dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Urbanization and ageing have important implications for public mental health and well-being. Cities pose major challenges for older citizens, but also offer opportunities to develop, test, and implement policies, services, infrastructure, and interventions that promote mental well-being. The MINDMAP project aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive function of older individuals. Methods: MINDMAP aims to achieve its research objectives by bringing together longitudinal studies from 11 countries covering over 35 cities linked to databases of area-level environmental exposures and social and urban policy indicators. The infrastructure supporting integration of this data will allow multiple MINDMAP investigators to safely and remotely co-analyse individual-level and area-level data.
Individual-level data is derived from baseline and follow-up measurements of ten participating cohort studies and provides information on mental well-being outcomes, sociodemographic variables, health behaviour characteristics, social factors, measures of frailty, physical function indicators, and chronic conditions, as well as blood derived clinical biochemistry-based biomarkers and genetic biomarkers. Area-level information on physical environment characteristics (e. g. green spaces, transportation), socioeconomic and sociodemographic characteristics (e. g. neighbourhood income, residential segregation, residential density), and social environment characteristics (e. g. social cohesion, criminality) and national and urban social policies is derived from publically available sources such as geoportals and administrative databases.
Cities pose major challenges for older citizens, but also offer opportunities to develop, test, and implement policies, services, infrastructure, and interventions that promote mental well-being. The MINDMAP project, building on a novel database infrastructure, aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive function of older individuals. Funded from 2016 to 2020 by the Horizon2020 programme of the European Commission, MINDMAP aims to achieve its research objectives by bringing together ten longitudinal studies from eight European countries, the United States (US), Canada and Russia (in total over 35 cities of different sizes) linked to databases of area-level environmental exposures and social and urban policy indicators.
Linking micro-(i. e. individual), meso-(i. e. neighbourhood), and macro-(i. e. city or national) level data enables MINDMAP to investigate the causal pathways and multi-level interactions between characteristics of the urban environment and the behavioural, social, and biological determinants of mental well-being and cognitive function in older adults. Compared to studies based on a single country or city, integrating data from cohort studies in multiple cities offers many advantages for research exploring the impact of the urban environment on mental well-being. Harmonizing information across international cohort studies and combining them with data from different sources (physical, social and socioeconomic environmental characteristics, policy indicators) allows examining contextual determinants of variation in mental wellbeing across different populations and exploring the impact of neighbourhood, urban, and national policies for the prevention of mental disorders in older people.
Furthermore, integrating data increases sample sizes and statistical power necessary to identify high-risk population subgroups, study relatively rare health conditions, unravel causal pathways and explore interactions between risk factors. Finally, and potentially most relevant for studies investigating environmental influences on health, integrating data from different geographical locations increases the variation in environmental characteristics and policies that influence mental wellbeing and cognitive function both within as well as between cities. The MINDMAP database infrastructure will support these research objectives by integrating data from multiple sources and providing investigators with a platform to analyse it. The infrastructure will allow multiple MINDMAP investigators to safely and remotely coanalyse data from multiple sources and across different populations.
Variables and data sources MINDMAP is integrating data from numerous sources for different observational units. Individual-level data collected by longitudinal ageing studies will be combined with area-level urban characteristics and local and national policy indicators. Additional file 2 provides a visual representation of the structure of the MINDMAP project, including all workpackages and their relation to the different data presented below. A detailed overview of data used in the MINDMAP project is provided in Additional file 3. The selection of variables was based on scientific literature and a draft conceptual model on the influence of environmental factors on mental well-being and cognitive function that is being developed by MINDMAP investigators.
Area-level data Area-level information on physical environment characteristics (e. g. green spaces, transportation), socioeconomic and sociodemographic characteristics (e. g. neighbourhood income, residential segregation, residential density), and social environment characteristics (e. g. social cohesion, criminality) and national and urban social policies is derived from publicly available resources. Physical environmental characteristics Geospatial data is being collected from existing data portals, and cityspecific contacts across the MINDMAP study sites. In the European Union, publicly available spatial information has drastically improved thanks to INSPIRE  [3] , a 2007 European Directive that establishes a data infrastructure for the collection and distribution of spatial information in the European Union.
The European Data Portal  [4]  was systematically reviewed for all files containing items relevant to mental well-being or intermediary factors for all countries and cities of the participating European cohort studies. In addition, using the European Data Portal, relevant national, regional, and local data portals were identified and are systematically searched for relevant data that is not yet catalogued on the European Data Portal. Harmonized high-resolution land use data, road infrastructure files, and residential address databases of the general population over the study territory were obtained for all European MINDMAP cities. For its land use data, MINDMAP extracted data from the European Urban Atlas  [5]. This data is derived from satellite imagery and consists of 21 distinct categories, which capture a city's land use (including public green areas).
This data is being used to calculate individual 'greenness' exposure. In combination with the infrastructure information, measures such as nearest road network distance to urban green spaces are also being calculated. Point data of all residential addresses is used to determine population density. Information on facilities, transportation, and pollution have been obtained for a subset of cities from local and national data portals and are used to derive measures such as exposure to pollutants, access to public transport and availability of facilities. The CLSA is part of the Canadian Urban Environmental Health Research Consortium (CANUE), a pan-Canadian initiative which is gathering and developing measures of environmental characteristics such as greenness, walkability, air pollution, and socioeconomic conditions for every neighbourhood across Canada  [6].
National and local policies Data on national and subnational policies that range from proximal to more distal influences on the mental well-being of older people in an urban environment has been collected within the MINDMAP project to evaluate the effects of public policies on mental well-being outcomes. Existing, cross-city and cross-national databases such as the Social Insurance Entitlements Dataset (SIED)  [8] , the Labour Market Reforms (LABREF) database  [9] , the Eurostat databases  [10] , and the OECD Long Term Care database  [11]  were the principal sources for social policies such as old age pensions and social care. Urban policy indicators, such as transportation affordability and accessibility indicators, were collected for each MINDMAP city from the Eurostat Urban Audit database  [12]  and the OECD Metropolitan Indicators database  [13].
Mental health policy indicators, such as mental health system governance, resources and services were collected at the national level for European countries from the Eurostat Health Indicators database and the European Health for All database  [14] , and for all countries from the WHO Mental Health Atlas Country Profiles  [15]  and from two OECD data sources  [16, 17]. MINDMAP aims to collate such policy data for the past 30 years, and earlier, when applicable. When longitudinal data was not available, we collected the latest available cross-sectional data. In addition, data has been collected on local mental health promotion and prevention policies through interviews with experts in MINDMAP cities  [18].
The MINDMAP process To support cross-national research on ageing, mental wellbeing and the urban environment, the MINDMAP consortium adapted harmonization guidelines and software applications developed by Maelstrom Research  [19, 20]. These tools have been employed under similar collaborative health research projects such as BioSHaRE  [21] , InterConnect  [22] , and the Canadian Partnership for Tomorrow Project  [23]. Seven consecutives actions are being undertaken to establish an integrated database infrastructure allowing analyses of individual-and area-level data for research in ageing, mental well-being, and the urban environment (Fig. 2 ).
Define research questions As a first step, MINDMAP consortium investigators identified a number of research questions addressing the variation in mental well-being and disorders in old age, both within cities as well as between cities and exploring how environments and policies at different levels might influence mental well-being in later life. Table  2  shows main research questions to be answered with the integrated database infrastructure. In addition, more detailed domain-specific research questions were defined, to be explored by each work package (Additional file 2).
Document metadata The design of participating studies and the data they collect were documented on a web-based platform  [24]. This platform includes a search and query interface allowing MINDMAP investigators to quickly and easily identify studies collecting data items required to answer specific research questions. Questionnaires, standard operating procedures, and data dictionaries were also documented within the platform so that heterogeneity of data collection instruments could be properly assessed. Area-level urban characteristics as well as local and national policies of interest are also being documented.
Develop data sharing and publication guidelines In order to establish basic governing principles for the consortium, MINDMAP principal investigators drafted guidelines covering access and usage of cohort study data and publication of results. First, each cohort study's regular data access procedures will be respected, including the submission of access applications and obtainment of all required approvals from ethical review boards. Second, only data relevant to answer MINDMAP research questions is being requested. Third, after receiving all necessary approvals, these subsets of cohort study data will be hosted on firewall-protected servers. Participating studies were given the option of transferring a subset of their data to the coordinating centre's (Erasmus MC) server or installing a local server at their home institution.
Put in place IT infrastructure Given potential restrictions related to sharing of individuallevel data, a distributed database infrastructure was put in place to support data harmonization and cross-study analyses (Fig. 3 ). As such, a primary data server was installed at Erasmus Medical Centre in Rotterdam (the MINDMAP coordinating centre) to host datasets from studies whose policies allow the physical transfer of data to a third party. Cohort studies with more restrictive data sharing rules were given the option of installing secondary data servers in their own institution, which would be remotely accessible via encrypted connections (using HTTPS). Finally, a central analysis server running RStudio  [25]  was set up and allows authenticated MINDMAP staff and investigators to securely access firewall-protected data on the primary and secondary data servers (see step 7 below).
Research teams began by reviewing the variables collected by each cohort study and related documentation (e. g. questionnaire(s), standard operating procedures, data dictionaries) for their assigned domain, and identifying missing information or highlighting unclear variable definitions, codes, or values. Targeted variables for harmonization are then defined (e. g. current cigarette consumption -categorical: yes (coded as 1) or no (coded as 0); pack-years of smoking -continuous variable) and documented in a central MINDMAP GitHub repository. The choice and specific definitions of targeted variables is determined by the research questions that they will help to address and the actual data collected by each cohort. Once defined, the potential for each cohort to generate target variables is assessed.
Next, data harmonizers develop data transformation scripts to generate common-format variables in RStudio  [25]  on the password protected central analysis server. Decisions made and harmonization scripts applied for each studyspecific dataset are documented using cohort-specific RMarkdown documents  [26]  in the publicly-accessible MINDMAP GitHub repository, thereby making data transformation decisions open and transparent. Lastly, quality control checks are conducted on harmonized variables by comparing the distribution and counts of harmonized datasets to the data originally collected by each study. Fig. 2  Step-by-step process to establish the MINDMAP integrated database infrastructure Table  2  Main MINDMAP research questions to be answered with the integrated database infrastructure 1.
Link area-level data Addresses and postal codes of cohort participants will be used to link urban environmental characteristics and policy data (i. e. area-level data) to harmonized cohort data (Fig. 4 ). Given that the utilization of residential locations in research projects compromises study participants' privacy, the georeferenced information will be blinded in a step-by-step process. Firstly, the cohort data manager will generate new unique identifiers (UID2) for all individuals in cohort studies along with dummy (i. e. random) identifiers (DUID) and residential locations (home address or postal code) for approximately 5% of the total cohort study's sample (more if preferred). Second, a Link file containing UID2 and residential locations (RL) as well DUID and dummy RLs will be sent to the MINDMAP data manager.
Co-analyse integrated data Using a web browser and secure internet connection, authenticated MINDMAP researchers can login to the central analysis server outlined in step four and conduct on-demand statistical analyses on geographically distributed firewall-protected databases using the RStudio web interface. While some studies have given permission for individual-level data to be analysed by MINDMAP investigators, others have restricted data access to aggregatelevel information. For all analyses that include cohort studies prohibiting the use of individual-level data, the DataSHIELD approach is used  [27, 28]. Under Data-SHIELD, analysis requests are sent from the central analysis computer to the harmonized data held on the data servers. Computation is done simultaneously but in parallel on each data server linked by non-disclosive summary statistics.
Discussion The MINDMAP project aims to identify the opportunities and challenges posed by the urban environment for the promotion of mental well-being and cognitive function in later life. MINDMAP aims to achieve its research objectives by bringing together longitudinal studies from 11 countries covering over 35 cities linked to databases of area-level environmental exposures and social and urban policy indicators. The infrastructure supporting integration of this data will allow multiple MINDMAP investigators to safely and remotely co-analyse individual-level and area-level data through a single platform. The MINDMAP project has several important strengths. Integrating data from cohort studies in multiple cities and across various exposure or policy databases allows examining the role of contextual determinants on variations in mental well-being across different populations.
It also increases variations across these contextual determinants and it raises sample sizes and statistical power and, because the data is pooled from different regions and jurisdictions, allows exploring the effect of policy on mental well-being. The harmonization approach and tools that are employed by the project have been methodically developed by Maelstrom Research  [19, 20]  and put to use in similar research collaborations  [21] [22] [23]. These tools and approaches have been adapted to accommodate the specific needs of the MINDMAP project and ensure that all aspects of the harmonization project are carried out in a uniform, open, and methodical way to optimize the validity of the research results and transparency of the methodology. Moreover, the research teams contributing to the project bring a wide range of experiences and expertise that complement each other.
Streamlining this process while respecting each study's guidelines and regulations necessitates considerable time investments and meticulous planning. MINDMAP is a novel research collaboration which is combining population-based cohort data with publicly available datasets not typically used for ageing and mental well-being research. Integration of various data sources and observational units into a single platform will facilitate multilevel analyses exploring the influence of individualand area-level determinants of mental well-being. In the end, this infrastructure will help to explain the differences in ageing-related mental and cognitive disorders both within as well as between cities around the world and assess the causal pathways and interactions between the urban environment and the individual determinants of mental well-being and cognitive ageing in older adults.
Values of r 2 among LSMs are quite variable, and some higher values may result from similarity in model structures and forcing within GLDAS 1. 0 and GLDAS 2. 1 model groups. What Are the Uncertainties in TWSA Trends from GRACE? Differences between models and GRACE TWSA trends may result from uncertainties in GRACE data (SI Appendix, section 4). Generally good agreement in TWSA trends from the different GRACE solutions, with slopes close to 1 (range: 0. 8-1. 1) and high r 2 values (range: 0. 85-0. 94), provides confidence in the GRACE trend estimates (SI Appendix, Table  S6A ). Decreasing and increasing TWSA trends in TWSA from JPL-M tend to be larger than those from CSR-M, as shown by more intense colors in the global maps (Fig. 3 A  and B ) and slightly larger net trends from JPL-M relative to CSR-M trends (Table  3 ).
Although we could not apply the same climate forcing to all of the models, we tried to isolate the impacts of climate forcing on TWSA trends by running WGHM using different climate forcings. Results show variations in net TWSA trends summed over all basins: WFDEI forcing (-12 km 3 /y), Global Precipitation Climatology Center (GPCC) v. 7 (-6. 1 km 3 /y), and Climate Research Unit (CRU) TS 3. 23 (-23 km 3 /y) (SI Appendix, Fig. S18 ). Agreement between TWSA trends based on WFDEI and GPCC is high (r 2 = 0. 7, slope = 0. 9) but is much lower for trends based on WFDEI and CRU (r 2 = 0. 0, slope = 0. 3). We show TWSA time series for many basins, including precipitation forcing from WFDEI and the Princeton Global Meteorological Forcing Dataset (PGMFD) from NOAH-3. 3, to highlight variability in these two forcings.
Future Research This study focuses on comparisons in TWSA trends between models and GRACE to complement previous studies that focused on fluxes (e. g. , river discharge, ET). However, future studies should consider a multivalidation exercise that includes both storage and fluxes. Previous MIPs did not include modeled TWSA output; however, the ISIMIP group is now requesting TWSA output (ISIMIP2b) from modeling groups, and all of the relevant data should be available to compare both storage and fluxes among models. Discrepancies in TWSA between GRACE and models can reflect uncertainties in both GRACE and models. Uncertainties in GRACE output may be related to GRACE processing, which is continually improving and is currently at level 5. Future research should explore differences between GRACE solutions in different regions [e.
The SUMMA approach may be used to identify future model improvements and related data and research needs. Reliable data sources are also critical for modeling analyses. The International Land Model Benchmarking Project includes a number of data sources for hydrology, energy, fluxes, and vegetation dynamics (https://www. ilamb. org/benchmarks/). Such efforts should be expanded in the future. Large discrepancies between models and GRACE TWSA trends in many regions in this study indicate that detailed regional modeling efforts could complement global models. The current round of ISI-MIP models includes impacts on global water and regional water (https://www. isimip. org/about/). The Amazon and surrounding basins represent an obvious target area for regional modeling because of their large contribution to net TWSA trends (e.
Using an original database of 469 politically connected firms under the Mubarak regime in Egypt, we explore the economic effects of cronyism. Previous research has shown that cronyism is lucrative. We address numerous questions raised by this research. Do crony firms receive favorable regulatory treatment? They do: connected firms are more likely to benefit from trade protection, energy subsidies, access to land, and regulatory enforcement. Does regulatory capture account for the high value of connected firms? In our sample, regulatory capture as revealed by energy subsidies and trade protection account for the higher profits of politically connected firms. Do crony firms hurt aggregate growth? We identify the growth effects of the entry of politically connected firms by comparing detailed 4-digit sectors where they entered, between 1996 and 2006, and sectors that remained unconnected.
Firm Data To examine the economic effects of insider privilege, we need a dataset of politically connected firms under the Mubarak regime in Egypt, and information about firm performance and the regulatory environment confronting firms. Following Fisman (2001), we created a list of politically connected businessmen (all were men) by interviewing managers of banks and private equity funds, lawyers and NGOs (e. g. , anti-corruption organizations). At the same time, similar to Faccio (2007), we pruned the list to include only those businessmen who had influential political posts in the ruling party or in the government, or whose immediate family members did. Unlike Faccio, we also had sufficient information to identify long-term friends of the Mubarak family; these were also identified as connected businessmen.