species recovered better. Resilient grass and legume species lowered cumulative N2O emissions. Grasses with lower inherent leaf and root δ13C (and legumes with lower root δ13C) lowered cumulative N2O emissions during and after the flood. Our results highlight the differing responses of grasses with contrasting resource acquisition strategies, and of legumes to flooding. Combining grasses and legumes based on their traits and resource acquisition strategies could increase the flood-resilience of managed grasslands, and their capability to mitigate flood-induced N2O emissions.

opencc-by-4.0Nov 2022View details →

UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation

This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density.  Below are the description of each data file: <ul> <li>high_density_both_lane_raw_AI_speed.csv <ul> <li>Description: extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li>Data Fields: <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>

opencc-zeroDec 2022View details →

Can behavior and physiology mitigate effects of warming on ectotherms? A test in urban ants

1. Global climate change is expected to have pervasive effects on the diversity and distribution of species, particularly ectotherms whose body temperatures depend on environmental temperatures. However, these impacts remain difficult to predict, in part because ectotherms may adapt or acclimate to novel conditions or may use behavioral thermoregulation to reduce their exposure to stressful microclimates. 2. Here we examine the potential for physiological and behavioral changes to mitigate effects of environmental warming on five species of ants in a temperate forest habitat subject to urban warming. 3. We worked in eight urban and eight non-urban forest sites in North Carolina, USA; sites experienced a 1.1°C range of mean summer air temperatures. At each site, we documented species-specific microclimates (ant operative temperatures, Te) and ant activity on a transect of 14 bait stations at three times of day. In the laboratory, we measured upper thermal tolerance (CTmax) and thermal preference (Tpref) for each focal species. We then asked whether thermal traits shifted at hotter sites, and whether ants avoided non-preferred microclimates in the field. 4. CTmax and Tpref did not increase at warmer sites, indicating that these populations did not adapt or acclimate to urban warming. Consistent with behavioral thermoregulation, four of the five species were less likely to occupy baits where Te departed from Tpref. Apparent thermoregulation resulted from fixed diel activity patterns that helped ants avoid the most inappropriate temperatures but did not compensate for daily or spatial temperature variation: Hotter sites had hotter ants. 5. This study uses a novel approach to detect behavioral thermoregulation and sublethal warming in foraging insects. The results suggest that adaptation and behavior may not protect common temperate forest ants from a warming climate, and highlight the need to evaluate effects of chronic, sublethal warming on small ectotherms.