dataesr/scientific-paragraphs-categorization huggingface dataset:
The Open Government Licence (UK) The UK Government Licensing Framework for Public Sector Information 84 was created to license "the use and re-use of public sector information both in central government and the wider public sector. "  85 The Open Government License 86 replaced its precursor, namely the Click-Use license on 2010. The license removes barriers to the reuse of public sector information, requiring only "including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to th(e) licence. "  87 e licensed rights include intellectual property rights and sui generis database rights. The copyright status of public sector information in the UK also includes the Crown copyright 88 , which is a special copyright that vests in governmental works.
Accepted manuscript The final publication is available at B. van Loenen et al. (eds. ), Open Data Exposed, Information Technology and Law Series 30, https://doi. org/10. 1007/978-94-6265-261-3_6 13 available of public sector information 92. The charges are therefore permitted as long as they are in the public interest and as long as they are calculated based on objective, transparent and verifiable criteria. 93.
98 However, and according to the published decree of approved licenses 99 , administrations can also use the Open Database License. The list in question does not include the Creative Commons licenses, even though their compatibility with the Etalab license is stated within the license terms. The absence of Creative Commons from the decree is noteworthy because they are the most widespread licenses on an international level in open scientific and public sector data. 100 92 The license is created in accordance with the Re-use of Public Sector Information (PSI) Regulations 2015: The Re-Use of Public Sector Information Regulations 2015, SI 2015/1415, (UK), available at legislation. gov. uk/uksi/2015/1415/contents/made. Accessed on 1 July 2017. 93 The charges in question are not to be construed as means of opening data in order to finance the public sector.
7 In order to signify the change, the added provision clarifies that the license conforms to the n° 78-17 of 6 January 1978 regarding the protection of personal data. 98 The decree (Décret n° 2016-1922 du 28 décembre 2016 relatif à la publication en ligne des documents administratifs) published in accordance to article 6 of the law n° 2016-1321 of 7 October 2016 specifies that all French administrations with more than 50 agents have to make their data open. 99 See article D323-2-1 created by decree n°2017-638 du 27 avril 2017 -art. 1. 100  The justification for not including the Creative Commons in the decree of open data licenses could be found on the fact that version 4. 0 has not yet been translated in French thus risking user confusion if applied.
Compatibility issues in the same open data licensing model The sources of incompatibility of licenses in the same model stem either from the difference in licensing terms or from the diversity arising from the evolution of the versions of the licenses. The Creative Commons licenses have created multiple sources of incompatibility varying from the difference in license elements to the differences in incremental versions and ported ones 104. Incompatible restrictions are common within license families as well as between licenses of different models. For example, when the license restricts commercial uses, the derivative works created can only be published with the same license because additional permissions are required in order to obtain the right to permit commercial uses of the published content.
Consequently, the license CC BY NC can only be combined with the same license or with an equally restrictive license such as CC BY NC SA. The only license that can be easily combined with works licensed under more restrictive terms is the CC BY license because it imposes no obligations as to the licensing conditions of the derivative content. The Open Government license is incompatible with the two more restrictive licenses from the same licensing model. Similarly, the Open Data Commons permissive licenses are only oneway incompatible because of the share alike clause. For example, the database licensed with ODC By can freely be reused for the creation of a derivative database, which can be licensed with the ODbL, because nothing in the first license dictates the licensing terms of derivative databases.
At the same time, the derivative created from the reuse of a database licensed under the ODbL can only be licensed under the same license or "a later version of this 101 Benjamin Jean uses examples from set theory to demonstrate the complexities of license compatibility. Jean 2006 102 Dulong de  Rosnay 2010, p. 60 ; Giannopoulou 2016, pp. 118-126. 103  See "What does one-way or two-way compatibility mean?" in ShareAlike compatibility. Available on: https://wiki. creativecommons. org/wiki/ShareAlike_compatibility. Accessed May 2018. 104 Dulong de Rosnay 2010. License similar in spirit to this License; or iii. a compatible license" 105. The share alike obligation dictates the compatibility rules because it restricts the licensing conditions of the derivative databases. Etalab version 2. 0 is the only license that declares backwards compatibility with content licensed with the prior version of the same license 106.
Finally, the public domain tools ensure only one-way compatibility with the licenses because the application of a waiver to a derivative database is only possible when its maker is the rights holder of all the rights involved. The evolution of the terms of the licenses for databases creates incompatibility risks between different versions. For example, the ported versions 2. 0 in France, the Netherlands, Belgium, and Germany are only compatible between each other because of the inclusion of the sui generis rights to the license. The ported versions 3. 0 create an additional fragmented licensing field because of the waiver of the sui generis rights and the non-application of the license elements to the sui generis rights 107. Creative Commons declares the licenses compatible with the equivalent version 4.
Compatibility issues across different open data licensing models Creating interoperable licenses facilitates the reuse of information and strengthens the Open Data movement. Lately, "progress has been made to match some characteristic prescriptions featured in most of the open data licenses (particularly those developed by public authorities) by prohibiting explicitly to sublicense the material, as well as prescribing to mark or indicate modifications to the original data and not to assert any official status regarding the licensee's use of such data. In addition to that, the adoption of the Database directive terminology contributes to improve lexical accuracy and clarity". 108 The standardization tendency has harmonized a lot of prerogatives across the open data licensing field, but incompatibility risks remain.
109 rsions prior to version 4. 0 of the Creative Commons licenses are a priori not compatible with other open data licenses because of the diverse rules that apply to sui generis rights. The incompatibility across different open data licenses due to the license elements is relatively straightforward because, with the exception of the Creative Commons licenses, the rest of the open data models examined have developed a limited diversity in license elements. However, "the Share Alike compatibility is merely a political statement which must be validated by the facts" 110. The process of validating compatible licenses is not only a parallel examination of the license terms in order to verify the similarities, but it is also a political consensus between organizations that the licenses produce the same effects.
Creative Commons has made a list of compatible licenses, but there are no open data licenses on that list. The international open data models, such as the Creative Commons version 4. 0 and the Open Data Commons licenses are considered the point of reference for the local open data licenses. In the spirit of standardization of the licensing terms and in order to maximize the potential of reuse of the open data, the local licenses intentionally declare compatibility with the larger open data models. For example, according to the Open Government license (UK), "these terms are compatible with the Creative Commons Attribution License 4. 0 and the Open Data Commons Attribution License, both of which license copyright and database rights. This means that when the Information is adapted and licensed under either of those licences, you automatically satisfy the conditions of the OGL when you comply with the other licence.
Fragmentation and proliferation of open data policies The economic and social value of open data has led to policies that prioritize transparency, democracy, and innovation. The multiplication of policies and the expansion of licensing models have nevertheless given rise to a quasi-universal set of principles for open data. However, even though there is a trend towards the establishment of open data, the policies implemented and the tools used to implement them are not as homogenous as they initially appear to be. At the same time, the interaction and engagement with open data transcends national border. Consequently, the diversity of policies poses an obstacle to the wide dissemination of data and hinders their reuse due to the fragmentation of the licensing options. The European Data Portal 111 demonstrates the extent of the license fragmentation by listing the different licenses applied across the EU.
Lack of harmonization of underlying legal framework As we demonstrated in the first section, there is a big diversity in underlying positive law that applies to data and databases. This diversity adds to the legal complexity of creating open data that are accessible and re-usable on a transnational level, with the use of interoperable licenses and streamlined rules. For example, the different applications of the originality threshold for copyright protection add to the confusion in distinguishing original from non-original databases according to applicable law. Applying an open data license is only possible when the database is protected by copyright or by a sui generis right. The application of the sui generis right in some jurisdictions for both original and non-original databases poses an extra layer of complexity to the creation of open data.
The combination of the diversity in standards of copyright protection with the fragmented application of the extra layer of sui generis right can lead to either an extensive application of licenses or to the application of a license that insufficiently licenses the rights related to a database. Recognizing that the sui generis right can be an obstacle to the creation of open public sector data, French law precludes the sui generis rightsholder from invoking their right in order to impede the creation of open data. Consequently, the sui generis right cannot be used by administrations or other third party rightsolders to prohibit the application of a license creating open data in France. of covering the necessary costs. What's more, the transposition of the PSI Directive in France brought a nuance to the principle of free access in applied open data policies.
Accepted manuscript The final publication is available at B. van Loenen et al. (eds. ), Open Data Exposed, Information Technology and Law Series 30, https://doi. org/10. 1007/ 978-94-6265-261-3_6   18   The application of a license inherently presumes the existence of property rights. According to some commentators, the creation of open data through the application of the licenses takes "for granted an established system made up of several layers of rights to exert control over information" 117. Even though a lot of public sector data is not protected by copyright or sui generis rights, open data licenses and waivers are applied to databases with an unclear intellectual property rights status or who are already in the public domain. 118 The difficulty in ascertaining whether a database is eligible for protection or not leads to a tendency to overregulate by applying open data licenses 119.
Such a license would have to take into account the different needs of various types of datasets ranging from scientific data to public sector data, the variety of legal obstacles, and the linguistic barriers, in order to contribute to the improvement of open data quality. This study demonstrated that the variation in open data licensing schemes founded in the diversity of the underlying legal framework could be addressed with a combination of affirmative policies towards improvement of interoperability complemented by a standardization of the existing licenses. Based on these conclusions, further research can be pursued on the relevance of the license infrastructure in improving open data quality. For example, the development of interpretation methods such as checklists across different dataset types could contribute towards the interpretation of restrictions such as share alike and commercial use in specific contexts.
This form of the license distinguished Creative Commons from other open content licenses because it was an effective expression of rights into code. See Dulong de Rosnay 2016 59 Giannopoulou 2016; Dulong de Rosnay 2010. Ibidem Accepted manuscript The final publication is available at B. van Loenen et al. (eds. ), Open Data Exposed, Information Technology and Law Series 30, https://doi. org/10. 1007/978-94-6265-261-3_6 9.
This form of the license distinguished Creative Commons from other open content licenses because it was an effective expression of rights into code. See Dulong de Rosnay 2016 59 Giannopoulou 2016; Dulong de Rosnay 2010. Ibidem Accepted manuscript The final publication is available at B. van Loenen et al. (eds. ), Open Data Exposed, Information Technology and Law Series 30, https://doi. org/10. 1007/978-94-6265-261-3_6 9 60 Maracke 2010. 61 Ibidem, p. 70. 62 Creative Commons goals and objectives. https://wiki. creativecommons. org/wiki/4. 0#Goals_and_objectives. Accessed May 2018. 63Maracke 2010, p. 79. 6.
Alternatively, the Ministry may receive information from national statistics offices or non-governmental survey organisations. Regardless of how the data are collected and transmitted, these are often stored away as reports in shelves or in databases at various levels of the health system, and are rarely used thereafter. The full potential to utilise these data for evidence-informed decision making and for improving public health is thus rarely achieved. Operational research (OR) is a discipline that encourages the use of routine data to enhance the quality, coverage, effectiveness and efficiency of health systems, health services or disease control programmes. 3 The capacity for undertaking this type of research and then using the research evidence to change policy and practice is poor in many low-and middle-income countries.
Presentation of the Tool Chain We model a hybrid system with a SysML state machine diagram, using the Papyrus Eclipse plug-in. Then, we transform the SysML model using a QVT operational model to model transformation into a Hy-Div model. HyDiv is a pivot meta-model that we designed to capture a high level representation of the hybrid system, independently of the source model (we could use Simulink/Stateflow instead of SysML). We then use an Acceleo model to text transformation to transform the HyDiv model into Xlia, the input language of Diversity, as shown in Figure  9. The SysML Model The hybrid system is modeled as a SysML Block with attributes for the variables and the behavior of the system. The variables have the Derivative_Type and the behavior is defined by a State Machine. In this state machine, the guards are written in OCL.
We use the OECD-REGPAT 1 database to identify technological fields and compute measures of relatedness and knowledge complexity. The OECD-REGPAT derives from PATSTAT: it contains all patent applications to the EPO between 1977 and 2011. Patent applications are regionalized at the NUTS-2 level by inventor address. 2  Although the geographical coverage of the data spans all OECD countries, the focus is restricted to the EU-28 plus Iceland, Norway and Switzerland. Patent applications are classified in technological domains according to the International Patent Classification (IPC). We use Cambridge Econometrics to measure regional population, population density and gross domestic product (GDP) per capita.
September 2015 Edition. OECD ¼ Organisation for Economic Co-operation and Development. 2. Fractional counts cleaned by inpadoc families of PAT-STAT. NUTS ¼ Nomenclature of Territorial Units for Statistics. 3. In this paper we used EconGeo, version 1. 3 (see https://github. com/PABalland/EconGeo). 4. Source: WIPO IPC Technology Concordance Table.
1. September 2015 Edition. OECD ¼ Organisation for Economic Co-operation and Development. 2. Fractional counts cleaned by inpadoc families of PAT-STAT. NUTS ¼ Nomenclature of Territorial Units for Statistics. 3. In this paper we used EconGeo, version 1. 3 (see https://github. com/PABalland/EconGeo). 4. Source: WIPO IPC Technology Concordance Table.
Data An anonymous, online web-based survey consisting of 120 items related to climate change knowledge, experiences, attitudes, concerns, and sustainable consumption practices was conducted across all states of Australia in mid-2010  (Reser et al. , 2012). A professional survey firm (Qualtrics) was employed to randomly select a gender-balanced cohort of panel members residing in each of 35 geographical regions. The final sample comprised 3,096 Australian citizens (47% male, 53% female) over the age of 15 years. Approximately 80% of the sample was aged from 25 to 65 years. Geographically, 71% of respondents described their residential circumstances as either urban or suburban, a further 17% as 'country town', and 12% as rural or rural residential. In terms of annual household pre-tax income, approximately half of the sample reported incomes between AUD40,000 and 100,000 (approximately USD37,800 to 94,300), with approximately one quarter reporting more, and another quarter reporting less, than this range.
The geographical distribution of the sampling procedure and ultimate survey sample reflected, in part, the nature of survey panels used by international research survey companies such as Qualtrics. Smaller centres were less likely to have resident panel members, whereas larger towns and cities had much larger numbers of available panel members. Given the largely urban population and coastal demographic distribution of settlements in Australia, the geographical distribution figures are not surprising and closely reflect national figures  (ABS, 2011). As the data was collected via an online survey, it is worth considering how accurate this data is compared to traditional survey approaches involving face-to-face interviews and telephone surveys. In this regard, a substantial review of the literature has examined the relative strengths and merits of traditional survey administration methodologies compared with online surveys, with the strong consensus being that well designed and executed webbased surveys typically achieve very comparable if not superior results to conventional best practice (e.
As discussed in  Chai et al. (2015) , the observed differences tend to be larger among those people who express the greatest concern about climate change. In terms of explanatory variable, we develop a proxy for the imitation process in regions by calculating the number of peers located in the same region and in the same age group as any given respondent. As noted in Hypothesis 3, the greater the number of peers located in a particular region, the greater the potential for consumers to imitate and learn from peers. Data about the number of people living in the same neighbourhood and their age groups were sourced from the Australian Census (ABS, 2011).
This is because our measure does not explicitly control for the fact that the rate of consumption is usually below 50% for our three composite variables. For example, the mean of the action variable is 6. 4 out of a possible 15. This means that if we take two "average" consumers and compare their MP profiles, even if none of the actions are taken by either of the consumers (maximum diversity), we will still have two actions which will not be taken by either of them. Therefore, the distance that we calculate will be at most 0. 87 (and not 1, which would imply complete dissimilarity across the two profiles). they have not taken. This will register as similarity of profiles. 17 Therefore, the normalized Euclidean distance constitutes the lowest boundary of similarity between consumption profiles in our dataset.
As such, these results support Hypothesis 2C that the imitation process appears to be driven by status concerns. This results suggests that the behavioral tendency to imitate the green behavior of peers does not generate spillovers in other, non-visible consumption domains, as implied by Hypothesis 2B. In order to check the robustness of these results, we used both a different geographic aggregation method as well as an alternative dependent variable. Concerning the former, we used the stated population centre instead of using postcodes to classify respondents into the 19 These results are available upon request. same regions. 20 In relation to the alternative dependent variable, we used census data that reported the number of peers located in that region that belonged to the same age group as the respondent (ABS, 2011).
A fascinating but rather crude start to examining the dynamics green behavior has been made  Melo et al. (2018)  who only have data on two observed time periods. 23 Given data on migration patterns across regions, it would be interesting to examine whether people migration between regions vary the number of MPs adopted according to changes in their social environment. In addition, similar to  Heffetz (2011) , it would be useful to empirically develop an accurate assessment of what type of MPs are considered visible by agents and whether there was any variation in the types of MPs considered to be visible across different age groups, regional areas and social networks. Finally, future survey work could consider examining how the propensity to adopt MPs relate to the structure of consumer lifestyles, which could measured by examined using question about the type of consumption routines individuals follow in major consumption domains and their implications for the adoption of MPs.
These readers are really gatekeepers, not an audience. Reading after publishing does happen sometimes, particularly among PhD students or junior staff, but this is an activity which is epiphenomenal to publishing itself. That is to say, the publication is the aim, and in a sense it doesn't matter what is written, as long as it produces a publication. Or, to be more precise, the aim is to produce a record of a publication, on a CV, in an institutional research database, on a website, in a state research exercise. The trace of a publication is ultimately the aim of the activity, the evidence that it exists, because this sign is a valuable commodity, and the more of these signs, spoors, tracks point to particular authors, the more benefits will accrue to them. Publishing, as I suggested earlier, is not the same as writing.
We study the performance determinants of private equity investing in emerging markets (EM) compared to developed markets (DM) using a novel dataset. Using a multilevel linear model specification, our results suggest that performance in emerging markets in highly dependent on geographical and cultural proximity. The effect is significantly higher for GPs investing in both markets compared to pure DM-and EM-players respectively. Cross-cultural and geographical effects are enhanced when the GP investment teams are also culturally close using different measures. Our results also show that the realized returns are highly dependent on the investment period, the investment style and the GP's experience on each market.