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1940 Census Data for Austin, Texas
No full textThis is the census data collected for Austin, Texas in 1940
Instances for general quadratic integer programming problem with and without linear (resource) constraints
No full textThere are two sets of instances: 1) general quadratic integer programming problem with linear (resource) constraints; 2) general quadratic integer programming problem without linear constraints;
For general quadratic integer programming problem with linear resource constraints,
Max f(x) =dx + xqx
s.t. ax <= b; (resource constraints: such as people, equipment, material, and budget)
0<=x <=u; (lower and upper bounds of decision variables)
the name of instance is nxxxmyyytt-zzz-in.txt:
xxx is the size of decision variables;
yyy is the number of linear resource constraints where yyy=0.2*xxx or 0.5*xxx;
tt='e' is the type of instance with large slack value of 'b'
tt='d' is the type of instance with medium slack value of 'b'
tt='h' is the type of instance with small slack value of 'b'
zzz is the type of instances, zzz =1, 2, 3, 4, and 5
For zzz=1, the range of value for d and q elements is -20 to 20, the upper bound of decision variable is 10, the range of value for a is 0 to 9
For zzz=2, the range of value for d and q elements is -40 to 40, the upper bound of decision variable is 20, the range of value for a is 0 to 19
For zzz=3, the range of value for d and q elements is -80 to 80, the upper bound of decision variable is 40, the range of value for a is 0 to 39
For zzz=4, the range of value for d and q elements is -160 to 160, the upper bound of decision variable is 80, the range of value for a is 0 to 79
For zzz=5, the range of value for d and q elements is -200 to 200, the upper bound of decision variable is 100, the range of value for a is 0 to 99
for each instance, the input file format is:
name of instance
number of variables n, number of knapsack constraints m
the linear coefficients of the objective function, d
blank line
the quadratic coefficients of the objective function, q,
blank line
the right hand size of source constraints, b
the coefficients of source constraints, a
blank line
the upper bound of decision variable, u
For general quadratic integer programming problem without linear resource constraints:
Max f(x) =dx + xqx
s.t. 0
the name of instance is nxxx-zzz-in.txt:
xxx is the size of decision variables;
zzz is the type of instances, zzz =1, 2, 3, 4, and 5
For zzz=1, the range of value for d and q elements is -20 to 20, the upper bound of decision variable is 10;
For zzz=2, the range of value for d and q elements is -40 to 40, the upper bound of decision variable is 20;
For zzz=3, the range of value for d and q elements is -80 to 80, the upper bound of decision variable is 40;
For zzz=4, the range of value for d and q elements is -160 to 160, the upper bound of decision variable is 80;
For zzz=5, the range of value for d and q elements is -200 to 200, the upper bound of decision variable is 100;
for each instance, the input file format using is:
name of instance
number of variables n
the linear coefficients of the objective function, d
blank line
the quadratic coefficients of the objective function, q
blank line
the upper bound of decision variable, u<BR
Replication Data for: Assessing the performance of environmental DNA metabarcoding for characterizing fish diversity along the Texas Gulf Coast
No full texteDNA sequence files, in fasta format, for the use of a coastal Texas eDNA study conducted in 2021
3D Reconstructions
No full textThese are the thermal and visible 3D reconstructions acquired using the PyMTI-UAS instrument in Iceland during the 2022 Fagradalsfjall volcanic eruption campaign
Editors of Anthologies
No full textEditors of Anthologies file for The Database of African American and Predominantly White American Literature Anthologies (DALA
MATLAB scripts to process aggregated data from DRIAD output files
No full textThis dataset contains the MATLAB scripts needed to process the aggregated DRIAD data generated by "processing_ionwake_files.m" in the dataset "Replication Data for: DOI 10.1063/5.0075261" and creates the figures used in the Physics of Plasmas pape
Interview transcripts for the project titled Toward Data Quality Assurance Infrastructure for Research Data Repositories
No full textThis dataset is a collection of interview transcripts from a study that examined data quality assurance practices in research data repositories in the USA. In particular, the data was used to examine the following research questions:
How do research data repositories define data quality? How do RDRs ensure data quality? What are the challenges and problems of DQA in RDRs? What are some of the strategies for resolving those problems?
The dataset includes 18 transcripts representing 18 repositories and 17 universities in the US. 17 of these universities were R1 universities, and one was an R2 university.
Interviews were conducted between December 2022 and February 2023.
Out of the 18 repositories represented by the transcripts, 15 were generalist or domain-agnostic, while the remaining 3 were domain-specific. These domain-specific repositories focused on the social sciences, biology, and applied science and engineering
Texas State Retention Rates and Interventions
No full textTexas State Retention Rates and Interventions