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180 results for “job”
Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses
<p>Dataset used in the publication: Santos, A. C., Arriaga, P., & Simões, C. (2021). Catching the audience in a job interview: Effects of emotion regulation strategies on subjective, physiological, and behavioural responses. Biological Psychology, 162, 108089. <a href="https://doi.org/10.1016/j.biopsycho.2021.108089" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.biopsycho.2021.108089</a></p> <p>Includes: data in SPSS and CSV and codebook. </p> <p>In the emotion regulation process more than one strategy is often used, though studies continue to rely on the manipulation of one strategy alone. This study compares the effects of Combined Cognitive Reappraisal (CCR: acceptance and reappraise via perspective-taking) and suppression using the Trier Social Stress Test (TSST). One hundred participants were randomly assigned to one of the two groups and subjective, physiological, and behavioural data were recorded. Continuous electrocardiography was recorded to measure heart rate variability (HRV) and stress levels. Affective ratings were provided before and after the TSST. Behavioural expressions were videotaped and analysed independently. Trait social anxiety/fear, age and gender entered as covariates. Although no group differences were found on affective ratings, the CCR group presented less physiological stress, higher HRV, their speech was better perceived, displayed more affiliative smile and hand gestures. Results suggested that CCR is more appropriate than suppression for managing social stress situations.</p>
Job applications for positions in the Napoleonic administration (1800-1815)
<p>Dataset of application letters for positions in the Napoleonic administration (prefect, sub-prefect, secretary general, and counselor of the prefecture) in the French Republic and later the French Empire, drafted by a sample of 330 French and Italian candidates. The 800 applications are sourced from the French National Archives, collection F1dII. The table contains metadata extracted by the researcher. Column headers include a legend explaining the content where necessary. For columns where this is not specified, the content should be interpreted as follows. For instance, in the column concerning the candidates' previous experience as prefects or sub-prefects: "SPsi" = yes, the candidate has served as a sub-prefect; "SPno" = no experience as a sub-prefect. Other columns follow a similar structure. The final column contains full transcriptions of the texts, without lemmatization, preserving the original spelling. The concluding salutations in the letters have not been transcribed.</p>
Data Science job offers in Euraxess.
<p>It is not always easy to find job opportunities if you are interested in beginning to do research in a certain field. In this sense, having an up-to-date dataset with job offers in your field of interest would simplify this search. This dataset could be generated using web scraping methods.</p> <p>Although the web scraper we built could be applied to every field, in this project we focused in opportunities related with data science (i.e. data scientist, data analyst, data engineer...) published on <a href="https://euraxess.ec.europa.eu/">EURAXESS</a>.</p> <p>The dataset generated with this package contains job offers obtained from EURAXESS. Each row of the dataset contains different job offers and its attributes. In the example table showed below, the dataset was obtained using "Data Scientist" as keyword, but another keywords would result in different datasets. The columns describing the dataset are:</p> <ul> <li>Job Offer Title: Title of the job offer.</li> <li>Researcher Profile: Expected applicant profile/s.</li> <li>Company: Company offering the job.</li> <li>Hours/Week: Weekly working hours.</li> <li>Country: Country where the job is offered.</li> <li>City: City where the job is offered.</li> <li>Where to Apply: Url or email where to apply to the offer.</li> <li>More info: URL where the offer can be located.</li> </ul> <p>Dataset generated by web scraping methods: https://github.com/avicenteg/euraxess_scraping</p>
TalentCLEF 2025 corpus: Skill and Job Title Intelligence for Human Capital Management
<blockquote> <p><strong>🚨 Current Status: Submission of Working Notes</strong></p> </blockquote> <p><strong>If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo: </strong></p> <pre><code>@inproceedings{gasco2025overview, title={{Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management}}, author={Gasco, Luis and Fabregat, Hermenegildo and Garc\'{\i}a-Sardi{\~n}a, Laura and Estrella, Paula and Deniz, Daniel and Rodrigo, \'{A}lvaro and Zbib, Rabih}, booktitle={{International Conference of the Cross-Language Evaluation Forum for European Languages}}, year={2025}, publisher={Springer} }</code></pre> <p> </p> <h2><strong><a href="https://talentclef.github.io/talentclef/" target="_blank" rel="noopener">TalentCLEF2025</a> corpus - </strong>Task B Test set release</h2> <h3><strong>Introduction:</strong></h3> <p>The first edition of TalentCLEF aims to develop and evaluate models designed to facilitate three essential tasks:</p> <ol> <li>Finding/ranking candidates for job positions based on their experience and professional skills.</li> <li>Implementing upskilling and reskilling strategies that promote the coninuous development of workers</li> <li>Detecting emerging skills and skills gaps of importance in organizations.</li> </ol> <p>With that aim, the task is divided into two tasks: </p> <ul> <li><strong>Task A - Multilingual Job Title Matching</strong>. This task involves developing systems to identify and rank the job titles most similar to a given one by generating a ranked list of similar titles from a specified knowledge base for each job title in a provided test set.</li> <li><strong>Task B - Job Title-Based Skill Prediction. </strong>Task B requires developing systems that can retrieve relevant skills associated with a specified job title.</li> </ul> <div> <p>This data repository contains the data for these two tasks. The data is being released progressively according to the<a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/schedule/"> task schedule</a>.</p> <p>The task evaluation takes place on Codabench (<a href="https://www.codabench.org/competitions/5842/">Task A</a> and <a href="https://www.codabench.org/competitions/7059/">Task B</a>). Participants must register for the competition through <a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a> to be part of the evaluation campaign.</p> <p> </p> </div> <h3><strong>File structure: </strong></h3> <div> <div> <div> <div> <blockquote> <p>For a detailed description of the data structure, you can refer to the <a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/description_corpus/">TalentCLEF2025 data description page,</a> where it is thoroughly explained.</p> </blockquote> </div> </div> </div> </div> <p>The files is organized into two <code>*.zip</code> files, <code>TaskA.zip</code> and <code>TaskB.zip</code>, each containing training, validation and test folders to support different stages of model development. So far, only the training set for both tasks has been released, but in future releases, as the tasks progress, additional data will be added to the different subfolders for each task.</p> <p><strong>TaskA</strong> includes language-specific subfolders within the training and validation directories, covering English, Spanish, German, and Chinese job title data. The training folders for TaskA contain language-specific .tsv files for each respective language. Validation folders include three essential files—queries, corpus_elements, and q_rels—for evaluating model relevance to search queries. TaskA’s test folder has queries and corpus_elements files for testing retrieval.<br><br></p> <pre><code>TaskA/ │ ├── training/ │ ├── english/ │ │ └── taskA_training_en.tsv │ ├── spanish/ │ │ └── taskA_training_es.tsv │ └── german/ │ └── taskA_training_de.tsv │ ├── validation/ │ ├── english/ │ │ ├── queries │ │ ├── corpus_elements │ │ └── qrels │ ├── spanish/ │ ├── german/ │ └── chinese/ │ └── test/ ├── english/ │ ├── queries │ └── corpus_elements ├── spanish/ ├── german/ └── chinese/ </code></pre> <p><strong>TaskB</strong> follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published.</p> <pre><code>TaskB/ │ ├── training/ │ ├── job2skill.tsv │ ├── jobid2terms.json │ └── skillid2terms.json<br>│ ├── validation/ │ ├── queries │ ├── corpus_elements │ └── qrels │ └── test/ ├── queries └── corpus_elements </code></pre> <p><strong>Tutorials:</strong></p> <table> <tbody> <tr> <td><strong>Notebook</strong></td> <td>Link</td> </tr> <tr> <td>Data Download and Load using Python </td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_data_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> <tr> <td>Task A - Development set Baseline generation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskA_DevSet_Baseline.ipynb">Link to Colab</a></td> </tr> <tr> <td>Task B - Prepare submission file and run evaluation</td> <td><a href="https://colab.research.google.com/github/TalentCLEF/talentclef_tutorials/blob/main/talentclef2025/TalentCLEF_TaskB_submission_creation_tutorial.ipynb" target="_blank" rel="noopener">Link to Colab</a></td> </tr> </tbody> </table> <p><strong>Resources: </strong></p> <ul> <li><a href="https://talentclef.github.io/talentclef/docs/" target="_blank" rel="noopener">Web</a></li> <li><a href="https://clef2025-labs-registration.dei.unipd.it/">CLEF Lab Registration Page</a></li> <li><a href="https://www.codabench.org/competitions/5842/">Codabench Task A</a></li> <li><a href="https://www.codabench.org/competitions/7059/">Codabench Task B</a></li> <li><a href="https://talentclef.github.io/talentclef/docs/talentclef-2025/data/additional_resources/">Additional Resources</a></li> </ul>
Replication package for the paper "A configurational approach to job quality analysis: forms of inequalities at work in Europe"
<p>The following replication package is appended to the article <span><span><span><span>Étienne Penissat</span><span>, </span></span><span><span>Cécile Rodrigues</span><span> & </span></span><span><span>Alexis Spire</span></span></span></span> <span>(2024)</span> "<span>A configurational approach to job quality analysis: forms of inequalities at work in Europe",</span> <span>European Societies,</span> <span>DOI: <a href="https://doi.org/10.1080/14616696.2024.2312950">10.1080/14616696.2024.2312950</a></span></p> <p>The scripts to be run in the following order are:</p> <p>- 1_Penissat_EuropeanSocieties_2023_DataPreparation.R : Recoding, formatting and scope of data used</p> <p>- 2_Penissat_EuropeanSocieties_2023_DataAnalysis.Rmd : Analysis and statistical results</p> <p>The data used in the article comes from the EWCS (2015) - European Working Condition Survey - provided by the European foundation for the improvement of living and working conditions. The data is not available on free access but can be obtained on request. Information on the survey wave used can be found here : https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015</p> <p>- In the first "1_Penissat_EuropeanSocieties_DataPreparation.R" script, the file containing data named "ewcs_1991-2015.dta" is used. The file called "eseg2_trad.csv" contains english labels for the nomenclature of professional positions ESeG. As "ewcs_1991-2015.dta" is not freely available, it is not included in the package and "eseg2_trad.csv" is located in the "data" folder.</p> <p>- The first script creates the data file "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" in the "results" folder.</p> <p>- The second script called "2_Penissat_EuropeanSocieties_DataAnalysis.Rmd" uses the "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" data file and produces the "2_Penissat_EuropeanSocieties_DataAnalysis.html" file containing all code and results presented in the article.</p>
Understanding Organizational Commitment and its Factors Influencing the Nurse's Job Satisfaction in Hospitals- A Systematic Literature Review and Further Research Agendas
<div> <p><em><span>Organizational commitment is a crucial concept when it comes to human resource management and Organizational behavior. It has to do with how much a worker commits to and identifies with the goals, values, and purposes of their company. Elevated levels of Organizational commitment are associated with enhanced job satisfaction, less attrition, and better performance. The expected ideal condition, status, and research deficit are all included in this article. A research agenda is determined by applying the ABCD framework to qualitatively analyze the identified research gap.The paper documents the topic and provides helpful information about it, which will aid future scholars. </span></em></p> </div>
Data Steward Professional: Reference dataset of Data Steward related job vacancies for competences assessment
<p>Data Stewardship vacancies collection to support FAIRsFAIR Data<br> Stewardship Professional Competence Framework<br> <br> This dataset is provided to validate and support the analysis of Data<br> Stewardship competences.<br> The dataset includes a collection of vacancies from the popular job search<br> website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Data Steward".<br> <br> <strong>Acknowledgment</strong><br> The research leading to these results has received<br> funding from the Horizon2020 projects FAIRsFAIR<br> (grant number 831558)<br> <br> <strong>References</strong><br> FAIRsFAIR Project Deliverable D7.3 Data Stewardship<br> Professional Competence Framework, Work in Progress.<br> To be published Feb 2021<br> Yuri Demchenko, Lennart Stoy, Research Data Management and Data<br> Stewardship Competences in University Curriculum, In Proc. Data Science<br> Education (DSE), Special Session, EDUCON2021 – IEEE Global Engineering<br> Education Conference, 21-23 April 2021, Vienna, Austria</p>
Gamification in Software Engineering: The Mediating Role of Developer Engagement and Job Satisfaction
<p>Replication package with covariance matrices (instead of original dataset) and R script.</p>
Performance of different augmented writing tools on german job ads
<p>This dataset was created as part of an empirical analysis of four German-language augmented writing technologies for detecting gender exclusion. For this purpose, approximately 160,000 job postings from three different platforms were collected and evaluated using the technologies. The dataset primarily contains the number of expressions extracted per job posting, as well as the gender scores and categories calculated by the technologies. Together with variable descriptions and the list of keywords used to sample the leading positions, this dataset serves as additional information for a manuscript under review.</p>
Artificial Intelligence: Professional reference dataset of Artificial Intelligence professional competences analysis based on the job market
<p>Artificial Intelligence vacancies collection to support FAIRsFAIR Artificial Intelligence Professional Competences<br> <br> This dataset is provided as validation and support for the analysis of Artificial Intelligence competences.<br> The dataset includes a collection of vacancies from the job application<br> website <a href="http://indeed.com/">indeed.com</a> that responded to the search term "Artificial Intelligence".</p> <p>The used search term could be easily adjusted in the provided code at <a href="https://github.com/atomcracker/Competence_analysis.git">Github Repository</a>. The heavy extensive research analysis is reflected in graphs, described and reflected in <a href="https://scripties.uba.uva.nl/search?id=727184">Thesis</a>.</p>
IoT nodes movement and job requests
<p>This dataset contains information about the movements of IoT nodes in an urban area with a 22KM x 8 KM dimension. The workload that is created by these nodes is originated from the requests submitted by these IoT nodes. Every event is logged with a timestamp along with other required information. Dataset files are in CSV format.</p>
Job workload data and transaction logs for the eBlocBroker
<p><strong>* What is eBlocBroker?</strong></p> <p>eBlocBroker is a blockchain based autonomous computational resource broker.</p> <p><strong>* Job workload data and transaction logs for the eBlocBroker</strong></p> <p>This repository contains job workload data and test results for the paper titled: <em>"eBlocBroker: A Blockchain Based Autonomous Computational Resource Broker"</em>. eBlocBroker and its driver programs are available from the following GitHub repository: <a href="https://github.com/ebloc/ebloc-broker">https://github.com/ebloc/ebloc-broker</a>.</p> <p>We first deployed our eBlocBroker contract on bloxberg. Then we tested eBlocBroker and the robustness of our Python scripts that allow provider and requester nodes to interact with each other through eBlocBroker and cloud storage services using two types of synthetic CPU workloads explained as follows:</p> <ol> <li>This workload helps to test running source code in combination with already cached and not cached datasets on the provider. As the source code, the <a href="https://dl.acm.org/doi/abs/10.1145/3330481">cppr</a> (colored parallel rush-relabel algorithm) is used, which runs with additional datasets. Three cppr processes run one after another with different randomly selected datasets. All four providers have the same 12 medium-size datasets, of which only 3 distinct ones from each other have lower prices. Two data files are the provider’s registered data, and one is from the requester’s local storage.</li> <li>The <a href="https://www.nas.nasa.gov/software/npb.html">NAS Parallel Benchmarks</a>, a small group of programs targeting the performance evaluation of parallel supercomputers. One of the NAS serialized benchmarks in Class B (Block Tridiagonal solver, Scalar Pentadiagonal solver, Unstructured Adaptive mesh, and Lower-Upper Gauss-Seidel solver) is selected randomly. Since providers’ prices are the same, the calculated cost for NAS jobs will be the same for all providers.</li> </ol> <p>In the test, our helper Python script maintains one hundred synthetic requesters within the requester node continually submit the following chosen workload randomly for 14 hours and 30 minutes.</p> <p>This record provides logs of clusters and clients, results of the submitted jobs on each cluster that is either completed of failed and their gained and returned fees, logs of the submitted jobs and their transaction hashes, and Slurm's job submission information, which are generated by the Driver programs.</p> <p><strong>** Transactions are taken from bloxberg (<a href="https://blockexplorer.bloxberg.org">https://blockexplorer.bloxberg.org</a>)</strong></p> <p>- Transactions deployed on the eBlocBroker Smart Contract:<br> <a href="https://blockexplorer.bloxberg.org/address/0xa0Fac3232234478E6A0d4d5564ed239c956A21f0/transactions">https://blockexplorer.bloxberg.org/address/0xa0Fac3232234478E6A0d4d5564ed239c956A21f0/transactions</a></p> <p>- <a href="https://blockexplorer.bloxberg.org/address/0x29e613B04125c16db3f3613563bFdd0BA24Cb629/transactions">Transactions of the <em>provider0_0x29e613B04125c16db3f3613563bFdd0BA24Cb629</em></a><br> <em>- </em><a href="https://blockexplorer.bloxberg.org/address/0x1926b36af775e1312fdebcc46303ecae50d945af/transactions">Transactions of the <em>provider1_0x1926b36af775e1312fdebcc46303ecae50d945af</em></a><br> - <a href="https://blockexplorer.bloxberg.org/address/0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24/transactions">Transactions of the<em> provider2_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24</em></a> <br> - <a href="https://blockexplorer.bloxberg.org/address/0x51e2b36469cdbf58863db70cc38652da84d20c67/transactions">Transactions of the <em>provider3_0x51e2b36469cdbf58863db70cc38652da84d20c67</em></a> </p> <p><strong>* Files</strong></p> <p>Each provider contains <em>eudat, gdrive, ipfs, and ipfs_gpg </em>folders that contains the patch results that obtained from the named cloud storage.</p> <p>$ tree -L 2 .<br> ├── README.org<br> ├── base_test_eblocbroker<br> │ ├── NPB3.3-SER_source_code<br> │ ├── README.md<br> │ ├── _cppr<br> │ ├── cppr<br> │ ├── cppr_example.sh<br> │ ├── datasets<br> │ ├── run_cppr<br> │ ├── setup.sh<br> │ └── test_data<br> ├── check_list.org<br> ├── provider0_0x29e613B04125c16db3f3613563bFdd0BA24Cb629<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x29e613b04125c16db3f3613563bfdd0ba24cb629.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x29e613B04125c16db3f3613563bFdd0BA24Cb629.csv<br> │ └── watch_0x29e613b04125c16db3f3613563bfdd0ba24cb629.out<br> ├── provider1_0x1926b36af775e1312fdebcc46303ecae50d945af<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x1926b36af775e1312fdebcc46303ecae50d945af.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x1926b36af775e1312fdebcc46303ecae50d945af.csv<br> │ └── watch_0x1926b36af775e1312fdebcc46303ecae50d945af.out<br> ├── provider2_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x4934a70ba8c1c3acfa72e809118bdd9048563a24.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24.csv<br> │ └── watch_0x4934a70ba8c1c3acfa72e809118bdd9048563a24.out<br> ├── provider3_0x51e2b36469cdbf58863db70cc38652da84d20c67<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x51e2b36469cdbf58863db70cc38652da84d20c67.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x51e2b36469cdbf58863db70cc38652da84d20c67.csv<br> │ └── watch_0x51e2b36469cdbf58863db70cc38652da84d20c67.out<br> ├── requesters<br> │ ├── ebloc-broker-logs<br> │ └── gdrive<br> └── transactions_contract_0xa0Fac3232234478E6A0d4d5564ed239c956A21f0.csv</p>
Job-Related Health Issues That Affect Employees Working in Pharmaceutical Marketing
<p><strong><span>Abstract</span></strong></p> <p><span>Marketing representatives play a critical role in a pharmaceutical organization in the development and sustainability of their business through helping in selling of products and services. It is not an easy job. The Medical Marketing representative is a high-risk job with immense stress and negative consequences for individuals. It requires more skills, wider knowledge and emotional stability than the other profession. Due to extensive traveling, wandering and waiting time, target issues, work-life balancing problems and lack of job security the medical representative feels exhausted.</span></p> <p><span>In India, the pharmaceutical industry is growing tremendously for the past few years. This industry is highly competitive in nature. It increases the need for the marketing representatives and also their roles and responsibilities simultaneously. The cut-throat competitive scenario in the market increases the pressure of achieving targets to pharmaceutical sales which ultimately induced their job stress and other health issues. So, the pharmacy institution must provide more attention to prevent and reduce the burnout of the marketing representatives, otherwise the institution will lose its reputation.</span></p> <p><strong><span>Keywords: </span></strong><span>occupational risks; pharmaceutical marketing force; road traffic accidents; violence; workplace stress; Burnout; Frustrating; Medical Representatives; Stress and Work-Life Balancing Problems; stress management Violence; Well-being.</span></p>
30000 job postings from SEEK Australia
<p>SEEK is Australia's number one employment marketplace. This dataset contains 30000 job postings from SEEK Australia website.</p> <p>Following data fields are included in the dataset:</p> <ul> <li>category</li> <li>city</li> <li>state</li> <li>company name</li> <li>job title</li> <li>job description</li> <li>job type</li> <li>salary offered</li> <li>posting date</li> </ul> <p><strong>Objectives</strong></p> <p>The uses of this dataset are endless. Some of the inspirations could be:</p> <ul> <li>Top paying companies</li> <li>Highest number of job posting by a particular company</li> <li>State/City with highest job openings</li> <li>Salary distribution by state</li> <li>Ratio of different job types</li> </ul> <p><strong>Background</strong></p> <p>This data was extracted using <a href="https://jobspikr.com/?utm_source=rb-zenodo&utm_medium=referral&utm_campaign=seek-dataset">JobsPikr</a> - a job data delivery platform that extracts job data from various company sites across the globe on daily basis powered by machine learning techniques.</p>
30000+ job postings from eMedCareers (Europe)
<p>eMedCareers is a job search platform for pharmaceutical, biotechnology or healthcare jobs. This dataset contains 30000 latest job postings in Europe from eMedCareers job portal.</p> <p>Following data fields are included in the dataset:</p> <ul> <li>category</li> <li>location</li> <li>company name</li> <li>job title</li> <li>job description</li> <li>job type</li> <li>salary offered</li> <li>posting date</li> </ul> <p><strong>Objectives</strong></p> <p>The uses of this dataset are endless. Some of the inspirations could be:</p> <ul> <li>Top paying companies</li> <li>Highest number of job posting by a particular company</li> <li>State/City with highest job openings</li> <li>Salary distribution by state</li> <li>Ratio of different job types</li> </ul> <p><strong>Background</strong></p> <p>This data was extracted using <a href="https://jobspikr.com/?utm_source=rb-zenodo&utm_medium=referral&utm_campaign=seek-dataset">JobsPikr</a> - a job data delivery platform that extracts job data from various company sites across the globe on daily basis powered by machine learning techniques.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Instances of parallel related machine scheduling problem with jobs release dates and deadlines
<p>The following dataset contains randomly generated problem instances for the related machine scheduling problem with job's release dates and due dates.<br> There are 4 instance classes each of which contains 10 random instances (i.e., 10 text files) with n=120 jobs and m=26 machines. The instance classes differ one from another in the expected relative width of job's time windows.</p> <p>The text file structure is as follows:<br> n=<number of jobs>;<br> m=<number of machines>;<br> p_{ij}=<br> <n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i>;<br> r_j=<br> <vector with n elements: the j-th element corresponds to the release date of job j>;<br> d_j=<br> <vector with n elements: the j-th element corresponds to the due date of job j>;</p> <p>The jobs are sorted by release date in ascending order.</p>
Data curation materials in "Daily life in the Open Biologist's second job, as a Data Curator"
<p>This is the supplementary material accompanying the manuscript "Daily life in the Open Biologist’s second job, as a Data Curator", published in <a href="https://doi.org/10.12688/wellcomeopenres.22899.1">Wellcome Open Research</a>. </p> <p>It contains:</p> <p><strong>- Python_scripts.zip</strong>: Python scripts used for data cleaning and organization:</p> <p> -add_headers.py: adds specified headers automatically to a list of csv files, creating new output files containing a "_with_headers" suffix.</p> <p> -count_NaN_values.py: counts the total number of rows containing null values in a csv file and prints the location of null values in the (row, column) format.</p> <p> -remove_rowsNaN_file.py: removes rows containing null values in a single csv file and saves the modified file with a "_dropNaN" suffix.</p> <p> -remove_rowsNaN_list.py: removes rows containing null values in list of csv files and saves the modified files with a "_dropNaN" suffix.</p> <p><strong>- README_template.txt</strong>: a template for a README file to be used to describe and accompany a dataset. </p> <p><strong>- template_for_source_data_information.xlsx</strong>: a spreadsheet to help manuscript authors to keep track of data used for each figure (e.g., information about data location and links to dataset description).</p> <p><strong>- Supplementary_Figure_1.tif</strong>: Example of a dataset shared by us on Zenodo. The elements that make the dataset FAIR are indicated by the respective letters. Findability (F) is achieved by the dataset unique and persistent identifier (DOI), as well as by the related identifiers for the publication and dataset on GitHub. Additionally, the dataset is described with rich metadata, (e.g., keywords). Accessibility (A) is achieved by the ease of visualization and downloading using a standardised communications protocol (https). Also, the metadata are publicly accessible and licensed under the public domain. Interoperability (I) is achieved by the open formats used (CSV; R), and metadata are harvestable using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), a low-barrier mechanism for repository interoperability. Reusability (R) is achieved by the complete description of the data with metadata in README files and links to the related publication (which contains more detailed information, as well as links to protocols on protocols.io). The dataset has a clear and accessible data usage license (CC-BY 4.0).</p>
Astra Job Data Set
<p>Jobs run on Astra while it was operating from January 20, 2019 to September 14, 2019. See the dataset description files in the archive for additional details.</p>
Adastra jobs MI250 15days
<p>Adastra jobs MI250 15days</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.