dataesr/scientific-paragraphs-categorization huggingface dataset:
According to the Open Knowledge Foundation, the main defining element of open data is that it should be freely used, re-used, and redistributed by anyone. Within this spectrum, open data initiatives operate under the premise of providing free access to datasets in key areas of urban development, thus increasing the information base, and potentially empowering citizens  (Baack, 2015). Sieber and Johnson (2015)  discuss different pathways of how open data can mediate between the state and civil society, from simply opening up government data to a participatory design, in which governments and citizens co-produce data together. To grasp such collaboration, open data research and practice extensively refer to the term "open data ecosystem" defined as a loose group of diverse actors with various skills and interests, linked by dynamic interaction through cooperation, competition, and negotiations  (Zuiderwijk et al.
Data were gathered through semi-structured interviews and content analysis of the platform, as well as through document research. Interviews were focused on those actors identified as shaping the goals of the open data initiative and platform and directly involved in their development. Therefore, no interviewees were recruited among platform users (that are also more difficult to identify). Instead, interviews were conducted with elected officials and decision-makers, platform developers and managers, as well as members of civil society associations working with the topic of open data (Table  2 ). Interviews were held during January and October 2019 in Lyon. In Berlin, initial interviews were conducted in early 2018 and updated in summer 2019. Interview questions were addressing the actors' conceptions of open data and how they relate to the open data initiative, both in terms of their influence and potential divergence.
Document analysis focused on core policy documents, such as strategy papers and practical guidelines, websites, reports and promotion materials edited by city departments, consultancies, and members of civil society. We also included press articles, legal acts and other administrative documents. This corpus consists of documents covering the publication and use of open data, as well as economic development and smart city strategies. Open data platforms were analyzed by their traceable evolution over time, structure, functional possibilities, available datasets, as well as data contributors and users. The information from these sources was ordered chronologically and according to the concerned actors. Interviews, as well as open data editorial entries were coded using the following main categories: structure (context, barriers, windows of opportunity), agency (strategies, projects), conceptions (visions of open data, objectives) and relationship toward other actors (references to the "ecosystem, " cooperation and rivalries).
However, some uncertainty remained among the administration about which data to publish, epitomized in the Open Data Handbook recently issued in November 2019  (SenWEB, 2019). Lately, the topic of open data has been trickling through different institutions, resulting in the engagement of some branches and agencies in their own open data projects, such as the Berlin Police Department hosting a frequently updated "Crime Atlas" since late 2018. Further hopes for collaboration between the state and civil society are invested in the City Lab opened in mid-2019, a state-sponsored space for events and co-working, offering its professional staff and own funding scheme for projects.
In 2017, the Foundation gained a large project (ODIS) to support the implementation of open data across all the branches of the administration according to the eGovernment Law. This project provides a knowledge database as well as organizational and personnel resources to provide advice for administrations to implement open data. The third group includes civil society organizations and think tanks with a focus on lobbying, as well spaces for hacking and coding, where activists work on the practical development of open data applications and projects. In the case of the Open Knowledge Lab and Foundation both functions are paired under the same umbrella. Whereas, the Foundation is focused on influencing policy and distributing technical solutions for open data, mainly through informal networks and events, the Lab founded in early 2014 is a space for experiments, projects, and software development.
Obviously, their activity very much is motivated by civic conceptions of open data. The apps and programs developed by the Lab are based on the data published by the city-i. e. , informing on construction projects, Christmas markets, public transport data, providing information for refugees, or listing the most popular given names-serving as demonstrators for "open knowledge, open data, transparency, and civil participation" according to their webpage. OKL members were issuing applications on specific topics before the official authorities, as in the case of several apps on water quality in the city's lakes. Beyond displaying data, members also work on programs that "scrape" information from datasets that have not yet been treated to satisfy open data standards. Before the opening of the City Lab as an institutionalized forum between the administration and civil society, connections with the administration were established on a personal level.
As one interviewee described, "we show you what we can do with your data; we will show the advantages, and we will work for free with your data" (B-OK1). Such collaborations did result in access to datasets hitherto hidden, but also produced personal networks while relying on the goodwill of administration officials on different levels. Fourthly, in terms of economic actors, there appears a split between start-up businesses, often portrayed as the addressee of open data policies, and the major utilities, as data providers who are reluctantly sharing parts of their operational data. The position of start-ups in this field is weak in different ways: small businesses presumably do not have the capacity for lobbying and political work beyond their own interest, they have little resources to offer services for free, and they are affected by a shift in the administration strategy regarding open data (B-RI).
A Growing Stock of Data: Berlin's Open Data Platform The central role of the administration and the existing links with civil society and research actors are mirrored in Berlin's open data webpage daten. berlin. de, which is subcontracted to an external provider. As of September 2019, more than 1,700 data sets (most of them of public origin) are accessible on Berlin's Data Portal, covering such diverse topics as kindergarten placements, a catalog of destroyed premises after World War II and recent figures on public finance, under standard licenses guaranteeing completely open access and use  (SenWEB, 2019). The backend of the portal is organized with the software CKAN that was initially developed by the Open Knowledge Foundation and today is used globally for the publication and arrangement of data.
Datasets are ordered by their date of publication, and a search engine based on meta-data such as keywords and topics (e. g. , "transport and traffic") is to help navigating the amount of data; beyond there are no tools making the page more accessible for non-experts, but a feedback forum indicates that some of the visits can be traced back to Open Knowledge Lab members seeking to improve datasets and webpage functions. According to the published statistics on the webpage, the data portal registered between 6,000 and 17,000 visits per month. However, there is no data available on the background of users, while impressions from the webpage underline an expert audience and its general usage as demonstrator for data and possible use cases.
Supporting this network approach, the association founded the living lab "Tubà" as an arms-length agency for fostering economic collaboration: "Sometimes, large corporations working with us, ask: 'Do you have an excel file of start-ups?' -Well no, it doesn't work like this. We create this alchemy,  [. ]  the idea is that we can create a synergy and animate it" (L-T). The association Lyon Urban Data and its physical space Tubà are acting as intermediaries in the ecosystem of local economic actors, specifically supporting start-ups in the field of smart city (L-T). One of the most featured showcases of Tubà was ForCity, a start-up developing 3D-modeling software for urban development scenarios. In total, Tubà has contacts with 150 start-ups and 15 of them have been accommodated in its incubator  (Tubà, 2019).
This demonstrates how the shifts in urban governance created ground for new organizational structures to arise, improving the administration's leverage in negotiating publicprivate partnerships, and enabling it to build up personnel, both internally and subcontracted, with skills and expertise outside classical bureaucratic training, namely in business, technology, and ICT (L-OD1, L-T). Thus, Lyon was the first French city to recruit a Chief Data Officer in 2015  (Januel, 2018) , and the same year, the GrandLyon Smart Data platform was remodeled under the name Data GrandLyon, since then publishing data from both public and private actors involved in the collaboration mentioned above. Further on, in 2016, GrandLyon hired a Data Platform Product Manager, responsible for web development. He was one of the actors behind the third version of the platform, based on a more flexible code that enables to continuously add new functions, enhancing the platform's interaction features (L-OD2).
Data Platform for Economic Value and Public Services The platform Data GrandLyon mirrors the economic orientation of the actors behind it, but even more, the collaboration between the administration and infrastructure providers (see Table  4 ). The three versions of the platform, described above, reflect three underlying processes. First, the consolidation of resources enabled by the administrative reorganization in 2015. Second, while the first steps targeted the publication of "low hanging fruit" datasets, further adjustments aimed at widening the pool of data contributors, especially from the members of the Lyon Urban Data network. As of April 2020, there are 27 public and private data providers listed on the platform with, however, only two civil society associations, involved in environmental issues.
Third, the demand for more interactivity was recognized for users to engage with the data. In the current version, the underlying open-source code enables to add new functions over time, permitting to introduce more interactive tools, and, potentially, more communication with the platform managers and between the users (L-OD2). In fact, the latest version of the platform launched in April 2019 was in a beta-testing phase until May 2020, which permitted user feedback and the incorporation of new features. Today, the platform features 773 datasets that range from 3Dmaps and a 1-h traffic forecast to rainfall measurements and street markets, searchable by meta-data and filtered by types, themes, and dates. From the beginning, the platform offered data under three different licenses. The Open License, created by Etalab and compatible with most standard open licenses, covers 94% of Data GrandLyon datasets.
Apart from a general explainer video, there are no tutorials for users less technically skilled, although the platform management has considered creating a forum for questions (L-OD2). There is no possibility to submit datasets for publication directly on the website, but a new page "Contribution" has been recently added encouraging those who would like to contribute their data to follow a conventional "Contact us" form, which is still the only interaction channel apart from an email address and an opinion poll that is deliberately left on the website after the beta version, according to the corresponding news entry. Recently, a new page "Reuse" was added, containing applications that were developed on the basis of data provided by the platform (Figure  2 ). This depicts eight projects with the datasets they use, among which four are operated by GrandLyon itself, like Toodego helping to find various urban services (libraries, waste facilities, mutual aid during the COVID-19 pandemic, etc.
The VLKO platform has received less political support, financial and personnel resources than Data GrandLyon, and its platform is more volatile to the support of individual politicians. It was created at the initiative of a city official to gain higher priority of housing policy, by analyzing and mapping data with input from citizens. Since then, the VLKO team had been frequently working with civil society actors, such as neighborhood citizen councils (participatory bodies advising the city hall) and citizen associations to support their discussions with statistical data, analytical software, and participatory methodology. However, the cooperation between VLKO and citizens has recently been limited by the skepticism of current officials in power about the role of neighborhood representatives growing too strong: "There is an implicit limit that should not be crossed.
Back in 2014, GrandLyon and the City of Lyon started exploring possibilities for incorporating VLKO into the Data GrandLyon platform, serving as a special tool for accessing and visualizing demographic and socioeconomic data. However, soon after the formation of Métropole GrandLyon, these discussions were paused for the lack of time, due to an overflow of tasks related to the reorganization, and were never resumed as a result of a political power struggle between the two territorial entities. Another window of opportunity closed with the termination of an administrative unit for acquiring European project funding; this unit was shared between the City of Lyon and Lyon Métropole. Its closure resulted in a vacuum of communication channels for the topic of open data, limiting the possibilities for collaboration, even though VLKO management claims to have continuously pushed the agenda of platform integration.
A Civic Conception of Open Data Owing to its collaboration with civil society organizations, VLKO largely shares the latter's conceptions of open data. The VLKO website is dedicated to opening data on social issues such as pollution and health, employment and education, housing and social benefits, children and elderly people. In addition to data, the platform offers online cartography tools developed by Cité Publique in open domain enabling visualization of different local and national datasets with geographical distribution by such socio-demographical characteristics as age, level of education, professional occupation, household size, vehicle ownership and even library subscription. Apart from the data produced by the City of Lyon, some datasets come from open public sources like the national statistics bureau INSEE, others from public organizations, obtained through mutual cooperation or data requests.
" A distinctive feature of VLKO is its participatory approach toward using datasets on relevant societal issues like public health and environment. The citizen workshops they are running start with identifying questions that are of most interest to the participants. Then, the relevant datasets are selected (or found in external sources if they are not yet present on the platform), combined and visualized with the help of VLKO's cartography open-source software to support an informed discussion (L-CL). Thus, open data are not regarded as the end goal, but rather as a side-product of the VLKO initiative, as will become apparent in the next section looking at the platform interface and content.
Civic Data for an Expert Audience Datasets published on the VLKO platform are not exhaustive and tightly up-to-date, but they are collected and published as "support tools to strengthen the deliberation capacities, both within the municipal organization, as well as with the nonprofit partners of the City and with the inhabitants, " according to the platform. In this logic, the online platform itself is more oriented toward professional (or activist) users than "ordinary" citizens, owing to the fact that the demand for data comes from within the municipality or from citizen workshops, which are conducted by experts who know how to use it (Figure  3 ). The platform is not referenced on the central website of the City of Lyon, but promoted on the website of the DDT, rendering it more difficult to find for those not involved as workshop participants.
Data were generally published by the government and infrastructure corporations, but rarely third parties. The portals in both cities where complemented with a few applications developed by civil society groups, businesses, or the administration itself. In Lyon, this was the result of a formalized cooperation in a networked association; in Berlin collaboration between potential data providers remained more informal. More so, even though users were not the focus of our study, the navigation of the data platforms, and the lack of introductory tools therein, indicate advantages for expert and professional users. However, data can create visibility for social issues as figured in the VLKO platform, illuminating a broader potential of open data. First, open data platforms make an increasing number of datasets accessible to the public, partially based on open software running in the backend of urban portals.
This process might act as a selective filter for datasets to get uploaded, which, especially in Lyon, resulted in side-lining data addressing civic and social issues on the metropolitan platform. Local governments pushing their agenda too narrowly might leave civic actors unwilling to consider themselves part of an ecosystem that, in their view, is not likely to pursue civic conceptions of open data. More than that, local governments distribute physical, financial and personnel resources into projects to create thrust corresponding with their agenda for developing open data. Our analysis underlines that grasping the governance contexts, the resource distribution, as well as the strategies of the most powerful actors is a stronger predictor for the development of open data initiatives than the assumption of a benevolent and self-regulating evolution of open data ecosystems.
This paper is organized as follows: in §2 we describe our data for both observations and simulations; we then discuss how we measure mass density profiles in observations ( §3) and simulations ( §4); we present results from comparing measurements in observed and simulated galaxies in §5 and discuss potential reasons for disagreement in §6; finally we summarize and conclude in §7. We provide median surface mass density profiles and weak-lensing ∆Σ profiles for the HSC galaxies here: https: //github. com/f-ardila/HSC_vs_hydro-paper. The following cosmological parameters are assumed throughout: H0 = 70 km s -1 Mpc -1 , Ωm = 0. 3, and ΩΛ = 0. 7. We use the virial mass for dark matter halo mass (Mvir = 4π∆(z)ρc(z)R 3 vir /3) as defined in  Bryan & Norman (1998). All length units use physical units (not comoving).
One-Dimensional Surface Mass Density Profiles We measure the 1D stellar mass density profiles of HSC galaxies from their HSC i-band images using the method of  Huang et al. (2018b). We use the galaxy surface brightness profile function (galsbp) in the publicly available kungpao package 2. First, we apply an empirical background correction and mask out neighboring objects based on their brightness and proximity. Then, we draw concentric elliptical isophotes with a fixed ellipticity on the target, and at a given radius (along the semi-major axis), we measure the median intensity along each isophote after iterative 3σ-clipping. The ellipticity parameters are computed from the intensity-weighted moments of flux distribution of the object in the map using SEXTRACTOR  (Bertin & Arnouts 1996 ; through the SEP PYTHON library).
As a reference, 1. 0 corresponds to 3 and 6 kpc at z = 0. 2 and 0. 5, respectively, and the mean i-band seeing has FWHM = 0. 58. Our HSC WIDE data can reach > 29 mag/arcsec 2 in surface brightness radial profile measurements in the i-band. It is known that the hscPipe deblending process is not optimized for these extended objects and that it tends to oversubtract background light. To avoid these problems we use SEXTRACTOR  (Bertin & Arnouts 1996)  background subtraction and object detection (using the SEP PYTHON library) to generate the appropriate masks. Appendix B in  Huang et al. (2018b)  gives more details about the masking performed in our HSC sample. As is shown in that paper, different masking methods do not affect the measured surface brightness profiles of HSC galaxies within 100 kpc.
For our massive galaxies, we measure their ∆Σ profiles using the weak lensing shape catalog defined by  Mandelbaum et al. (2018). We follow the strategy outlined by  Singh et al. (2017)  and use the dsigma code 3  , which is optimized to work with HSC-SSP data. See  Speagle et al. (2019)  and  Huang et al. (2019)  for details of the g-g lensing measurements. With the help of these g-g lensing profiles and the deep stellar mass density profiles, we are able to build an empirical model that connects the stellar mass distribution of massive galaxies to their underlying halo masses  (Huang et al. 2019). This model (referred to as ASAP) is based on the Small MultiDark Planck (SMDPL;  Klypin et al. 2016 ) N-body simulation of halo merging history and a modified version of the UniverseMachine semiempirical model  (Behroozi et al.
• We provide median surface stellar mass density profiles and weak-lensing ∆Σ profiles for the HSC galaxies (https:// github. com/f-ardila/HSC_vs_hydro-paper) to facilitate comparisons with other simulations. In the era of large surveys that are wide, deep, and that have lensing capability (HSC, LSST, Euclid, WFIRST), joint comparisons between weak lensing and galaxy stellar profiles offer a powerful method for comparing simulations with observations. In particular, this combination provides a direct test of whether simulations build and deposit galaxy mass in the correct dark matter halos and therefore constrains the physics of feedback and galaxy growth.
ACKNOWLEDGEMENTS FA is supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1842400. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958. This material is based upon work supported by the  National Science Foundation  under Grant No. 1714610. This research was also supported in part by  National Science Foundation  under Grant No. NSF   PHY11-25915. AL acknowledges support from the  David and Lucille Packard foundation , and from the  Alfred P. Sloan Foundation. We acknowledge use of the lux supercomputer at  UC Santa Cruz , funded by  NSF   MRI grant   AST 1828315. PM acknowledges a  NASA  contract supporting the  WFIRST-EXPO Science Investigation Team  ( 15-WFIRST15-0004 ), administered by the  GSFC.
Funding was contributed by the  FIRST program  from  Japanese Cabinet Office , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) , the  Japan Society for the Promotion of Science (JSPS) ,  Japan Science and Technology Agency (JST) , the  Toray Science Foundation ,  NAOJ ,  Kavli IPMU , KEK, ASIAA, and  Princeton University. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.