We analyzed the structure of English language WikiProject coeditor networks and compare to the efficiency and performance of those projects.
The list of WikiProjects give an integer key, title, and unique URL for each project. The network files are indexed by the integer keys. The quality assessment logs are indexed by project title and article title. and Curation Notes: Readme file was updated Oct. 11, 2018 to include additional context on research, file contents, and organization (see first section of readme), and explanation of additional license in the deposit referring to the 'logbook' module.
This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
The data file is comprised of 22,500 X-ray projections (15 scans of 1500 projections each) recorded during solidification of Al-Ge-Na. The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.
Biomass burning produces smoke aerosols that are emitted into the atmosphere. Some smoke constituents, notably black carbon (BC), are highly effective light-absorbing aerosols (LAA). Emitted LAA can be transported to high albedo regions like the Greenland Ice Sheet (GrIS) and affect local snowmelt. In the summer, the effects of LAA in Greenland are uncertain. To explore how LAA affect GrIS snowmelt and surface energy flux in the summer, we conduct idealized global climate model simulations with perturbed aerosol amounts and properties in the GrIS snow and overlying atmosphere. The in-snow and atmospheric aerosol burdens we select range from background values measured on the GrIS to unrealistically high values. This helps us explore the linearity of snowmelt response and to achieve high signal-to-noise ratios. With LAA operating only in the atmosphere, we find no significant change in snowmelt due to the competing effects of surface dimming and tropospheric warming. Regardless of atmospheric LAA presence, in-snow BC-equivalent mixing ratios greater than ~60 ng/g produce statistically significant snowmelt increases over much of the GrIS. We find that net surface energy flux changes correspond well to snowmelt changes for all cases. The dominant component of surface energy flux change is solar energy flux, but sensible and longwave energy fluxes respond to temperature changes. Atmospheric LAA dampen the magnitude of solar radiation absorbed by in-snow LAA when both varieties are simulated. In general, the significant melt and surface energy flux changes we simulate occur with LAA quantities that have never been recorded in Greenland.
These data collection and analysis protocols and the attribute list are part of a larger research project, the Institute of Museum and Library Services # LG-06-14-0122-14. funded "Qualitative Data Reuse: Records of Practice in Educational Research and Teacher Development." As such, our research questions concern data reuse and data curation:
1. Data Reuse: What are the dynamics of the data reuse lifecycle (from selection of data through the reuse of data) in a qualitative digital educational archive?
2. Data curation: What special issues are involved in curating digital qualitative data for reuse?
• How can qualitative data archives best support data reusers throughout the data reuse lifecycle?
• What aspects of this experience are informative for other types of qualitative data archives?
The overall project employed mixed methods and collected interview, observational, and trace data from data reusers of video records of practice in education and repositories holding video records of practice. The interview protocol and interview codeset relate to the 44 interviews conducted with researchers and teacher-educators who have reused digital video records of practice as qualitative data for research and/or teaching.
Raw SNP genotypes are provided in STRUCTURE format, with a maximum of one SNP reported per ddRAD locus. The files "caryco_SNP.str" and "caryov_SNP.str" are genotypes for Carya cordiformis and Carya ovata, respectively. The first column of each file is the individual name, the second column is the population (see original publication for information on population locations), and the remaining columns are genotypes of individual SNPs. Rows represent individuals, with the diploid genotypes contained on two lines per individual. Missing data are entered as "0" (zero). The first row is a header with a unique identifier for each SNP. and Occurrence records for each species are provided in the file "occs_carya.csv" and contain the latitude and longitude of each record.
Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal.
The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.
The data were collected as part of the Stewardship Gap project, an 18-month study to investigate how research data and creative outputs supported by public or non-profit funding in the United States are being stewarded. These data were collected as part of a literature search of sources about research data stewardship and relate most directly to work describing “What We Know About the Stewardship Gap.” In this work, we categorized “gaps” in stewardship identified in the literature, how the gaps were related to one another, and efforts to measure and develop metrics for the gaps.
This dataset contains all data used to generate the figures in The Cryosphere manuscript “Measuring Snow Specific Surface Area with 1.30 and 1.55 micro-meter Bidirectional Reflectance Factors,” by Adam Schneider, Mark Flanner, and Roger De Roo. These data support the theory, calibration, and application of the Near-Infrared Emitting and Reflectance Monitoring Dome (NERD), an instrument engineered to rapidly retrieve surface snow specific surface area in the field. Note that this deposit includes a microCT scan database for natural snowfall samples collected in New Hampshire during 2015-2017, comprised of raw tiff files as well as reconstructions, binarized reconstructions, and some 3D model reconstructions. and Running python scripts generally require that the following packages are installed: NumPy, SciPy, Matplotlib, Pandas, and ipdb (for debugging).
Percent Weight Change Data:
The model was run continuously on a daily time step for seasonal intervals (Spring: March thru May; Summer: June thru August; Fall: September thru November) as well as contiguously from Spring to Fall to assess total growth over the likely growing season (March thru November). CSV files represent the simulated weight change (%) of Bighead and Silver Carp for the respective time periods associated with the file name. Initial fish mass for each seasonal interval and growing season was 4350 g for Silver Carp and 5480 g for Bighead Carp. Maximum and mean total weight change (%) was determined for three depth ranges (near surface depths [NS]: 0 – 10 m; deep chlorophyll layer depths [DCL]: 10 - 50 m; and whole water column [WC]). Coordinates are in decimal degrees.
File naming convention: speciesSeasonWtChange (e.g. bigheadFallWtChange = % weight change of Bighead Carp from September through November)
Monthly Habitat Quality Data:
Rdata files contain matrices of Bighead or Silver carp growth rate potential as represented as a mass-proportional growth rate (gram of carp/gram of carp/day [g/g/d]) for the 15th day of each month. Habitats with growth rate potential >= 0 g/g/d were deemed suitable.
Rows: Row numbers refer to the spatial node with 20 equally-spaced vertical layers.
Columns: Columns 1-20 refer to the growth rate potential value for each vertical layer of each node. Vertical layers are evenly spaced based on the total depth of the water column for each node. Depth for each node can be found in the grid attributes data file. Columns 21 ("meanG") and 22 ("Gmax") represent the average and maximum growth rate potential, respectively, of the fish across the whole water column for the corresponding node.
File naming convention: species_MonthNumber (e.g. silver_06 = Silver carp growth rate potential in June)
Spatial coordinates for each node can be found in the grid attributes data files.,
Grid attributes data:
This Rdata file provides the spatial reference data and other grid attributes. Coordinates are provided in UTM (x & y) and latitude and longitude (decimal degrees). Depth (meters) for each node is listed in this file.
, GRP Model code:
Details bioenergetics equations, foraging equation, functions for running the model on a monthly time-step and daily time step, and functions for basic analyses. Model is coded in R., and
The simulated input data (prey and temperature) used to run our model is not included in this data set. Instead we provide the model code, grid attributes, and outputs of the model.
The readRDS() function (R Base Package v.3.5.1) is required to read in .Rdata files in R.