dataesr/scientific-paragraphs-categorization huggingface dataset:
, (2010) ,  Vega et al. , (2013). However, differences in timescales, data collection and methodologies make it difficult to compare figures across member states  (Kildow and McIlgorm, 2010; Surís-Regueiro et al. , 2013; Zhao et al. , 2014). There is an obvious need for a comparable and comprehensive set of marine socio-economic data to set objectives within management, define and inform policy and to track performance across industries. The EU Interreg IV (Priority 1) project Marnet (Marine Atlantic Regions Network) brought together eight partners across the five European Atlantic Arc countries -France, Ireland, Spain, Portugal and the United Kingdom (UK). A primary aim of the project was to develop a framework for the collection of marine socio-economic data across the participating countries.
In the EU, Britain, Italy and France were amongst the first to generate reports on their domestic maritime industries  (Mare, 1996; Pugh and Skinner, 1996; Kalaydjian, 1997). In the 1990s, marine economic reports were also issued on Norway and the Netherlands  (Wijnolst et al. , 2003)  and internationally, efforts were also made to quantify maritime activities in Australia and Canada  (RASCL, 2003; Anon. , 2004; GSGislason, 2007). A number of these countries, and others, now attempt to update their marine economy statistics on a regular basis. An action group on 'improving sectoral (ocean and coastal) socio-economic data at regional and EU level' was created by Eurostat in 2008. The purpose of this group was to recommend how best to collate data on coastal rather than marine socio-economic data at the regional and EU level.
In 2009, IFREMER concluded this analysis for Eurostat that examined the potential of developing a marine socioeconomic database for Europe. The authors of that study,  Kalaydjian et al. , (2009)  highlighted the fact that the reporting efforts on marine activities, carried out by the aforementioned countries, all faced similar problems. Firstly, the study points to problems relating to the scope and coverage of maritime activities. In particular, questions were asked relating to the inclusion of all or some of the activities located on the coast and deciding on how far inland the coast extends. Other difficult questions dealt with which marine activities may or may not be defined as part of the marine economy. For example, should inland waterway transport be included? Should activities indirectly connected to specific maritime businesses be included? Should downstream trade in marine-related products be included? Secondly,  Kalaydjian et al.
, (2009)  also highlighted difficulties in collecting maritime-specific data, especially for the sectors such as maritime equipment, marine tourism and a number of newly emerging marine services. To answer some of these questions,  Kalaydjian et al. , (2009)  presented the architecture of a database for maritime activities in Europe and also proposed methods to collect missing data and identified other relevant indicators to analyze maritime affairs. This architecture formed a template for the Marnet marine socioeconomic database presented in this paper. In the remainder of this section, the discussion is focused on previous marine economy reporting efforts in the European Atlantic Arc countries -France, Ireland, Spain, Portugal and the UK. For a more in-depth review of global marine economy studies see  Surís-Regueiro et al.
Where data could not be extracted from the national statistics for sectors referred to as 'emerging marine sectors', such as marine biotechnology and marine ocean energy, a survey of relevant companies was conducted. A similar assessment was carried out for the French marine economy with the objective of assessing the weight of the French marine economy, its position with respect to international competition and its role within public services in France  (Kalaydjian et al. , 2010). The classification of the marine sector activities follows the French system of Nomenclature d  'Activités Française, 2003 (NAF 2003)  based on NACE 2003. The indicators used to evaluate each industrial activity included turnover, value added, employment, number of companies and export rates. The most recently published data on the French marine economy is for 2009.
Pugh et al. , (2008)  estimate the economics and employment statistics for marine activities for the UK marine economy for the reference year 2006. They also report on numbers employed, value added, exports and turnover. The classification system used is the UK Standard Industrial Classification of Economic Activities (SIC) which is carried out in conjunction with the EU NACE system. The two systems are identical. Other studies carried out on the UK marine economy include  Pugh and Skinner (2002)  who estimate levels of marine related activities using data for years 1999 -2000. More recently  Morrissey (2014)  reviewed two time frames,  2003 -2007 and 2008 -2011 , providing an insight into the performance of the marine sector over time. However, this study looks more at trends for a subset of marine data for the English, rather than UK, economy.
FRAMEWORK DEVELOPMENT OF THE ATLANTIC MARINE ECONOMY DATABASE To overcome the problems and inconsistencies listed in the previous section, the Marnet project developed a coherent framework for a marine economy database and applied a robust methodology for the collection of comparable marine socioeconomic data on maritime activities in the Atlantic Area. The framework was developed through the collaboration of partners from member countries, namely France, Spain, Ireland, Portugal and the UK. The aim of the project was to set out a clear definition of the Atlantic marine economy, identify and classify marine socio-economic indicators to be used to value the different economic activities in Atlantic regions and use a marine industries classification system relevant to all countries as well as a common geographical structure.
Data is collected across sectors, space and time. To ensure consistency among countries, Eurostat statistical classifications are used -NUTS (Nomenclature of Territorial Units for Statistics) for the spatial dimension and NACE for the sectoral dimension of the data. NACE and NUTS have been used for two major reasons: 1) they provide a common standard for the definition of economic activities in Europe in general and in the Atlantic countries in particular; 2) they provide a full coverage of the activities and geographical zones identified as relevant for the Atlantic marine economy without double accounting in terms of business or spatial units. In 2007, the National Oceans Economic Program (NOEP) produced a guide for the measurement of market data for the ocean and coastal economy  (Colgan, 2007).
The overall goal was to establish a clear, common and replicable marine economic data framework for European Atlantic regions. Figure  1  summarizes the Marnet framework. To develop a framework for the Marnet project, a methodology for the identification, collection and classification of socio-economic data relating to marine activities in the Atlantic Area was proposed. The process was started in 2013 with the aid of stakeholder participation in each partner region. Figure illustrates the approach taken to identify and measure relevant marine economic and social activity in the Atlantic Area, some of which will be discussed in more detail below. In the development of the framework a decision on what data to collect and the preferred data sources was agreed upon by the Marnet partners. Indicators should ideally be representative, quantifiable, comparable, reliable, adaptable and relevant.
Where data is not readily available, or is not easily extracted or identifiable as marine, a proxy can be used as a representation. Proxy indicators can be both easier to collect and appropriate for characterizing the development of a given activity in a particular geographical area. These indicators are often available from Eurostat, NSIs and the relevant national government agencies or departments. The final information collected within the framework was related to population and social data, including information on density, age structure, occupations unemployment and poverty. This constituted a major dimension of the framework along with the structural business dimension. Given the diversity of occupation classes used by the Atlantic NSIs, three main categories were agreed upon by the Marnet team as a common structure of the occupied population.
All of these indicators have been defined for the Atlantic zones determined by NUTS codes. Some have been defined for the basic administrative regions only (e. g. poverty index) while others were defined at as low a spatial scale as possible to represent coastal areas (e. g. population density or the occupied population structure). To ensure the reliability and comparability of data, the preferred data sources were established databases within national statistics institutes (NSI) and Eurostat that were based on the NACE classification system. Where data was not available from these institutions, other public and private sources were utilized to gather data, such as state agencies, R&D institutes and industry associations. In some cases, data available from the NSIs was only available at higher geographical levels, for example, NUTS 0 (see definition below); therefore other sources were used to get more localized data sets and proxies.
GEOGRAPHIC/SPATIAL COMPARABILITY As highlighted by  Hynes and Farrelly (2012) , there are numerous definitions of a coastal region or zone in the literature that one might use in attempting to examine the socio-economic characteristics of the Atlantic Arc EU member states. However, as the aforementioned authors point out, many of these definitions do not facilitate the collection of comparable statistics on coastal regions for use by policymakers, in pre-existing and accessible data portals. For this reason, a single uniform definition of the spatial element of the data collection within the Marnet project was employed and was based on the EU NUTS classification. The NUTS classification is a hierarchical system for the division of economic territories of the EU for the purpose of collection, development and harmonization of EU regional statistics, and socio-economic analysis of regions  (Anon.
, 2014). In addition to NUTS0, defined as the highest geographical level, i. e. the whole territory of a member state, there are three levels of NUTS (Figure  3 , next page) regions defined by Eurostat: • NUTS 0: major socio-economic regions, in most cases these are defined using country boundaries • NUTS 2: basic regions for the application of regional policies • NUTS 3: small regions for specific diagnoses In addition to the NUTS classification, in order to meet the demand for statistics at a local level, Eurostat has set up a system of Local Administrative Units (LAU) compatible with NUTS. At the local level, two levels of Local Administrative Units have been defined: the upper LAU level (LAU level 1, formerly NUTS level 4) and the lower LAU level (LAU level 2, formerly NUTS level 5). All data -accounting, social and proxies -were collected at the NUTS 0 level.
ECONOMIC COVERAGE (INDUSTRIAL COMPARABILITY) In the development of a European data framework it was crucial that there was comparability across the maritime economic sectors and industries. As previously mentioned, the economic coverage of the marine sector is defined using NACE (Nomenclature Générale des Activités Économiques dans les Communautés Européennes) codes, the EU statistical classification of activities. NACE codes are similar to the NAICS (North American Industry Classification System), with slight differences depending on industry specificities in the EU and North America. Specific EU member states classifications, including SIC and NAF mentioned above, follow the NACE system with slight modifications depending on national specificities. It is the European industry standard classification system and thus allows for collecting comparable data among countries.
LIMITATIONS IDENTIFIED In applying the data collection framework some limitations and difficulties were identified. These included confidentiality, identification of marine activities in national accounts, the need for physical indicators and identifying appropriate timeframes. In the NACE classification system, a code may be only partially marine related. In this instance, it is difficult to quantify the value of the marine specific activity. Where a NACE code is only partially marine based, proxies, estimates or physical indicators will be required to estimate the proportion of the data associated with the marine sector. Overcoming this issue with proxies is shown in Table  3  in the case of marine The availability of data across countries also differs. Comparable data collection can be difficult to ensure across all partner regions.
By requiring each country to source its data from Eurostat and the National Statistics Institutes, the occurrence of non-comparable statistics can be minimized for data characterizing marine activities at the national (NUTS 0) level. However, documenting the Atlantic marine economy also requires local business data and proxies (i. e. at NUTS 2, 3 and LAU levels). Some of these local indicators may be unavailable from NSIs; differences may then appear between the datasets of the different countries in the of a common standard for the Atlantic local zones. In such cases, the preferable option is to take stock of the definition and source of local indicators with the objectives of changing these and improving data comparability when further updates of the database are carried out in the future and when alternative relevant data sources become available.
The metadata set, drawn up parallel to the database, is therefore an essential tool for the gradual development of the latter. Confidentiality of data is another major issue, especially when comparing data across regions. Data can be classified as confidential for a number of reasons. The National Statistics Institutes will have regulations in place regarding the release of confidential data. In Ireland, for example, for commercially sensitive activities, confidential data occurs if one business makes up 80% or more of the turnover or employment of a given economic sector; or if a NACE code includes less than three firms. In order to overcome confidentiality issues, and to compare data across countries, the partners agreed upon 15 aggregate sectors. Each aggregate sector contains a number of single NACE codes aggregated together to make up a larger sector.
Comparisons across other marine industries such as seafood processing are more straight-forward as the associated NACE codes are completely marine. Table  4  (next page) presents data collected from NACE 3. 11, fisheries, and NACE 3. 21, aquaculture. Employment, GVA and the share of GVA as a percentage of GDP are presented. The data collected has been made available through the Marnet atlas of marine socio-economic data (http://marnet. locationcentre. co. uk) and can also be accessed through the Marnet network website 2. 1 The proxy value for the percentage share of marine tourism for Portugal in 2009 was obtained by considering only 13,5% and 20,4% of the total GVA of tourism activities in the Oporto and Lisbon metropolitan areas, respectively, as in Portugal's Assessment for the Marine Strategy Framework Directive ("Diretiva Quadro da Estratégia Marinha").
CONCLUSIONS European policies are recognizing the importance of socio-economic data to inform future decision making, management and regulation of marine sectors. European policy, such as the IMP, emphasizes the need for economic and social information on maritime affairs in its objectives. These include the construction of a decisionmaking framework, involving national and local authorities and stakeholders in maritime and coastal areas. In its Integrated Maritime Policy for the European Union, the EU commission also proposed developing a database on economic and social data for maritime sectors and coastal regions (Action 6. 5). While efforts have been made previously to report on the value of the European marine economy or elements of the marine economy  (Kalaydjian, 2009; ECORYS, 2013) , true comparisons, using these sources, are not possible across countries due to the use of secondary data from country reports that may have differing time frames, sectors, and/or spatial scales.
Other data sources on the marine economy may only report on coastal regions, profiling demography statistics, but not industry related data. For example the Eurostat database reports some statistics specifically related to maritime regions, including demography and coastal tourism. The Interreg project Marnet developed and applied a framework for the collection of consistent and comparable marine socio-economic data across the Atlantic Arc countries. The framework creates a clear template for comparison and analysis of marine socio-economic data across time, space and industry. The marine socio-economic data framework developed also contributes to requirements under a number of marine policies, including the Integrated Maritime Policy, the Marine Strategy Framework Directive, the EU Atlantic Strategy and the recently reformed Common Fisheries Policy presented in Figure  4  (next page).
It is, however, relevant to all European countries. It can provide a template for other European states to follow that could potentially facilitate the construction of a Europe wide marine economy information system. In terms of further development of the Marnet database, a range of options could be considered; the present discussion will be limited to an example. The remaining difficulties to be overcome result from the specific nature of the marine economy which rests on a spatial definition and remains without clear delimitations in national accounts. As mentioned earlier, a number of marine activities are part of sectors which include both marine and non-marine activities. It would be costly in terms of additional business inquiries (given those already carried out by NSIs) to combine local level data available and collected at a fine spatial resolution level (preferably at the LAU level) with business data available on every marine subset of NACE sectors; all the more if this extended database was regularly updated.
The exercise would also have limitations both in terms of the nature of collectible data at this high resolution level and in terms of confidentiality, not to mention the inquiry burden on enterprises. A less costly option is indicated by the final stage of the Marnet project: the practical initiatives were undertaken with the objective of using the database to analyze certain marine sectors in specific areas with potential for further economic development, and of increasing the awareness of the availability and the utility of the database. This approach will also help identify further data requirements to improve the database. This exercise suggests that it would be relevant to explore the possibility of: 1) regularly updating the existing database at reasonable cost in accepting its limitations and data gaps; 2) developing extensions of this main database, with higher resolution on specific sectors and geographical areas considered relevant with respect to maritime policy and marine economy issues.
This would require data from complementary sources, consistent with the main database, the extensions of which would be updated on a case by case basis. Other options could be derived from this example, depending on sectors and areas to be scrutinized, on the need for data update frequency, and on data acquisition costs to be estimated. Whatever the selected options, the sustainability of the database will rest on the existence of a common framework as developed in the Marnet project and based on the EU statistical classifications of activities and spatial areas. This is the key condition for securing a reliable set of indicators permitting to assess the value of the marine economy in broad terms and to verify the consistency of datasets developed in the future.
(2006)  using ACS data in B, V, I, and Hα bands. These authors examined the distributions of size and luminosity of star-forming regions, finding that both of these were well described with a power law, indicative of a hierarchical structure of stellar components within the galaxy. The work herein primarily focuses on describing the two-point correlation function as a tool to study the clustering properties of the young stellar clusters. For this goal, we use the galaxy NGC628 as an example. We 20 https://legus. stsci. edu/ hope to address the following points once the clustering properties of a larger number of LEGUS galaxies have been studied: (1) whether stars clusters are clustered and how strongly; (2) how the clustered distribution of stars compare to that of star clusters; (3) how the clustering depends on age and environment and the dissolution timescale of the clusters; and (4) whether or not clusters can be used to trace the structure of star formation.
Observations of NGC 628 consist of two pointings, one towards the center of the galaxy, NGC628c, and one towards the galaxy's eastern region, NGC628e, adjacent to the first pointing. All five filters in both pointings are aligned and referenced to the UVIS/F336W filter. Both pointings are combined into a single mosaic for analysis, shown in Figure  1. The images have a scale of 0. 04 arcsec per pixel, corresponding to a pixel resolution scale of 1. 9 pc at a distance of 9. 9 Mpc. General descriptions of the standard data reduction of the LEGUS datasets is available in  Calzetti et al. (2015).
When computed as above, a random distribution of an unclustered population will result in a flat correlation ω(θ) = 0, while a clustered distribution will have ω(θ) > 0. For hierarchical structuring, a general trend of decreasing 1 + ω(θ) with radius is expected  (Gomez et al. 1993; Larson 1995). This correlation function has also been successfully used for characterizing the clustering behavior of both stars and stellar clusters (e. g. ,  Gomez et al. 1993; Zhang et al. 2001; Scheepmaker et al. 2009; Gouliermis et al. 2015a,b)  in several nearby galaxies. Although the formulation in Eq 1 is optimized for taking into account edge effects, we still attempt to reproduce as closely as possible the geometry of the galaxy region sampled. As NGC 628 is nearly face-on, we do not need to take into account deprojecting the data before computing the correlation function.
1 and 5. 2 the differences between the correlations for the separate classes of clusters, and how we will attempt to single out the Class 0 clusters without visual inspection. As mentioned above, the LS estimator is minimally sensitive to the random sample size and is effective at handling both edge and masking effects, as found in  Landy & Szalay (1993)  and  Kerscher et al. (2000). Using the central pointing, NGC 628c, to test how changing the geometry and masks within the random data sample is going to affect the results, we found that a random catalog of the same observing geometry as the real data with a random sample size of 10,000 objects already converges to the same correlation function values as a random catalog with 500,000 sources. As a sanity check, we create random fields with 1200 and 70,000 objects and compute the correlation function between the two catalogs and find that the computed ω(θ) is zero across all angular scales.