New Allocation

## Overview

New Allocation is a dataset for object detection tasks - it contains Trash R3fm annotations for 1,893 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset provides detailed budget allocation insights for urban and rural households in India, capturing present living standards. The data includes various spending areas such as housing, food, transportation, healthcare, education, and discretionary expenses. The dataset is designed to help researchers, policymakers, and individuals understand spending habits and optimize budget planning.

Context: The dataset is derived from various government reports, surveys, and market research studies that provide a snapshot of the current economic conditions and living standards in India. It includes average income levels, typical expenses, and common savings patterns for both urban and rural households.

Sources:

National Sample Survey Office (NSSO) Ministry of Statistics and Programme Implementation (MoSPI) Various market research reports and publications Inspiration: The inspiration behind this dataset is to provide a clear and detailed picture of how households in different regions of India allocate their budgets. This can be a valuable resource for economists, social scientists, financial advisors, and anyone interested in understanding the financial behavior of Indian households.

This dataset contains client targets and financial data for every service delivery site contracted to deliver the Ontario Employment Assistance Services (OEAS) program for the 2016/17 to 2018/19 fiscal years. The financial data includes the amount of money allocated, the amount each service delivery site reported as having spent, and the Ministry approved expenditure amount. Service delivery sites are operated by service providers who hold the funding agreement with the Ministry, and one service provider can operate multiple service delivery sites. The values are broken down by the different budget lines for OEAS.Contextual Documentation

Dataset

Technical Dictionary

This dataset contains data on the financial allocations and expenditures for every service delivery site contracted to deliver Youth Job Connection (YJC) program for the 2016/17 to 2018/19 fiscal years. The financial data includes the amount of money allocated, the amount each service delivery site reported as having spent, and the Ministry approved expenditure amount. Service delivery sites are operated by service providers who hold the funding agreement with the Ministry, and one service provider can operate multiple service delivery sites. The values are broken down by the different budget lines for YJC.Contextual Documentation

Dataset

Technical Dictionary

This dataset contains client targets and financial data for every service delivery site contracted to deliver the Employment Service (ES) program for the 2016/17, 2017/18, and 2018/19 fiscal years. The financial data includes the amount of money allocated, the amount each service delivery site reported as having spent, and the Ministry approved expenditure amount. Service delivery sites are operated by service providers who hold the funding agreement with the Ministry, and one service provider can operate multiple service delivery sites. The values are broken down by the different budget lines for ES.Contextual Documentation

Dataset

Technical Dictionary

[v2 update] weather data correction

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal: Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale, Dataset of an energy community's generation and consumption with appliance allocation, Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108590 (https://www.sciencedirect.com/science/article/pii/S2352340922007971)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

End-user profiles:

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households

https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption

https://site.ieee.org/pes-iss/data-sets/

Dataset for paper to appear in Kenekayoro P (In Press). Population Based Techniques for Solving the Student Project Allocation Problem. International Journal of Applied Metaheuristic Computing (IJAMC)Contains data where students listed their preferred subject areas in other of preference. Dataset also contains subject areas with and academic supervisor to supervise in that project area.Goal is to create a student project allocation problem where students are allocated their preferred projects whilst ensuring that supervisors supervise equal number of students and average grade point average of students assigned to each supervisor is the same

This dataset is about books. It has 3 rows and is filtered where the book is Asset allocation : balancing financial risk. It features 7 columns including author, publication date, language, and book publisher.

Comparison of Asset Allocation - City of Providence Employees' Retirement System & Similar Plans

This dataset is about book subjects. It has 2 rows and is filtered where the books is International trade and resource allocation. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset contains 16 files, each of which details a crisis situation with a number of emergency sites, service providers, units, and types of emergencies. The specifications of each file are attached in their metadata. This is the accompanying dataset for the publication "A Combinatorial Optimization Model for Emergency Resource Allocation after Disasters."

Dataset containing all of the Federal Funding Allocation inputs submitted by reporting transit agencies to the National Transit Database in the 2022 and 2023 report years. This reflects the most recently published data within the Federal Transit Administration's NTD Data website.

These datatasets relate to the computational study presented in the paper "The Berth Allocation Problem with Channel Restrictions", authored by Paul Corry and Christian Bierwirth. They consist of all the randomly generated problem instances along with the computational results presented in the paper.

Results across all problem instances assume ship separation parameters of [delta_1, delta_2, delta_3] = [0.25, 0, 0.5].

Excel Workbook Organisation:

The data is organised into separate Excel files for each table in the paper, as indicated by the file description. Within each file, each row of data presented (aggregating 10 replications) in the corrsponding table is captured in two worksheets, one with the problem instance data, and the other with generated solution data obtained from several solution methods (described in the paper). For example, row 3 of Tab. 2, will have data for 10 problem instances on worksheet T2R3, and corresponding solution data on T2R3X.

Problem Instance Data Format:

On each problem instance worksheet (e.g. T2R3), each row of data corresponds to a different problem instance, and there are 10 replications on each worksheet.

The first column provides a replication identifier which is referenced on the corresponding solution worksheet (e.g. T2R3X).

Following this, there are n*(2c+1) columns (n = number of ships, c = number of channel segmenets) with headers p(i)_(j).(k)., where i references the operation (channel transit/berth visit) id, j references the ship id, and k references the index of the operation within the ship. All indexing starts at 0. These columns define the transit or dwell times on each segment. A value of -1 indicates a segment on which a berth allocation must be applied, and hence the dwell time is unkown.

There are then a further n columns with headers r(j), defining the release times of each ship.

For ChSP problems, there are a final n colums with headers b(j), defining the berth to be visited by each ship. ChSP problems with fixed berth sequencing enforced have an additional n columns with headers toa(j), indicating the order in which ship j sits within its berth sequence. For BAP-CR problems, these columnns are not present, but replaced by n*m columns (m = number of berths) with headers p(j).(b) defining the berth processing time of ship j if allocated to berth b.

Solution Data Format:

Each row of data corresponds to a different solution.

Column A references the replication identifier (from the corresponding instance worksheet) that the soluion refers to.

Column B defines the algorithm that was used to generate the solution.

Column C shows the objective function value (total waiting and excess handling time) obtained.

Column D shows the CPU time consumed in generating the solution, rounded to the nearest second.

Column E shows the optimality gap as a proportion. A value of -1 or an empty value indicates that optimality gap is unknown.

From column F onwards, there are are n*(2c+1) columns with the previously described p(i)_(j).(k). headers. The values in these columns define the entry times at each segment.

For BAP-CR problems only, following this there are a further 2n columns. For each ship j, there will be columns titled b(j) and p.b(j) defining the berth that was allocated to ship j, and the processing time on that berth respectively.

The Resource Allocation dataset contains information about resources and agents.

This dataset describes the software and data to quantify uncertainty in Pareto fronts arised from spatial data. This dataset contains the Python code for a multi-objective land use allocation optimization under uncertainty. The program is an extension to CoMOLA from Strauch et al. 2019 (https://doi.org/10.1016/j.envsoft.2019.05.003). For a detailed description of CoMOLA, we refer to the article. Before executing CoMOLA under uncertainty, extreme lower and upper bound samples need to be generated from the land use and soil fertility map with quantified uncertainty. That preprocessing is described reproducable with the following Mendeley Dataset: Hildemann, Moritz Jan; Verstegen, Judith (2021), “Sampling procedure of land use and soil fertility map under uncertainty”, Mendeley Data, V2, doi: 10.17632/6x6cccfc4x.1. For every produced extreme sample, CoMOLA needs to be performed with the corresponding land use and soil fertility map. As the computational effort and computation time are high (15-20 hours) and ten optimizations were performed for every extreme sample, the runs were performed in parallel on a high-performance Linux cluster (MEGWARE cluster with 15.120 cores, 412 nodes and Intel Xeon Gold 6140 18C 2.30GHz processors). The program is executable for Python 3.7 and 3.8 in a Linux environment. The changes compared to CoMOLA include: an update to Python 3.8, removal of R components in objecting the objective values, and the implementation of a seeding procedure to inject single-objective optima into the first generation of the Genetic Algorithm. The seeding procedure allowed faster and better convergence. The generated Pareto fronts can be used postprocessing to quantify the uncertainty in objective and solution space. Pseudo-random states are used to assure reproducibility despite the stochastic processes.

These are the dummy datasets used in the tutorial, "Producing a final year project allocation," for the Python library Matching's documentation.

Each dataset specifies the affiliations, capacities and/or preferences of the players in an instance of the student-project allocation problem (SA).

This dataset (n=18,486, vars=111) is the second of the two WEAI datasets used to calculate the WEAI measures. It includes all of the 24-hour time allocation data from Module G6, the time use questionnaire, and thus each respondent in Module G has multiple records, one for each of the 18 time use activities. The unique identifiers are pbs_id + idcode + activity.

The programs replicate tables from "Household Time Use Among Older Couples: Evidence and Implications for Labor Supply Parameters", by Rogerson and Wallenius. Please see the Readme file for additional details.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_0ad604387b5b2dd99fbf48d89cb4f416/view

The dataset is published with the following paper, please cite this paper when using the data, which is highly appreciated:Most root-derived carbon inputs do not contribute to long-term global soil carbon storage | Science China Earth SciencesWang, G., Xiao, L., Lin, Z. et al. Most root-derived carbon inputs do not contribute to long-term global soil carbon storage. Sci. China Earth Sci. 66, 1072–1086 (2023). https://doi.org/10.1007/s11430-022-1031-5There are three files and a reference list:Supplementary Data: The 54 studies from which the NPPdata were derived (Reference_NPP Data.docx).code.7z: Code to perform the data analysis in R.Output products.rar : Generated digital maps of BNPP.Please cite this paper when using the dataset, which is highly appreciated:Most root-derived carbon inputs do not contribute to long-term global soil carbon storage | Science China Earth Sciences