Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
96
datasets available to search
ShareScore release 0.9.0
Dataset results
96 results for “supply chain”
Quantifying the accelerated diffusion and cost savings of global solar photovoltaic supply chains
Open the record for dataset details and reuse information.
E-Supply Chain, E-Procurement, ERP's Impact on Indonesia's Industry Performance
Open the record for dataset details and reuse information.
Characterization of innovations related to short food supply chains in Europe
<p>Characterization of the recommended technological and non-technological innovations for Short Food Supply Chains, based on the European project Smartchain. Two matrices are used to identify the technological feasibility, social suitability, organizational means and financial feasibility of technological and non-technological innovations, in different national contexts. </p>
Dairy Supply Chain Sales Dataset
<p>1.Introduction</p> <p>Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.</p> <p>One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.</p> <p>This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.</p> <p>2. Citation</p> <p>Please cite the following papers when using this dataset:</p> <ol> <li>I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "<strong>Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory</strong>," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted</li> </ol> <p>3. Dataset Modalities</p> <p>The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.</p> <p>3.1 Data Collection</p> <p>The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.</p> <p>The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.</p> <p>Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.</p> <p>It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.</p> <p>The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).</p> <table align="center"> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Period</strong></p> </td> <td> <p><strong>Number of Samples (days)</strong></p> </td> </tr> <tr> <td> <p><strong>product 1 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 1 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 1 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 2 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 2 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 2 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 3 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 3 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 3 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 4 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 4 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 4 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 5 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>363</p> </td> </tr> <tr> <td> <p><strong>product 5 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 5 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 6 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>362</p> </td> </tr> <tr> <td> <p><strong>product 6 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 6 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> <tr> <td> <p><strong>product 7 2020.xlsx</strong></p> </td> <td> <p>01/01/2020–31/12/2020</p> </td> <td> <p>362</p> </td> </tr> <tr> <td> <p><strong>product 7 2021.xlsx</strong></p> </td> <td> <p>01/01/2021–31/12/2021</p> </td> <td> <p>364</p> </td> </tr> <tr> <td> <p><strong>product 7 2022.xlsx</strong></p> </td> <td> <p>01/01/2022–31/12/2022</p> </td> <td> <p>365</p> </td> </tr> </tbody> </table> <p> </p> <p>3.2 Dataset Overview</p> <p>The following table enumerates and explains the features included across all of the included files.</p> <table> <tbody> <tr> <td> <p><strong>Feature</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p><strong>Day</strong></p> </td> <td> <p>day of the month</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Month</strong></p> </td> <td> <p>Month</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>Year</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p><strong>daily_unit_sales</strong></p> </td> <td> <p>Daily sales - the amount of products, measured in units, that during that specific day were sold</p> </td> <td> <p>units</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_sales</strong></p> </td> <td> <p>Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year</p> </td> <td> <p>units</p> </td> </tr> <tr> <td> <p><strong>percentage_difference_daily_unit_sales</strong></p> </td> <td> <p>The percentage difference between the two above values</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>daily_unit_sales_kg</strong></p> </td> <td> <p>The amount of products, measured in kilograms, that during that specific day were sold</p> </td> <td> <p>kg</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_sales_kg</strong></p> </td> <td> <p>Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year</p> </td> <td> <p>kg</p> </td> </tr> <tr> <td> <p><strong>percentage_difference_daily_unit_sales_kg</strong></p> </td> <td> <p>The percentage difference between the two above values</p> </td> <td> <p> kg</p> </td> </tr> <tr> <td> <p><strong>daily_unit_returns_kg</strong></p> </td> <td> <p>The percentage of the products that were shipped to selling points and were returned</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>previous_year_daily_unit_returns_kg</strong></p> </td> <td> <p>The percentage of the products that were shipped to selling points and were returned the previous year</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p><strong>points_of_distribution</strong></p> <p> </p> </td> <td> <p>The amount of sales representatives through which the product was sold to the market for this year</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>previous_year_points_of_distribution</strong></p> <p> </p> </td> <td> <p>The amount of sales representatives through which the product was sold to the market for the same day for the previous year</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>Table 1 – Dataset Feature Description</p> <p> </p> <p>4. Structure and Format</p> <p>4.1 Dataset Structure</p> <p>The provided dataset has the following structure:</p> <p> </p> <p>Where:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Type</strong></p> </td> <td> <p><strong>Property</strong></p> </td> </tr> <tr> <td> <p>Readme.docx</p> </td> <td> <p>Report</p> </td> <td> <p>A File that contains the documentation of the Dataset.</p> </td> </tr> <tr> <td> <p>product X</p> </td> <td> <p>Folder</p> </td> <td> <p>A folder containing the data of a product X.</p> </td> </tr> <tr> <td> <p>product X YYYY.xlsx</p> </td> <td> <p>Data file</p> </td> <td> <p>An excel file containing the sales data of product X for year YYYY.</p> </td> </tr> </tbody> </table> <p>Table 2 - Dataset File Description</p> <p>5. Acknowledgement</p> <p> </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).</p> <p> </p> <p> </p> <p>References</p> <p> </p> <p>[1] MEVGAL is a Greek dairy production company</p>
Development of public dynamic spatio-temporal monitoring and analysis tool of supply chain vulnerability, resilience, and sustainability
Open the record for dataset details and reuse information.
DNAJC9 prevents CENP-A mislocalization and chromosomal instability by maintaining the fidelity of H3-H4 supply chains
GEO Series GSE253387. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
THE ROLE OF TRANSPORT LOGISTICS IN MANAGEMENT OF PRODUCT SUPPLY CHAINS.
Open the record for dataset details and reuse information.
Interview data for 'Risks in the offshore wind supply chain and tendering process impacts: Insights from industry expert elicitations'
<p>Updated 3 category labels to reduce potential for confusion. (v3)</p> <p>Interview data with restored functionality of some unused data analysis methods. (v2)</p> <p>Original upload. (v1)</p>
E-Drone: Transforming the Energy Demand of Supply Chains Through Integrated UAV-to-land Logistics for 2030
ClinicalTrials.gov study NCT04990843. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect Partial Digested Triglycerides on Supply Long-chain Fatty Acids to Very Preterm Newborns
ClinicalTrials.gov study NCT07311382. IPD Sharing: NO. Countries: 1. Publications: 0.
Colombian Departments Exposure to Global Supply Chain Disruptions: Implications for Regional Inflation Differentials
Open the record for dataset details and reuse information.
Dataset of the paper "Software Supply Chain Meets Large Language Models: Can Dependency-related Problems Be Solved?"
<p>This is the dataset of the paper "Software Supply Chain Meets Large Language Models: Can Dependency-related Problems Be Solved?"</p>
MILP-Approach for the Supply Chain Master Planning Problem – Data Set
<p>This data set is about a multi-period, multi-stage, multi product supply chain with multi-level bill of materials supply chain master planning problem within the automotive industry. In total it specifies a supply chain consisting of 322 products, 14 nodes, 52 periods and 73 ar<strong>c </strong>and the associated parameters (e.g. inventory, production and transport capacities, bill of materials, operations, ..)</p>
Preliminary Code for "Scenarios for the Decarbonization of the Electric Vehicle Battery Supply Chain"
<p>Preliminary Dataset and Code for a paper by Sofia Martinez, Costa Samaras, and Corey Harper</p>
Supplementary information for "Conceptualization and analysis of the interdependencies between resilience and sustainability at supply chain and factory level"
Open the record for dataset details and reuse information.
SoK: Taxonomy of Attacks on Open-Source Software Supply Chains - Visualization Tool Screenshots & Selected Papers
<p>This artifact complements the paper "SoK: Taxonomy of Attacks on Open-Source Software Supply Chains", submitted at IEEE S&P 2023.</p> <p>The papers selected during the Systematic Literature Review (SLR) are presented in the CSV file.</p> <p>This screenshots display the main features of the visualization tool that allows to explore the taxonomy of attacks on OSS supply chains, as well as the related safeguards and the selected references.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.