IntelligentMonitor: Empowering DevOps Environments With Advanced Monitoring and Observability aims to improve monitoring and observability in complex, distributed DevOps environments by leveraging machine learning and data analytics. This repository contains a sample implementation of the IntelligentMonitor system proposed in the research paper, presented and published as part of the 11th International Conference on Information Technology (ICIT 2023).

If you use this dataset and code or any herein modified part of it in any publication, please cite these papers:

P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.

For any questions and research queries - please reach out via Email.

Abstract - In the dynamic field of software development, DevOps has become a critical tool for enhancing collaboration, streamlining processes, and accelerating delivery. However, monitoring and observability within DevOps environments pose significant challenges, often leading to delayed issue detection, inefficient troubleshooting, and compromised service quality. These issues stem from DevOps environments' complex and ever-changing nature, where traditional monitoring tools often fall short, creating blind spots that can conceal performance issues or system failures. This research addresses these challenges by proposing an innovative approach to improve monitoring and observability in DevOps environments. Our solution, Intelligent-Monitor, leverages realtime data collection, intelligent analytics, and automated anomaly detection powered by advanced technologies such as machine learning and artificial intelligence. The experimental results demonstrate that IntelligentMonitor effectively manages data overload, reduces alert fatigue, and improves system visibility, thereby enhancing performance and reliability. For instance, the average CPU usage across all components showed a decrease of 9.10%, indicating improved CPU efficiency. Similarly, memory utilization and network traffic showed an average increase of 7.33% and 0.49%, respectively, suggesting more efficient use of resources. By providing deep insights into system performance and facilitating rapid issue resolution, this research contributes to the DevOps community by offering a comprehensive solution to one of its most pressing challenges. This fosters more efficient, reliable, and resilient software development and delivery processes.

Components The key components that would need to be implemented are:

• Data Collection - Collect performance metrics and log data from the distributed system components. Could use technology like Kafka or telemetry libraries.
• Data Processing - Preprocess and aggregate the collected data into an analyzable format. Could use Spark for distributed data processing.
• Anomaly Detection - Apply machine learning algorithms to detect anomalies in the performance metrics. Could use isolation forest or LSTM models.
• Alerting - Generate alerts when anomalies are detected. It could integrate with tools like PagerDuty.
• Visualization - Create dashboards to visualize system health and key metrics. Could use Grafana or Kibana.
• Data Storage - Store the collected metrics and log data. Could use Elasticsearch or InfluxDB.

Implementation Details The core of the implementation would involve the following: - Setting up the data collection pipelines. - Building and training anomaly detection ML models on historical data. - Developing a real-time data processing pipeline. - Creating an alerting framework that ties into the ML models. - Building visualizations and dashboards.

The code would need to handle scaled-out, distributed execution for production environments.

Proper code documentation, logging, and testing would be added throughout the implementation.

Usage Examples Usage examples could include:

• Running the data collection agents on each system component.
• Visualizing system metrics through Grafana dashboards.
• Investigating anomalies detected by the ML models.
• Tuning the alerting rules to minimize false positives.
• Correlating metrics with log data to troubleshoot issues.

References The implementation would follow the details provided in the original research paper: P. Thantharate, "IntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability," 2023 International Conference on Information Technology (ICIT), Amman, Jordan, 2023, pp. 800-805, doi: 10.1109/ICIT58056.2023.10226123.

Any additional external libraries or sources used would be properly cited.

Tags - DevOps, Software Development, Collaboration, Streamlini...

Job Offers Web Scraping Search

Targeted Results to Find the Optimal Work Solution

By [source]

About this dataset

This dataset collects job offers from web scraping which are filtered according to specific keywords, locations and times. This data gives users rich and precise search capabilities to uncover the best working solution for them. With the information collected, users can explore options that match with their personal situation, skillset and preferences in terms of location and schedule. The columns provide detailed information around job titles, employer names, locations, time frames as well as other necessary parameters so you can make a smart choice for your next career opportunity

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great resource for those looking to find an optimal work solution based on keywords, location and time parameters. With this information, users can quickly and easily search through job offers that best fit their needs. Here are some tips on how to use this dataset to its fullest potential:

• Start by identifying what type of job offer you want to find. The keyword column will help you narrow down your search by allowing you to search for job postings that contain the word or phrase you are looking for.

• Next, consider where the job is located – the Location column tells you where in the world each posting is from so make sure it’s somewhere that suits your needs!

• Finally, consider when the position is available – look at the Time frame column which gives an indication of when each posting was made as well as if it’s a full-time/ part-time role or even if it’s a casual/temporary position from day one so make sure it meets your requirements first before applying!

• Additionally, if details such as hours per week or further schedule information are important criteria then there is also info provided under Horari and Temps Oferta columns too! Now that all three criteria have been ticked off - key words, location and time frame - then take a look at Empresa (Company Name) and Nom_Oferta (Post Name) columns too in order to get an idea of who will be employing you should you land the gig!

  All these pieces of data put together should give any motivated individual all they need in order to seek out an optimal work solution - keep hunting good luck!

Research Ideas

• Machine learning can be used to groups job offers in order to facilitate the identification of similarities and differences between them. This could allow users to specifically target their search for a work solution.
• The data can be used to compare job offerings across different areas or types of jobs, enabling users to make better informed decisions in terms of their career options and goals.
• It may also provide an insight into the local job market, enabling companies and employers to identify where there is potential for new opportunities or possible trends that simply may have previously gone unnoticed

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: web_scraping_information_offers.csv | Column name | Description | |:-----------------|:------------------------------------| | Nom_Oferta | Name of the job offer. (String) | | Empresa | Company offering the job. (String) | | Ubicació | Location of the job offer. (String) | | Temps_Oferta | Time of the job offer. (String) | | Horari | Schedule of the job offer. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

Background Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.

The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition:

1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)
2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)
3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)
4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.

Available supplementary data: ACS cohort, Matched controls; ambulance, OMOP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from European Parliament hosted by the EU Open Data Portal. The Open Data Portal is found here and they update their information according the amount of data that is brought in. Explore European Parliament data using Kaggle and all of the data sources available through the European Parliament organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the EU ODP API and Kaggle's API.

This dataset is distributed under the following licenses: Dataset License

Cover photo by Tamara Menzi on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Analysis of ‘How Every NFL Team’s Fans Lean Politically?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nfl-fandome on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data behind the story How Every NFL Team’s Fans Lean Politically.

Google Trends Data

Google Trends data was derived from comparing 5-year search traffic for the 7 sports leagues we analyzed:

https://g.co/trends/5P8aa

Results are listed by designated market area (DMA).

The percentages are the approximate percentage of major-sports searches that were conducted for each league.

Trump's percentage is his share of the vote within the DMA in the 2016 presidential election.

SurveyMonkey Data

SurveyMonkey data was derived from a poll of American adults ages 18 and older, conducted between Sept. 1-7, 2017.

Listed numbers are the raw totals for respondents who ranked a given NFL team among their three favorites, and how many identified with a given party (further broken down by race). We also list the percentages of the entire sample that identified with each party, and were of each race.

The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.

Source: https://github.com/fivethirtyeight/data

This dataset was created by FiveThirtyEight and contains around 0 samples along with Unnamed: 10, Unnamed: 4, technical information and other features such as: - Unnamed: 3 - Unnamed: 1 - and more.

How to use this dataset

• Analyze Unnamed: 13 in relation to Unnamed: 21
• Study the influence of Unnamed: 7 on Unnamed: 12
• More datasets

Acknowledgements

If you use this dataset in your research, please credit FiveThirtyEight

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Access detailed insights with our Instagram datasets, featuring follower counts, verified status, account types, and engagement scores. Explore post information including URLs, descriptions, hashtags, comments, likes, media, posting dates, locations, and reel URLs. Perfect for understanding user engagement and content trends to drive informed decisions and optimize your social media strategies. Over 750M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Post Hashtags Following Posts Profile Image Link Profile URL Profile Name Highlights Count Highlights Full Name Is Private Bio Hashtags URL Is Joined Recently And much more

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

Here, we provide a dataset of images of interfaces from household appliances, where all interface elements are labled with one of five different types of interface elements. Further, we provide auxillary materials to use and extend the dataset.

MY NASA DATA (MND) is a tool that allows anyone to make use of satellite data that was previously unavailable.Through the use of MND’s Live Access Server (LAS) a multitude of charts, plots and graphs can be generated using a wide variety of constraints. This site provides a large number of lesson plans with a wide variety of topics, all with the students in mind. Not only can you use our lesson plans, you can use the LAS to improve the ones that you are currently implementing in your classroom.

This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW9, RW10, RW11 and RW12) were continuously operated using a sequence of charging and discharging currents between -4.5A and 4.5A. This type of charging and discharging operation is referred to here as random walk (RW) operation. Each of the loading periods lasted 5 minutes, and after 1500 periods (about 5 days) a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.

This dataset includes U.S. low-temperature heating and cooling demand at the county level in major end-use sectors: residential, commercial, manufacturing, agricultural, and data centers. Census division-level end-use energy consumption, expenditure, and commissioned power database were dis-aggregated to the county level. The county-level database was incorporated with climate zone, numbers of housing units and farms, farm size, and coefficient of performance (COP) for heating and cooling demand analysis. This dataset also includes a paper containing a full explanation of the methodologies used and maps. Residential data were updated from the latest Residential Energy Consumption Survey (RECS) dataset (2015) using 2020 census data. Commercial data were baselined off the latest Commercial Building Energy Consumption Survey (CBECS) dataset (2012). Manufacturing data were baselined off the latest Manufacturing Energy Consumption Survey (MECS) dataset (2021).

This dataset represents the spatial overlap in areas with present (cropland, grazing, and recreation), potential (petroleum resources), or future (population) low-intensity land-use. The final areas designated as low-intensity land-use represent low intensity relative to the range of values for that variable in this region, either present or future, and do not represent the rate of change, recent or future, for that particular variable. We defined low-intensity areas as ≤25% quantile for that variable over the entire Colorado Plateau. We combined grazing and cropland low-intensity area into one “low agriculture” variable. Each band represents a unique overlap between the land-use types.

The Open Government Data portals (OGD) thanks to the presence of thousands of geo-referenced datasets, containing spatial information, are of extreme interest for any analysis or process relating to the territory. For this to happen, users must be enabled to access these datasets and reuse them. An element often considered hindering the full dissemination of OGD data is the quality of their metadata. Starting from an experimental investigation conducted on over 160,000 geospatial datasets belonging to six national and international OGD portals, this work has as its first objective to provide an overview of the usage of these portals measured in terms of datasets views and downloads. Furthermore, to assess the possible influence of the quality of the metadata on the use of geospatial datasets, an assessment of the metadata for each dataset was carried out, and the correlation between these two variables was measured. The results obtained showed a significant underutilization of geospatial datasets and a generally poor quality of their metadata. Besides, a weak correlation was found between the use and quality of the metadata, not such as to assert with certainty that the latter is a determining factor of the former.

The dataset consists of six zipped CSV files, containing the collected datasets' usage data, full metadata, and computed quality values, for about 160,000 geospatial datasets belonging to the three national and three international portals considered in the study, i.e. US (catalog.data.gov), Colombia (datos.gov.co), Ireland (data.gov.ie), HDX (data.humdata.org), EUODP (data.europa.eu), and NASA (data.nasa.gov).

Data collection occurred in the period: 2019-12-19 -- 2019-12-23.

The header for each CSV file is:

[ ,portalid,id,downloaddate,metadata,overallq,qvalues,assessdate,dviews,downloads,engine,admindomain]

where for each row (a portal's dataset) the following fields are defined as follows:

• portalid: portal identifier
• id: dataset identifier
• downloaddate: date of data collection
• metadata: the overall dataset's metadata downloaded via API from the portal according to the supporting platform schema
• overallq: overall quality values computed by applying the methodology presented in [1]
• qvalues: json object containing the quality values computed for the 17 metrics presented in [1]
• assessdate: date of quality assessment
• dviews: number of total views for the dataset
• downloads: number of total downloads for the dataset (made available only by the Colombia, HDX, and NASA portals)
• engine: identifier of the supporting portal platform: 1(CKAN), 2 (Socrata)
• admindomain: 1 (national), 2 (international)

[1] Neumaier, S.; Umbrich, J.; Polleres, A. Automated Quality Assessment of Metadata Across Open Data Portals.J. Data and Information Quality2016,8, 2:1–2:29. doi:10.1145/2964909

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

This file contains raw data for cameras and wearables of the ConfLab dataset.

./cameras

contains the overhead video recordings for 9 cameras (cam2-10) in MP4 files.

These cameras cover the whole interaction floor, with camera 2 capturing the

bottom of the scene layout, and camera 10 capturing top of the scene layout.

Note that cam5 ran out of battery before the other cameras and thus the recordings

are cut short. However, cam4 and 6 contain significant overlap with cam 5, to

reconstruct any information needed.

Note that the annotations are made and provided in 2 minute segments.

The annotated portions of the video include the last 3min38sec of x2xxx.MP4

video files, and the first 12 min of x3xxx.MP4 files for cameras (2,4,6,8,10),

with "x" being the placeholder character in the mp4 file names. If one wishes

to separate the video into 2 min segments as we did, the "video-splitting.sh"

script is provided.

./camera-calibration contains the camera instrinsic files obtained from

https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters can

be calculated using the existing intrinsic parameters and the instructions in the

multicamera-calibration repo. The coordinates in the image are provided by the

crosses marked on the floor, which are visible in the video recordings.

The crosses are 1m apart (=100cm).

./wearables

subdirectory includes the IMU, proximity and audio data from each

participant at the Conflab event (48 in total). In the directory numbered

by participant ID, the following data are included:

1. raw audio file

2. proximity (bluetooth) pings (RSSI) file (raw and csv) and a visualization

3. Tri-axial accelerometer data (raw and csv) and a visualization

4. Tri-axial gyroscope data (raw and csv) and a visualization

5. Tri-axial magnetometer data (raw and csv) and a visualization

6. Game rotation vector (raw and csv), recorded in quaternions.

All files are timestamped.

The sampling frequencies are:

- audio: 1250 Hz

- rest: around 50Hz. However, the sample rate is not fixed

and instead the timestamps should be used.

For rotation, the game rotation vector's output frequency is limited by the

actual sampling frequency of the magnetometer. For more information, please refer to

https://invensense.tdk.com/wp-content/uploads/2016/06/DS-000189-ICM-20948-v1.3.pdf

Audio files in this folder are in raw binary form. The following can be used to convert

them to WAV files (1250Hz):

ffmpeg -f s16le -ar 1250 -ac 1 -i /path/to/audio/file

Synchronization of cameras and werables data

Raw videos contain timecode information which matches the timestamps of the data in

the "wearables" folder. The starting timecode of a video can be read as:

ffprobe -hide_banner -show_streams -i /path/to/video

./audio

./sync: contains wav files per each subject

./sync_files: auxiliary csv files used to sync the audio. Can be used to improve the synchronization.

The code used for syncing the audio can be found here:

https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/audio

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

Student Performance Data

This dataset provides insights into various factors influencing the academic performance of students. It is curated for use in educational research, data analytics projects, and predictive modeling. The data reflects a combination of personal, familial, and academic-related variables gathered through observation or survey.

The dataset includes a diverse range of students and captures key characteristics such as study habits, family background, school attendance, and overall performance. It is well-suited for exploring correlations, visualizing trends, and training machine learning models related to academic outcomes.

Highlights:

Clean, structured format suitable for immediate use Designed for beginner to intermediate-level data analysis Valuable for classification, regression, and data storytelling projects

File Format:

Type: CSV (Comma-Separated Values) Encoding: UTF-8 Structure: Each row represents a student record

Applications

Student performance prediction Educational policy planning Identification of performance gaps and influencing factors Exploratory data analysis and visualization