Note:- Only publicly available data can be worked upon

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This repository contains supplementary materials for the following journal paper:

Valdemar Švábenský, Jan Vykopal, Pavel Seda, Pavel Čeleda. Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training. In Elsevier Data in Brief. 2021. https://doi.org/10.1016/j.dib.2021.107398

How to cite

If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).

@article{Svabensky2021dataset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Seda, Pavel and \v{C}eleda, Pavel}, title = {{Dataset of Shell Commands Used by Participants of Hands-on Cybersecurity Training}}, journal = {Data in Brief}, publisher = {Elsevier}, volume = {38}, year = {2021}, issn = {2352-3409}, url = {https://doi.org/10.1016/j.dib.2021.107398}, doi = {10.1016/j.dib.2021.107398}, }

The data were collected using a logging toolset referenced here.

Attached content

Dataset (data.zip). The collected data are attached here on Zenodo. A copy is also available in this repository.

Analytical tools (toolset.zip). To analyze the data, you can instantiate the toolset or this project for ELK.

Version history

Version 1 (https://zenodo.org/record/5137355) contains 13446 log records from 175 trainees. These data are precisely those that are described in the associated journal paper. Version 1 provides a snapshot of the state when the article was published.

Version 2 (https://zenodo.org/record/5517479) contains 13446 log records from 175 trainees. The data are unchanged from Version 1, but the analytical toolset includes a minor fix.

Version 3 (https://zenodo.org/record/6670113) contains 21762 log records from 275 trainees. It is a superset of Version 2, with newly collected data added to the dataset.

The current Version 4 (https://zenodo.org/record/8136017) contains 21459 log records from 275 trainees. Compared to Version 3, we cleaned 303 invalid/duplicate command records.

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

Sports data utilization solution Our device is a high-performance sensor system that precisely collects movement data of soccer players. It is a shin guard-type device that measures the movement of both feet 200 times per second with an ultra-precision sensor.Data type• 9-axis sensor data (acceleration 3-axis, gyro 3-axis, geomagnetic 3-axis)• Foot gap through UWB sensor• GPS-based location data (latitude, longitude)Main application areas ✅ Sports science research institute: Player performance… See the full description on the dataset page: https://huggingface.co/datasets/sa21c/sgs_g21_data.

Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA; commonly known as “drones,” unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost-effective way to collect high-quality wildlife population data, the validity of these claims is unclear. Using life-sized, replica seabird colonies containing a known number of fake birds, we assessed the accuracy of RPA-facilitated wildlife population monitoring compared to the traditional ground-based counting method. The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. We show that RPA-derived data are, on average, between 43% and 96% more accurate than the traditional ground-based data collection method. We also demonstrate that counts from this remotely sensed imagery can be semi-automated with a high degree of accuracy. The increased accuracy and increased precision of RPA-derived wildlife monitoring data provides greater statistical power to detect fine-scale population fluctuations allowing for more informed and proactive ecological management.

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2019 at six off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS, site terminated in 2013), Crocker Reef (FKNMS, site added in 2013), Sombrero Reef (FKNMS), Pulaski Shoal Light(Dry Tortugas National Park), and Pulaski Shoal West (Dry Tortugas National Park, site added in 2016). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner and others (2013).

Snapshot img

The data integration and data quality tools market size has the potential to grow by USD 843.29 million during 2020-2024, and the market’s growth momentum will decelerate during the forecast period.

This report provides a detailed analysis of the market by end-user (large enterprises, government organizations, and SME) and geography (North America, Europe, APAC, South America, and MEA). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including Data Ladder, Experian Plc, HCL Technologies Ltd., International Business Machines Corp., Informatica LLC, Oracle Corp., Precisely, SAP SE, SAS Institute Inc., and Talend SA.

Market Overview

Browse TOC and LoE with selected illustrations and example pages of Data Integration and Data Quality Tools Market

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Market Competitive Analysis

The market is fragmented. Data Ladder, Experian Plc, HCL Technologies Ltd., International Business Machines Corp., Informatica LLC, Oracle Corp., Precisely, SAP SE, SAS Institute Inc., and Talend SA are some of the major market participants. Factors such as the rising adoption of data integration in the life sciences industry will offer immense growth opportunities. However, high cost and long deployment time may impede market growth. To make the most of the opportunities, vendors should focus on growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this data integration and data quality tools market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and offers information on the products offered by various companies. Moreover, this data integration and data quality tools market analysis report provides information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of their future growth opportunities.

This report provides information on the production, sustainability, and prospects of several leading companies, including:

Data Ladder
Experian Plc
HCL Technologies Ltd.
International Business Machines Corp.
Informatica LLC
Oracle Corp.
Precisely
SAP SE
SAS Institute Inc.
Talend SA

Data Integration and Data Quality Tools Market: Segmentation by Geography

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The report offers an up-to-date analysis regarding the current global market scenario, the latest trends and drivers, and the overall market environment. North America will offer several growth opportunities to market vendors during the forecast period. The increasing demand for cloud-based data quality tools will significantly influence the data integration and data quality tools market's growth in this region.

44% of the market’s growth will originate from North America during the forecast period. The US is one of the key markets for data integration and data quality tools in North America. This report provides an accurate prediction of the contribution of all segments to the growth of the data integration and data quality tools market size.

Data Integration and Data Quality Tools Market: Key Highlights of the Report for 2020-2024

CAGR of the market during the forecast period 2020-2024
Detailed information on factors that will data integration and data quality tools market growth during the next five years
Precise estimation of the data integration and data quality tools market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the data integration and data quality tools industry across North America, Europe, APAC, South America, and MEA
A thorough analysis of the market’s competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of data integration and data quality tools market vendors

We can help! Our analysts can customize this report to meet your requirements. Get in touch

    Data Integration And Data Quality Tools Market Scope




    Report Coverage


    Details




    Page number


    120




    Base year


    2019




    Forecast period


    2020-2024




    Growth momentum & CAGR


    Decelerate at a CAGR of 3%




    Market growth 2020-2024


    $ 843.29 million




    Market structure


    Fragmented




    YoY growth (%)


    3.81




    Regional analysis


    North America, Europe, APAC, South America, and MEA




    Performing market contr

Data Acquisition (DAQ) System Market size was valued at USD 1.92 Billion in 2024 and is projected to reach USD 2.86 Billion by 2031, growing at a CAGR of 5.10% from 2024 to 2031.

Global Data Acquisition (DAQ) System Market Drivers

The market drivers for the Data Acquisition (DAQ) System Market can be influenced by various factors. These may include:

Growing Need for Industrial Automation: The need for data collection systems is being driven by the growing trend of automation in a number of industries, including manufacturing, automotive, aerospace, and healthcare. These systems are essential for gathering and evaluating data from sensors and other devices in order to enhance decision-making, quality assurance, and operational effectiveness.

Improvements in Internet of Things and Big Data Analytics: The demand for effective data acquisition solutions is being driven by the widespread use of Internet of Things (IoT) devices and the rapidly increasing volume of data they create. In contexts powered by the Internet of Things, DAQ systems provide real-time data collecting and analysis, facilitating predictive maintenance, asset optimization, and process optimization.

Growing Adoption of Wireless Data collection Systems: The need for wireless data collection systems is being driven by the uptake of wireless communication technologies like Bluetooth, Wi-Fi, and Zigbee. Compared to conventional wired solutions, these systems are more flexible, scalable, and affordable—especially in applications where wired communication is difficult or impracticable.

Growing Priority for Industry 4.0 and Intelligent Manufacturing: The integration of modern technologies like robotics, machine learning, and artificial intelligence into industrial processes is being driven by the concept of smart manufacturing and Industry 4.0. Real-time monitoring, control, and optimization of industrial processes are made possible by data acquisition systems, which operate as the foundation for gathering, processing, and transmitting data from linked devices and equipment.

Extending Research and Development (R&D) Applications: Data acquisition systems are extensively employed in academic institutions and research laboratories for a variety of R&D projects in fields including engineering, physics, chemistry, and biology. Stronger emphasis on innovation, product development, and scientific research is fueling the need for high-performance DAQ systems that can reliably and precisely capture and analyze large, complex data sets.

Strict Regulations for Safety and Compliance: There are strict regulations for safety, quality, and compliance in a number of industries, including food and beverage, pharmaceutical, and healthcare. Due to its ability to provide precise data monitoring, recording, and reporting for compliance needs, data acquisition systems are essential in guaranteeing compliance with these standards.

Growing Need for Control and Monitoring in Real-Time: Adoption of data collection solutions is being driven by the requirement for real-time control and monitoring of essential processes and systems in a variety of industries. DAQ systems offer the infrastructure required for real-time data collecting, analysis, and reaction, whether it is for monitoring environmental conditions, managing production parameters, or guaranteeing equipment reliability.

this srn dataset includes long-term time series on marine phytoplankton and physico-chemical measures, since 1992, along the eastern english channel coast. more precisely, samples were collected along transects offshore dunkerque, boulogne-sur-mer and the bay of somme. data are complementary to rephy and rephytox datasets. phytoplankton data essentially cover microscopic taxonomic identifications and counts, but also pigments measures (chlorophyll-a and pheopigment). physico-chemical measures include temperature, salinity, turbidity, suspended matters (organic, mineral), dissolved oxygen, dissolved inorganic nutrients (ammonium, nitrite+nitrate, phosphate, silicate).

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The data here concerns RD 743 from Niort to Parthenay. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The data relates to Boulevard Palissy, rue de la Marne and Rue Réole in Parhenay. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.

An atomic-scale ripple structure has been revealed by electron tomography based on sequential projected atomic-resolution images, but it requires harsh imaging conditions with negligible structure evolution of the imaged samples. Here, we demonstrate that the ripple structure in monolayer MoSe2 can be facilely reconstructed from a single-frame scanning transmission electron microscopy (STEM) image collected at designated collection angles. The intensity and shape of each Se2 atomic column in the single-frame projected STEM image are synergistically combined to precisely map the slight misalignments of two Se atoms induced by rippling, which is then converted to three-dimensional (3D) ripple distortions. The dynamics of 3D ripple deformation can thus be directly visualized at the atomic scale by sequential STEM imaging. In addition, the reconstructed images provide the first opportunity for directly testing the validity of the classical theory of thermal fluctuations. Our method paves the way for a 3D reconstruction of a dynamical process in two-dimensional materials with a reasonable temporal resolution.

What is the problem I am solving? How would you feel if, while reading this text, letters and words started merging or disappearing, making your reading impossible, and you knew there was nothing you could do to recover your reading ability? Frightening. Devastating. That is precisely the experience of millions of people in the world who have had a stroke or live with neurodegenerative conditions affecting the back of the brain – the area that controls vision. Science cannot repair a damaged brain, but in our research, I found a way to manipulate the text displayed in a reader device to compensate for this brain damage and make reading possible again. During this grant, we aim to convert these manipulation techniques into a commercially viable app to bring reading back to the lives of people with brain injury. For that purpose, we will release an MVP of the app and will collect anonymous user data to better understand the behaviour of the app beyond the clinical setting. The collection contains data collected via the app and online surveys – anonymous data. The app collected data from users (e.g., age, gender, country, type of brain injury). Data does not contain any personal identifiable data from users.

We developed a model for estimating demographic rates and population abundance based on multiple data sets revealing information about population age- and sex structure. Such models have previously been described in the literature as change-in-ratio models, but we extend the applicability of the models by i) using time series data allowing the full temporal dynamics to be modelled, by ii) casting the model in an explicit hierarchical modelling framework, and by iii) estimating parameters based on Bayesian inference. Based on sensitivity analyses we conclude that the approach developed here is able to obtain estimates of demographic rate with high precision whenever unbiased data of population structure are available. Our simulations revealed that this was true also when data on population abundance are not available or not included in the modelling framework. Nevertheless, when data on population structure are biased due to different observability of different age- and sex categories this will affect estimates of all demographic rates. Estimates of population size is particularly sensitive to such biases, whereas demographic rates can be relatively precisely estimated even with biased observation data as long as the bias is not severe. We then use the models to estimate demographic rates and population abundance for two Norwegian reindeer (Rangifer tarandus) populations where age-sex data were available for all harvested animals, and where population structure surveys were carried out in early summer (after calving) and late fall (after hunting season), and population size is counted in winter. We found that demographic rates were similar regardless whether we include population count data in the modelling, but that the estimated population size is affected by this decision. This suggest that monitoring programs that focus on population age- and sex structure will benefit from collecting additional data that allow estimation of observability for different age- and sex classes. In addition, our sensitivity analysis suggests that focusing monitoring towards changes in demographic rates might be more feasible than monitoring abundance in many situations where data on population age- and sex structure can be collected.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The data here concerns the A83 motorway from Nantes to Niort on only on the Niort bypass. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The route concerned is RD 950 going to Saintes on the municipality of Melle. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The data here concerns the A83 motorway from Nantes to Niort on only on the Niort bypass. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.

The European Directive on Strategic Noise Maps requires as a minimum the representation of the overall noise indicators Lden and Ln, for each source. These indicators correspond to the incident noise on the facades. The indicators represented are expressed in dB(A) and reflect a notion of overall discomfort or health risk. The data in this data set are related to the Lden indicator. “Lden" is an indicator of the overall noise level during a day (day, evening and night) used to qualify the discomfort associated with exposure to noise. It is calculated from the indicators "Lday", "Levening", "Lnight", "average noise levels over the periods 6h-18h, 18h-22h and 22h-6h. These are more precisely the isophone curves drawn in steps of 5 dB(A) from 55 dB(A) over a whole day. The data here mainly concerns Avenue Charles De Gaulle and Avenue Saint-Jean d’Angély in Niort en Deux-Sèvres. Articles L572-1 to 11 of the Environmental Code laying down various provisions for adaptation to Community law in the field of the environment and the implementing texts (Decree No 2006-361 of 24 March 2006, Decree of 4 April 2006 and Circular of 7 June 2007 on the drawing up of noise maps and plans for the prevention of environmental noise) provide for the indicators, the calculation methods to be used and the results I'm waiting for you. The data for this lot have been collected in accordance with these texts.