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25 results for “company data”
What does it take to generate new growth - Survey data on company perceptions on innovative behavior
<p>This data includes raw survey data, a codebook and the survey form for the survey <em>what does it take to generate new growth? </em>The survey focused on comprehensively mapping the Finnish companies growth outlooks and their underlying management practices and principles. The study creates an overview of top managers’ views on Finnish companies’ growth, innovativeness, and the ability for renewal. It allows us to identify what sets high-growing companies apart from others. The Codebook is associated with an SPSS and CSV file including the data.</p>
Survey with game development companies on personal data protection
<p><strong>Dataset linked to the article: </strong>Investigating the Implementation of Data Protection Laws in Brazilian Game Companies: An Initial Study</p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
Data on Companies of the Hungarian Battery Value Chain
<p>This dataset compiles data on companies of the Hungarian electric vehicle battery value chain. The data is accurate as of November 2024. </p> <p>The following data is presented:</p> <ul> <li>Position in the value chain;</li> <li>Company name;</li> <li>Data on the investment and investment subsidies provided by the Hungarian Government;</li> <li>Company financials (2023),</li> <li>Employee numbers, information on agency workers, average wages.</li> </ul>
Client data for churn prediction for an internet shipment reselling company
<p> Dataset to train and test a churn classifier model for a ecommerce company.</p>
Data on Direct-to-Consumer Genetic Testing and DNA testing companies
<p>This table was compiled between 2011 and 2018. It is a list of companies offering direct-to-consumer DNA tests over the internet. It is primarily concerned with direct-to-consumer genetic testing companies operating currently and also includes those, which are no longer operating. The table briefly summarizes the services offered by each company and gives the company’s location.</p> <p>I am continuing to update this list, so it is a work in progress, it does include some companies that advertise their services to physicians and some companies may have altered their offerings. Further updates will be provided later in 2018. I have added a shorter table at the end of relevant other companies. I hope to make a searchable database of a revised version of the document available through my website in the future.</p> <p>This work is being released for informational and educational purposes and should not be used for commercial purposes.</p> <p>Please refer to my website for further updates <a href="http://www.andelkamphillips.com">http://www.andelkamphillips.com</a></p>
Big Data Analytics: Data-driven optimization of manufacturing companies
<p>This dataset contains the result of a survey conducted to identify the transformation of manufacturing companies by big data analytics. It contains 154 participations from employees of manufacturing companies of which 133 have completed all questions. The dataset is UTF8 encoded and available in German (original) as well as English (translated).</p>
Technology transfer from Nordic capital parenting companies to Lithuanian and Estonian subsidiaries or joint capital com-panies: the analysis of the obtained primary data
<p>Scientific literature describes various factors influencing knowledge transfer and successful adoption, assimilation, transformation, and exploitation. These four components are mostly related to the absorptive capacity of the company. However, more factors influence both developments of innovations or patents and the lack of ability to use external and internal information (knowledge).<strong> </strong>Using external knowledge is often associated with previous experience or even a point of view towards investment in innovation or developing patents. Thus, the companies might be divided into innovators and imitators. The research addresses several problems (questions). What external factors are influencing knowledge transfer and further development of innovation? What factors are influencing absorptive capacity? What factors are essential in cooperation and knowledge transfer to switch from a linear to a circular economy? To collect data, a computer-assisted telephone interviewing method was used. The survey was addressed to subsidiaries, joint companies, Lithuanian-Nordic, Estonian-Nordic capital companies, or companies in close collaboration with the Nordic countries. 158 companies from Estonia and Lithuania agreed to answer all questions. The survey involves companies of various sizes and ages from different business sectors. Reliability was denoted as Cronbach's alpha has been estimated. KMO test was used to measure whether data is suitable for principal component analysis.</p> <p> Additionally, PCA was performed. PCA reduced the number of variables into an extracted number of components. The separate row of the component defined a linear composite of the component score that would be the expected value of associated variable. The data set may be used to develop interlinkages among the research mentioned above questions, and results of introducing innovation, the company's size, and age might be used as control variables. The article aims to analyze factors determining innovation development and their interlinkages while technology is transferred from Nordic parenting companies to the subsidiaries. The article's results contribute to the interdisciplinary knowledge transfer, innovations, and internationalization field.</p>
DMP Examine the correlation between Twitter Sentiment data and the stock data of the 4 big tech companies Apple, Amazon, Google and Microsoft
<p><span>The purpose of using these specific datasets are, the possibilities they give to analyze the stock data changes, based on the sentiment analysis of the previous day. Including this output information it is possible to analyze our goal of searching for possible correlation between those two. </span></p>
Data for "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks"
<p>This deposit provides the data for replicating the figures and table in the paper "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks". The full access is available upon request before June 2022. The complementary code is stored in a separate deposit (10.5281/zenodo.4739416).</p>
Company data
<p>Company data for featured example in "A Network Spectral Approach to Identify Organizational Weaknesses and Assess Strategic Robustness"</p>
A real-world energy management data set from a smart company building for optimization and machine learning
Open the record for dataset details and reuse information.
Data from: Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices
Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies' clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study's measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
<p><strong>Replication package for our paper </strong><a href="https://arxiv.org/abs/2212.06540">Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool</a></p> <p>It contains the following files:</p> <ol> <li>Our data set of 432,411 news headlines annotated as being environmental-, governance-, or social-related. We encourage fellow researchers to use the corpus as a benchmark for other ESG-relevant NLP tasks.</li> <li>Code/Notebooks that we used for the training and evaluation of our company detection, ESG classification, and sentiment models</li> <li>Full tables detailing the results of all experiments performed </li> </ol>
Clinical Trial Transparency and Data-Sharing Among Bio-Pharmaceutical Companies and the Role of Company Size, Location, and Product Type: A Cross-Sectional Descriptive Analysis
<p><b>Objective</b>: To examine company characteristics associated with better transparency and to apply a tool used to measure and improve clinical trial transparency among large companies and drugs, to smaller companies and biologics.</p> <p><b>Design</b>: Cross-sectional descriptive analysis.</p> <p><b>Setting and participants. </b>Novel drugs and biologics FDA approved in 2016 and 2017, and their company sponsors.</p> <p>Using established Good Pharma Scorecard (GPS) measures, companies and products were evaluated on their clinical trial registration, results dissemination, and FDA Amendments Act (FDAAA) implementation; Companies were ranked using these measures and a multi-component data sharing measure. Associations between company transparency scores with company size (large vs non-large), location (US vs non-US), and sponsored product type (drug vs biologic) were also examined. 26% of products (16/62) had publicly available results for all clinical trials supporting their FDA approval and 67% (39/58) had public results for trials in patients by 6 months after their FDA approval; 58% (32/55) were FDAAA compliant. Large companies were significantly more transparent than non-large companies (overall median transparency score of 95% [IQR 91-100] vs 59% [IQR 41-70], p<0.001), attributable to higher FDAAA compliance (median of 100% [IQR 88-100] vs 57% [0-100], p=0.01) and better data sharing (median of 100% [IQR 80-100] vs 20% [IQR 20-40], p<0.01). No significant differences were observed by company location or product type. It was feasible to apply the GPS transparency measures and ranking tool to non-large companies and biologics. Large companies are significantly more transparent than non-large companies, driven by better data sharing procedures and implementation of FDAAA trial reporting requirements. Greater research transparency is needed, particularly among non-large companies, to maximize the benefits of research for patient care and scientific innovation. </p>
Supplementary data on car company reduction target analysis and GHG emission model
<p>This dataset contains supplementary information for the Master's thesis "<em>Greenhouse gas emission coverage and reduction potentials of scope 3 emissions in car manufacturing"</em> published on 16 November 2022 at Utrecht University. The data consists of two separately files, including the car company reduction target analysis and the GHG emission calculation model that were covered in the thesis report. The supplementary data can be used in combination with the final thesis report.</p>
Maintenance Data Set for Car Rental Company
<p>Maintenance records for a car rental company. The column details are provided as below:</p> <ul> <li><strong>CARID: </strong>Unique ID for each of the cars in the fleet</li> <li><strong>Brand:</strong> Brand of the car</li> <li><strong>Model :</strong> Model of the car </li> <li><strong>SubModel :</strong> Submodel for the brand and model of the car</li> <li><strong>Segment:</strong> there are segments for the cars ( for further information please refer to wikipedia : https://en.wikipedia.org/wiki/Car_classification ) </li> <li><strong>Year :</strong> Model year of the car</li> <li><strong>Fuel Type: </strong>Fuel consumption Type ( like Diesel, Petrol, Hybrid or Electric)</li> <li><strong>Gear : </strong>Gear type of the car</li> <li><strong>Milage:</strong> is the of the call of the maintenance (expected milage for the maintenance), the actual milage might be different than the scheduled one, this column indicates the scheduled milage for maintenance.</li> <li><strong>MaintenanceMile :</strong> is the actual mile during the call </li> <li>CallID: a unique id for each maintenance call. </li> <li>StockID: ID of the parts or services provided during the call</li> <li>Quantity: Quantity of the parts or duration of service or amount of items used during the maintenance</li> <li>UnitPrice : unit price for the parts, services or consumption</li> <li>SubTotal : Line bases subtotal for the invoice (the actual total value in US Dollars for the quantity and the unit price : unitprice x quantity)</li> <li>Discount: Line based discount for the stockid, quantity and unitprice</li> <li>Tax : Legal taxes in USD</li> <li>GrandTotal : invoice based total amount (total amount of each lines in the same invoice)</li> <li>InvoiceDate : Data of invoice in Year - Month - Day Hour - Minute - Seconds , you can skip the hour, minute and seconds </li> <li>VendorID: Unique ID of each vendor providing the maintenance service</li> <li>CityID: Location of the maintenance</li> <li>MaintenanceType : It can be a regular maintenance ( 0) , request based maintenance (1) or unknown (2) </li> </ul> <p> </p>
Data from: Sharing of clinical trial data and results reporting practices among large pharmaceutical companies: cross sectional descriptive study and pilot of a tool to improve company practices
Open the record for dataset details and reuse information.
Clinical Trial Transparency and Data-Sharing Among Bio-Pharmaceutical Companies and the Role of Company Size, Location, and Product Type: A Cross-Sectional Descriptive Analysis
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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