dataesr/scientific-paragraphs-categorization huggingface dataset:
The application, implemented by the European Environment Agency (EEA), adds a new layer to the Eye on Earth¹⁶ online project that collects and shares environmental data as information concerning water, air, climate change, biodiversity and land use. SoundOfTheCity¹⁷ rests on a continuous, contextaware and unobtrusive participatory sensing approach for measuring noise levels and for monitoring the community exposure under the context of a healthy city  [62]. By default, one-second noise measurements are performed every 30 s. The anonymised measured data are sent to a cen-tral server that aggregates and generates noise maps. This Android application allows additionally city dwellers to upload sound samples, to annotate the classification of the recorded sound by cause and to provide each user with information on their personal noise exposure.
The amount of collected data from such heterogeneous sources (applications, sensors, individuals, environmental quantities), the data management requirements in terms of analysis, research and processing, and the exploiting of the gathered and generated data, place the information system issues at the heart of participatory sensing projects. The geographic information community defined standards for collecting (sensor standards), storing (Relational DataBase Management System, RDBMS), processing (spatial languages) and sharing geodata (view and discovery services). The European INSPIRE Directive incidentally established an infrastructure for spatial information in Europe to support Community environmental policies  [73]. The Open Geospatial Consortium²³ (OGC) defined standards (e. g. WPS, WMS, SFS, etc.
See section 4. 3) in order to formalise sensors data. Such standards promote the merging of different scientific skills within the context of territory observatories. The tripartite organization of crowdsourced-based projects based on the combination of the citizens personal observations and harnessing, the policymakers collection of data, and the scientific researches and develop-   ment of dedicated tools, stands for a promising capitalization framework. Environmental noise data can be recorded by the citizens who have sometimes the opportunity to brand their noise data with some associated perceptive feedbacks. These information are a valuable resource for both the scientists and public authorities since they provide a low-cost alternative solution to simulations or fixed sensors network monitoring while keeping the citizen at the heart of both the research and action plans.
Project position The proposed system is under development within the framework of the ENERGIC-OD project that aims at deploying a set of Virtual Hubs (VH) based on a broker approach to offer to both end-users (through geoportals) and machines (web services, applications), unique and mutually consistent access points to heterogeneous data sources, including INSPIRE-compliant systems and Copernicus/GMES services. In this context, the main originality of our framework's concept compared to approaches detailed in section 3. 2 rests upon the proposed system prototype, named OnoM@p (Open noise Map). This system relies upon a smartphone application dedicated to both novice and experts volunteers who are concerned about their own noise exposure and who agree to anonymously upload their geolocalised noise measurements along walking journeys or at specific locations.
The OnoM@p infrastructure aims at capitalising on interoperable standards in terms of data formats, transfer protocols, as well as web view and discovery services, for many purposes. First, the data harvested by voluntary citizens are enhanced by means of dedicated automated processes able to control the data faithfulness (see section 6. 1), to intersect the upgraded data with both geographical and population data to furnish value-added datasets (see section 6. 2) and to redistribute the data to both the scientific community and urban collectivities for then valuating the data through their reuse for other goals and the development of new related-services²⁴.
General overview The Spatial Data Infrastructure (SDI) can be represented as a five-component system such as depicted in Figure  1  that summarizes the conceptual model behind the proposed framework. With the dedicated smartphone application installed on their smartphone (see section 5), each individual can estimate its own noise exposure and, by uploading its geo-localised measurements, contribute to a community noise data collection. The output database is interconnected with OGC standards through services for broadcasting and consulting the database (see section 4. 3). Thus more advanced noise-related indicators as statistical and spatial indicators are evaluated (see section 6) by combining the collected noise data with OpenStreetMap and statistical data (e. g. demographic data) through the Energic-OD Virtual Hub.
Finally, the gathered data are published though Web Services. Such an initiative is thus valuable at once for the citizen (i. e. at the individual scale), for the collectivities and for the environmental science community (i. e. at the collective scale). For scientists, this project stands for an opportunity to gather a large amount of noise data over a wide area and to study the feasibility of improving the representativeness of simulation-based noise maps by integrating crowdsourced data information. Such a database can be also useful for many other purposes, in particular concerning the data qualification. Such information is also interesting for local collectivities and main agglomerations that can lean upon this collective effort to grasp information regarding the local noise concerns and to better inform the population about its noise exposure.
Tools, frameworks and standards The smartphone application is implemented through the Android Software Development Kit (SDK). The sound signal processing is developed entirely in Java programming language and integrated into the Android implementation (see section 5). The standards entailed for both the sending of the data by the user and the return of enhanced information toward the smartphone are detailed in Figure  2. The data uploaded by the volunteers from the application feed the database which is managed by the open source spatial database H2GIS²⁵ that completely complies with the OGC for Structured Query Language (SQL) standards and built on the Java DataBase Connectivity (JDBC) compliant H2 database. H2GIS is harnessed for both building the noise measurements database directly issued from the users contributions, and computing additional noise indicators by compiling the noise data with both statistical and geographical data.
The Geographic Information System (GIS) is also employed to filter or alert to potentially poorly localised data based on the geographical database. The data collected along a journey (i. e. a track) and shared by the volunteers with the application are forwarded to the database in zipped files that contains 3 files: a GeoJ-SON file that stores all track coordinates, a txt file²⁶ that contains the noise indicators computed along the track and another txt file that concatenates some metadata concerning the smartphone, the calibration method, etc. This compressed file is transferred to the server by means of a Web Processing Service (WPS) handled by the open source software GeoServer²⁷ which is used to share information, to manage the database and to render thematic maps. The zipped files received on the server are thus unzipped and the extracted files are integrated into the database by a relational database management system (RDBMS) SQL script.
The cross-calibration process and the estimation of the advanced noise indicators (see section 6) is also handled by GeoServer and implemented through Simple Features Specification (SFS) for SQL. Besides, the resulting data gathered in the database are made available as Web Map Service (WMS) layers that are displayed on the smartphone in a custom map browser thanks to the open-source JavaScript library Leaflet²⁸ based on HTLM5 canvas. 5 Smartphone application.
Smartphone's microphone calibration A calibration step must be achieved beforehand any measurement. Two calibrations are conceived within the OnoM@p system, which can be used separately or combined: an a priori standard individual calibration described hereafter and an a posteriori cross-calibration detailed in section 6. 1. A high dispersion can be observed in measurements if different devices and noise measurement applications are used. This dispersion can theoretically be reduced thanks to an individual calibration process. Such calibration was proposed in  [43] , restraining however measurements to the case when identical smartphones and applications are used by all the participants. The objective of the a priori individual calibration included in the OnoM@p system is to determine the offset values to apply to the raw data furnished by each smartphone per frequency bands.
This individual calibration should be theoretically carried out in lab by comparing the smartphone's outputs with reference results delivered by an accurate and reliable microphone, what would debase both the relevancy and operability of the proposed outsourced system expected to be a self-sufficient tool. This solution is nonetheless proposed by the application for individuals equipped with a reference microphone by confronting either A-weighted global sound levels L Aglobal or preferentially A-weighted third octave band sound level spectra L A,fc. Incidentally, an alternative practical solution is proposed here that consists in standardising the smartphone's microphone at the vicinity of a public terminal (e. g. a fixed noise monitoring station in a street or a sonometer in a concert hall during a musical event) that Brought to you by | IFSTTAR Authenticated Download Date | 5/9/16 9:56 AM records simultaneously with the mobile device the background noise and relays the results to the server that update the data linked to the user profile in the database with the calibration offset values.
Brought to you by | IFSTTAR Authenticated Download Date | 5/9/16 9:56 AM direction of the major sound sources and at a minimum distance of his body, etc. Besides, a full documentation of the application and some presentation videos will also be provided on the OnoM@p website. In writing the paper, the smartphone application is largely operational, but a few of its sections still need improvements or further developments. For instance, the data transmission from the smartphone to the server database has to be pursued and the implementation of the third octave band filtering explained in section 5. 2 must be optimised. In addition, the graphical user interface will propose two modes of use: a novice mode and an expert mode. The second mode, dedicated to experienced users, shall propose additional functionalities and include specific features (e.
Database structure The database is populated with the three files contained in each zip file send by a smartphone. Six tables are used to collect all data in the database: • appli_track contains the data referring to noise indicators along the GPS tracks; • appli_user aggregates information about the contributors; • appli_point is made up with measurement points data with supplementary information concerning the device state; • appli_calibrate gathers the calibration values for the GPS tracks for each nominal frequency; • appli_freq gives the sound levels per nominal frequencies for each measurement point; • freq lists the nominal frequencies.
Outsourced data treatment The collected raw noise data on the server are stored and processed in order to describe and to evaluate the sound environments, based on two steps. The first step concerns the cross-calibration of the raw noise data for the purpose of both detecting the outliers and improving the accuracy of the geo-referenced noise measurements (section 6. 1). The second step consists in calculating advanced indicators by combining the cross-calibrated noise data with geographical and statistical data (section 6. 2).
Cross-calibration Even if an a priori standard individual calibration is achieved (see section  5. 4) , what is not necessarily the case, both some deviation and some dispersion remain in the noise measurements, which can be associated with the sensor or with the operator. Moreover, the individual calibration is an expensive and time consuming process, which loses in efficiency if the apparatus deviates with time or with atmospheric conditions (i. e. air temperature, ambient pressure, hygrometry). Thus, an a posteriori cross-calibration is proposed within the OnoM@p system, which is able to deal with a heterogeneous mobile sensors network with not necessarily previously calibrated devices. The objective of this outsourced cross-calibration is to rely on the mobile sensors network for correcting the errors in the raw data furnished by each individual sensor.
As a result of this second step, each sensor is associated with an estimated bias, which is stored and serves to correct subsequently the raw data furnished by the corresponding smartphone. However, the correction achieved at this stage makes the assumption that the smartphones network has a zero average bias, what is not necessarily the case. The last step relies on the estimate of the average bias of the smartphones network through a comparison with a fixed high-quality sensors network, if available. This average bias is evaluated by calculating the difference between the noise levels measured by the whole smartphones when they pass in the vicinity of a fixed trustworthy sensor. The bias estimated by means of each fixed sensor is averaged over the fixed sensors network. This assumes however that the sound levels provided by the fixed sensor and the ones given by a perfect mobile sensor passing by in front of it are equal, what cannot occur as the locations of both sensors cannot be rigorously the same.
Further studies are required to design transfer functions between the fixed sensor position and the smartphone location for reducing this bias. Note finally that the average bias of the smartphones network remains probably similar from one city to another. Consequently, the estimated bias could be imported from a city equipped with a high-quality sensors network, hence limiting the installation and monitoring costs. The cross-calibrated data make up a set of georeferenced L Aeq,fc ,1s values that can be considered as reliable and are stocked in the database. These data are then used to estimate the basic and advanced indicators presented hereafter. In writing the paper, this protocol was experienced with a Matlab algorithm and has to be transposed into SQL programming language.
Basic indicators The common indicators are calculated by aggregating the cross-calibrated data gathered within a buffer area whose radius (equal to at least 20 m) depends on the amount of collected data. The estimated basic noise indicators are the day-evening-night equivalent sound level L den and the Daily Average Noise Patterns (DANP), which are the average L Aeq,1h value per 1 h-period of the day, evaluated as in  [76]. The L den value (Eq. (4b)) is deduced from the DANP values (Eq. (4a)), by introducing the following evening and night penalties: DANP [hi;hi+1] = 10 log 10 (︃ 1 N i Ni ∑︁ ni 10 ( 0. 1×L Aglobal,n i ) )︃ , (4a) L den = 10 log 10 In writing the paper, the publishing of the noise map with GeoServer remains under development.
Consequently, the indicators are selected based on both their enforceability and transparency. Their calculation, outsourced to the cloud server, takes profit of the powerfulness of spatial SQL language features available through the H2GIS engine (see section 4. 3). Exposure indicators are dedicated at characterizing either the user own exposure or to the population exposure. At an individual level, the personal exposure is assessed by estimating and displaying within the smartphone application results screen both the L A,global and RNE values related to a measure (see section 5. 4). At a population scale, the L den and DANP maps are combined with occupation rates data (if available) in order to determine the number of persons exposed to a L den level higher than a given threshold value or comprised within a range of values.
Data enhancement The data forwarded by the application users are made more valuable at both an individual and public scales. For the user, a valuable targeted feedback related to its own needs through devoted services which harvest the noise database (i. e. benefiting from the whole users' contributions) stands for an added-value information for the contributor. This shall moreover enable to motivate the participant to continue contributing to the crowd noise database by uploading geo-localized measurements. The proposed fully-interoperable infrastructure is also designed to encourage new development initiatives that reuse the built database for the purpose of user-centric services. For example, a few accessibility indicators can be designed that aim at offering tools dedicated to reducing its personal noise exposure.
At a collective scale, the proposed system gives a free access for local authorities to view and discovery services that enable to generate the strategic noise maps demanded by the European union (see section 1). The regulation necessitates to provide noise maps for each type of source. Thus, future works will consist in developing outsourced processing methods to discriminate a posteriori the noise sources. Nevertheless, the present experimental approach also offers complementary information to simulation-based noise mapping that cannot account for all noise sources. Besides, accessibility to data shall act as an incentive for the collectivities to regularly update their noise maps and to easily analyse the impacts of achieved urban plannings. The built noise database and related services shall also initiate business actions.
We study wage adjustment during the recent crisis in Italy using a unique dataset on immigrant workers that includes those employed in formal and informal sector. We find that before the crisis immigrants' wages in the formal and informal sectors moved in parallel (with a 15% premium in the formal labor market). During the crisis, however, formal wages did not adjust down while wages in the unregulated informal labor market fell so that by 2013 the gap had grown to 32%. The difference was particularly salient for workers in occupations where the minimum wage is likely to be binding, and in "simple" occupations where there is high substitutability between immigrant and native workers. Calibrating a simple partial equilibrium model of spillovers between formal and informal markets, we find that less than 10% of workers who lost a formal job during the crisis move to the informal sector.
Another approach taken by the literature is comparing labor markets in different industries within the same country, although comparability issues still arise. In order to construct a convincing counterfactual for a regulated labor market, one would need to study a nonregulated labor market in the same sector within the same country. That is precisely the scope of this article. We compare formal and informal labor markets in Italy over the years 2001-13 considering informal employment as a proxy for unregulated counterfactual to the regulated formal labor market. 1 We are able to carry out this analysis as we use a unique dataset on immigrants who work both in formal and informal sector. 2  We use a large annual survey of immigrants working in Lombardy carried out by the Foundation for Initiatives and Studies on Multi-Ethnicity (ISMU).
Our data cover around 4000 full-time workers every year, a fifth of which works in the informal sector. The dataset is therefore sufficiently 1. We define informal employment as employment without a legal work contract. We use the term "informal" as a synonym of "underground" and "unofficial. " A key assumption of our analysis is that we consider the informal labor market to be less regulated than the formal labor market. 2. Information on native workers' informality status is not available from other datasets. In the concluding remarks, we discuss the external validity of our findings. large to allow us comparing the evolution of wages in the formal and informal sectors controlling for household characteristics, occupation, skills, and other individual characteristics (age, gender, year of arrival to Italy, and country of origin).
We find that in this case the crisis would have resulted in a much smaller decline in formal employment between  2008 and 2013 (1. 5-4. 5% rather than the actual 16%). To verify the validity of our empirical strategy, we perform a number of checks. First, we run several placebo tests that provide support to the Source: ISMU survey, authors' calculations. less relevant than the minimum wage regulation. However, these results may also be explained by the destruction of jobs due to the exit of firms that can neither reduce wages nor reduce employment. Since our dataset does not include detailed data on employers, we cannot distinguish between these two explanations. parallel trends assumption required for difference-in-differences estimation. Second, we use the information we have on the immigrants' intentions to return to their origin country and show that informality is not associated with selection into return migration.
The authors also argue that one of the major channels of adjustment is a great reduction of the inflow of Mexican immigrants during the recession. Lessem and Nakajima (2015)  confirm this finding using the data from the Mexican Migration Project based on the undocumented migrants' recollections of their dates of trips to the United States and the wages they earned there. They also show that undocumented Mexican immigrants' wages in the United States are negatively correlated with the US unemployment rate-unlike the wages of the legal migrants and the wages of the natives, including those of the Mexican origin. Their estimates stress the important role of occupational spillovers: during the US recessions, undocumented Mexican immigrants are more likely to shift to agricultural jobs. Our work adds to this first branch of literature by exploiting information from a unique dataset on immigrants working in the formal and informal labor markets, considering informal employment as a proxy for unregulated counterfactual to the regulated formal labor market.
We cluster the standard errors by province of residence, by simple/complex dummy and by before/after crisis dummy; we end up 9. Conditioning on full-time employment, the estimated coefficient at the interaction term does not include the differential effect of informality during the crisis through changes in working hours. In Table  A7  (Supplementary Appendix) we show regressions where we use information on individuals who are employed on part-time basis. The ISMU dataset does not include information on hourly wages or the number of hours worked. with 44 clusters (11 provinces times 2 types of occupations times 2 time periods). 10  A potential threat to the identification might arise if the formal and informal sectors have been distinctively hit by the Great Recession due to preexisting differences in industry specialization.
Data Our main database comes from the annual survey of immigrants undertaken by an independent Italian nonprofit organization called Foundation for Initiatives and Studies on Multi-Ethnicity (ISMU). This survey provides a large and representative sample of both documented and undocumented immigrants residing in Lombardy and working in formal and informal sectors. 13 The ISMU survey adopts an intercept point sampling methodology, where the first step involves listing a series of locations typically frequented by immigrants (such as religious sites, ethnic shops, or healthcare facilities), whereas in a second step both meeting points and migrants to interview are randomly selected. At each interview, migrants are asked how often they visit the other meeting points, which permits to compute ex-post selection probabilities into the sample.