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opencc-by-4.0Apr 2021View details →

Unit Test Trace Analysis Data

In order to find performance changes at code level, Peass (https://github.com/DaGeRe/peass) uses unit tests as proxy for the performance of a software. The performance of a unit test may not change if the called source stays the same. Therefore, PeASS' regression test selection identifies which tests to execute. For further analysis of the performance changes, the changed source is analysed and methods, which are called and which contain source code changes, are extracted. This dataset provides the results of the regression test selection and source code analysis of Apache Commons Compress, CSV, DBCP, fileupload, imaging, IO, JCS, numbers, pool and text and httpcomponents-core. To execute the analysis, execute the following steps: <pre><code class="language-bash">tar -xvf peass_metadata_commons.tar # extract folder chmod +x getCalls.sh # Make script executable, since Zenodo provides scripts without x flag ./getCalls.sh # Execute analysis</code></pre> This consumes a significant amount of hard disc space and contains long filenames; therefore execution on eCryptfs might yield problems.

Figures 31–36 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 31–36. Dirrhagofarsus unicolor (Hisamatsu). 31) Elytral apices, lateral view. 32) Male, dorsal habitus. 33) Head, frontal view. 34) Pronotum, lateral view. 35) Female, dorsal habitus. 36) Aedeagus, dorsal view. (Scale: 31–36 = 1.0 mm.)

Figures 11–14. Dirrhagofarsus species, frontal carina. 11 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 11–14. Dirrhagofarsus species, frontal carina. 11) Dirrhagofarsus modestus (Fleutiaux). 12) Dirrhagofarsus unicolor (Hisamatsu). 13) Dirrhagofarsus ernae Otto, Muona and McClarin. 14) Dirrhagofarsus brevis Otto, new species.

Figures 27–30 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 27–30. Dirrhagofarsus modestus (Fleutiaux) habitat, Chequamegon-Nicolet National Forest, along Diamond Roof Road, Oconto County WI, U.S.A., 23-VI-2021. 27) Rotten stump. 28) Female beetle on stump. 29) Female beetle on nearby fallen log. 30) Nearby fallen log. Images taken by author with Samsung Galaxy S10e smartphone.

Figures 21–26 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 21–26. Dirrhagofarsus modestus (Fleutiaux). 21) Elytral apices, lateral view. 22) Male, dorsal habitus. 23) Head, frontal view. 24) Pronotum, lateral view. 25) Female, dorsal habitus. 26) Aedeagus, dorsal view. (Scale: 21–26 = 1.0 mm.)

Figures 6–10 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 6–10. Dirrhagofarsus lewisi (Fleutiaux). 6) Dorsal habitus. 7) Head, frontal view. 8) Pronotum, lateral view. 9) Elytral apices, lateral view. 10) Aedeagus, dorsal view. (Scale: 6–10 = 1.0 mm.)

Figures 1–5 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 1–5. Dirrhagofarsus ernae Otto, Muona and McClarin. 1) Dorsal habitus. 2) Head, frontal view. 3) Pronotum, lateral view. 4) Elytral apices, lateral view. 5) Aedeagus, dorsal view. (Scale: 5 = 0.5 mm; 1–4 = 1.0 mm.)

Figures 15–20. Dirrhagofarsus brevis Otto new species. 15 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 15–20. Dirrhagofarsus brevis Otto new species. 15) Male holotype, elytral apices, lateral view. 16) Male holotype, dorsal habitus. 17) Male holotype, head, frontal view. 18) Male holotype, pronotum, lateral view. 19) Female allotype, dorsal habitus. 20) Male paratype, aedeagus, dorsal view. (Scale: 15–20 = 1.0 mm.)

opencc-by-4.0Mar 2022View details →

Trajectories and driving profiles for hybrid h2 multiple units.

Identifier: DOI Creator: German Aerospace Center, Institute of Vehicle Concepts nameType: Organizantional Title: Trajectories and driving profiles for hybrid h2 multiple units. Publisher: Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Fahrzeugkonzepte. Publication Year: 2022 ResourceType: Simulated Trajectories Subject: This data set comprises driving profiles and simulated trajectories for bi-mode fuel cell hydrogen multiple units. Date: 2022-02-10 Description: This data set comprises driving profiles and simulated trajectories for bi-mode fuel cell hydrogen multiple units. Methodology is described in relatedItem. FundingReference: FCH2Rail; Fuel Cell Hybrid Power Pack for Rail Applications; Grant Agreement Number: 101006633 RelatedItem: "D1.1 - Report on line and use case based requirements" of the FCH2Rail project. This dataset comprises following files: characteristic_traction_curve.csv Resembles the maximum force applicable by the electric engine at a given velocity to accelerate the train. Velocity is given in kilometer per hour [km/h], traction_force is given in newton [N]. characteristic_maximal_electrical_breaking_curve.csv Resembles the maximum force applicable by the electric engine at a given velocity to decelerate the train. Velocity is given in kilometer per hour [km/h], max_elec_braking_force is given in newton [N]. characteristic_maximal_mechanical_breaking_curve.csv Resembles the maximum force applicable by the mechanical brake at a given velocity to decelerate the train. Velocity is given in kilometer per hour [km/h], max_mech_braking_force is given in newton [N]. electrifications.csv Electrifications are derived from open street map (OSM). pkm resembles distances from start in kilometers. Electrification: 1 = electrified, 0 = not electrified. maxspeeds.csv pkm resembles distances from start in kilometers. Maxspeeds are derived from Open Street Map data. Data gaps are manually corrected with infrastructure maps from Adif. Maxspeeds in km/h. slopes.csv pkm resembles distances from start in kilometers. Slopes in permille are derived from JAXA ALOS 0.1 X 0.1 DEM. Underlying method is described in the public deliverable "D1.1 - Report on line and use case based requirements" of the FCH2Rail project. timetable.csv pkm resembles distances from start in kilometers. station_name represents common name of railway stations where the train stops. standing_time [s] resembles the standing time within the station in seconds. driving_time [s] resembles driving time to the next station in seconds. vehicle.csv Contains the descriptive values for the vehicle. Vehicle resembles the name of the train to which the corresponding  values are assigned. Static_mass is specified in kilogram [kg], rotating_mass is specified in kilogram [kg]. Davis coefficient davis_a is specified in newton [N], davis_b is specified in newton per kilometers per hour [N/(km/h)] & davis_c is specified in newton per squared kilometers per hour [N/(km/h)^2].   simulated_trajectory.csv Underlying simulation method, with which simulated trajectory is obtained is described in the public deliverable "D1.1 - Report on line and use case based requirements" of the FCH2Rail project. Traveltime is specified in seconds, starts with zero. Traveled distance is specified in meters [m]. Velocity is specified in meters per second [m/s].  Power at the wheel is specified in watt [W].

opencc-by-4.0Jun 2022View details →

Figure 3: Percentages of women in the non-atypical academic workforce in a subset of higher education institutions in the United Kingdom by institutional groupings, 2021

This Zenodo entry includes the full data files (.csv and .xlsx) for Figure 3: Percentages of women in the non-atypical academic workforce in a subset of higher education institutions in the United Kingdom by institutional groupings, 2021', included in the manuscript "The Curtin Open Knowledge Initiative: Sharing data on scholarly research performance", authored by members of the Curtin Open Knowledge Initiative (COKI). The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).

opencc-by-4.0Jun 2022View details →

Supporting Data for "Decarbonization pathways for the residential sector in the United States"

This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change.  Description of the files can be found in the 'Data_Descriptors.docx' file. Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu

opencc-by-4.0Jun 2022View details →

A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action

This data set pertains to the following research article: Purnell, P.J. (2022) A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action. arXiv:2201.02006

dryad40/100

Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects

Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.

opencc-zeroJul 2022View details →

dryad40/100

Differential equity in access to public and private coastal infrastructure in the Southeastern United States

Despite the ubiquity of coastal infrastructure, it is unclear what factors drive its placement, particularly for water access infrastructure (WAI) that facilitates entry to coastal ecosystems such as docks, piers, and boat landings. The placement of WAI has both ecological and social dimensions, and certain segments of coastal populations may have differential access to water. In this study, we employed an environmental justice framework to assess how public and private WAI in South Carolina, USA is distributed with respect to race and income. Using publicly available data from state agencies and the US Census Bureau, we mapped the distribution of these structures across the 301 km of the South Carolina coast. Using spatially explicit analyses with high resolution, we found that census block groups with lower income contain more public WAI, but racial composition has no effect. On the other hand, private docks showed the opposite trends, as the abundance of docks is significantly, positively correlated with census block groups that have greater percentages of White residents, while income has no effect. Under a "need-based" model of equity, we argue that WAI are not equitably distributed in South Carolina and constitute an environmental justice issue. We contend that the racially unequal distribution of docks is likely a consequence of the legacy of Black land loss, especially of waterfront property, throughout the coastal Southeast over the past half-century. Knowledge of racially inequitable distribution of WAI can guide public policy to rectify this imbalance and support advocacy organizations working to promote public water access. Our work also points to the importance of considering race in ecological research, as the spatial distribution of coastal infrastructure both directly affects ecosystems through the structures themselves and regulates which groups access water and what activities they can engage in at those sites.

opencc-zeroJul 2022View details →

opencc-by-4.0Jul 2022View details →

Gesport_Women on sports boards in NSFs of five countries: Italy, Portugal, Spain, Turkey and the United Kingdom

This database has been built by the authors with the support of the Erasmus+ programme of the European Union under Grant number 590521-EPP-1-2017-1-ES-SPO-SC. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain, and the United Kingdom in 2018 and in 2022.

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions

This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model. The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions. The two files, SWA9505V2_Gsim_PMA_WRES.mat and SWA9505V2_Gsim_PMA_WMP.mat, represent model outputs from the two hydropower models, WRES and WMP respectively. Each file contains 6 variables: 1) “Models”: the 10 global climate models (GCMs). 2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015). 3) “PMA_G_mn_6605”:  1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH. 4) “PMA_G_mn_1150”:  Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation. 5) “PMA_G_yr_6605”:  1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH. 6) “PMA_G_yr_1150”:  Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation. The following journal paper details the method in creating the dataset: Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants Zhou et al. (2022) Preparing for submission to Environmental Research Letters.