dataesr/scientific-paragraphs-categorization huggingface dataset:
The field study did not capture the underlying mechanism. Therefore, to provide evidence for a part of the proposed mechanism, specifically the impulsiveness that gets triggered across the groups of varying cohesiveness due to the corresponding construal levels, we turn to a separate study. We collected smartphone usage data from 156 undergraduate student participants (males: 61%, females: 39 %), who were Android users at a large Asian university. A custom application running on their phones recorded the start and end times of each continuous duration for which they interacted with their phones. We define the session interval as the time lag between two consecutive app usage sessions. Shorter session intervals imply more frequent use of the phone. While the application recorded session timings and the names of the applications the students were interacting with, the specific content they were viewing was not captured as a means of preserving privacy.
The items were averaged to form an overall attitude measure (α = 0. 90). To check for the success of the manipulation of group cohesiveness, we used the same three-item scale as study 2a (α = 0. 80). Finally, they answered an attention check question where they were asked to recall the duration of the promotion ("According to the scenario, for how long is the promotional offer valid? Next week/ Next month"), reported their frequency of ordering food delivery ("How often do you generally either get takeaways or order food delivery?" 1 = almost daily to 4 = less than once a week), deal proneness ("How often 1 In order to ensure data quality in the Amazon MTurk sample, we included an attention check item after the dependent variable measure to confirm that the participants had read all the information before responding.
Introduction Vulnerable countries already experience first-hand the adverse effect of climate change. To tackle the challenges, these countries need access to state-of-the-art climate information, tools with financial support, and investments. Among concerted efforts, high-income countries have agreed to jointly mobilise significant financial resources to address the pressing mitigation and adaptation needs of developing countries, directly linking to Sustainable Development Goals (SDG) 10 Reduce Inequality and specifically to target 10. b  [1]. However, the implementation of the Paris Agreement  [2]  on climate adaptation and several other SDG such as 13, 2, 6, 14, and 15 (Climate action and other depending on it) demand easy access and readily available hydroclimate data on variability and change, information for assessments around the globe, and interpretation of the climate change signal when linked with societal impacts to create climate actions for a new climate resilience region; all these in especially data-sparse regions worldwide.
Materials and Methods The platform contains climate information produced following an extensive production chain; starting from climate model data and going through several refinement steps and tailoring, including the usage of observed data, before it is ready to be transferred to users and incorporated into decision-making. The technically and computationally demanding production chain is applied for the calculation of climate indicators. The use of indicators is an efficient way in making climate information accessible to users within a sector, as the indicators concentrate the outcome of the climate models into a smaller quantity that is sector relevant.
5 degree), and finally the calculation of WI at a catchment scale using a global hydrological model. Specifically, the CIs are calculated from non-bias adjusted CMIP5 ensemble at 2 degrees and at 0. 5 degrees from the bias adjusted ensemble. CI calculated from CORDEX ensembles are calculated at 0. 5 degrees from non-and bias-adjusted ensembles, respectively. 2. 1. 1. Global and Regional Climate Models The CORDEX data used here were available for ten different domains: Africa, Arctic, Australia, Central America, Eastern Asia, Europe, Mediterranean and North Africa, North America, South America, Western Asia; all at 44km original resolution. For each domain, The selection of CI and WI was agreed at an early stage of the development of the platform and was based on previous experience gathered from working with different user needs.
The selection of climate models primarily serves the need of global coverage and the use of CORDEX domains and models. The use of the CORDEX data was a prerequisite in this development, making the platform, at least to our knowledge, the only web-based platform which provides CORDEX based CI for most of the CORDEX domains. To facilitate both near future and far future assessments by the users, all indicators are calculated as absolute values for reference period , and as relative or absolute changes depending on the indicator for the 3 future periods  (2011-2040, 2041-2070, 2071-2100). An ensemble of model results is provided for each CORDEX domain and for the CMIP5 ensemble, to indicate confidence in the estimates (mean, median, 25th, and 75th percentiles). All indicators are produced in two formats: NetCDF) and Excel with metadata linked to them.
All data and models underwent a number of quality checks before including them in this production. Although both Model Intercomparison projects (CMIP5 and CORDEX) follow quality assurance protocols, additional checks were required to meet the needs of the production chain of indicators. This is usually necessary when developing a climate service for a wide range of users, among them non-scientific users (e. g. ,  [20] ). The checks involved file format, data gaps, overlapping periods, missing values, units, data and domain dimension, metadata information, and calendar adjustments. Ranges checks also took place to identify unrealistic values. Small data gaps (few days) were handled by copying the preceding time step or data for the same days of year from the previous year. Expert judgement was used to disqualify models and scenarios with large number of unrealistic values or errors in the model experiment.
Bias Adjustment Daily precipitation and temperature variables from both CMIP5 and CORDEX models were bias adjusted and downscaled to a 0. 5-degree spatial resolution using the Distribution-Based Scaling method (DBS,  [21] ) with a global gridded reference dataset called Hydrological Global Forcing Data (HydroGFDv2. 0) [22]. Bias adjusted variables from RCM are used to calculate the climate indicators at a 0. 5-degree spatial resolution. The bias adjusted variables (precipitation and mean, maximum, and minimum temperature) are also used as forcing input for the global hydrological model World-Wide Hype (WW-HYPE,  [23] ) to calculate water related indicators at catchment resolution. The bias adjustment method, reference dataset and global hydrological model are developed by the Research Department of the Swedish Meteorological and Hydrological Institute (SMHI).
For assessment of hydrological variables, WW-HYPE was run for each of the CORDEX domains and ensemble separately. The CORDEX domains provided an extra difficulty for the hydrological assessment since river basins can cross CORDEX domain boundaries, leading to inconsistent forcing data over such basins. This was dealt with using the following quality check after the runs: If the upstream area of a river catchment or a river stream was located outside of the CORDEX domain boundaries, then this basin or stream was excluded from the simulations (grey areas in maps) and the user should use another domain. This applied for all the WI listed in Table  3. All WI were defined by SMHI hydrology experts and were chosen in this prototype since they are frequently used in hydrological assessments. Qualitative assessment of validity of WW-HYPE outputs were performed through diagnostic map plots.
The Climate Information Platform-Main Tools There were two online tools developed in the prototype phase of the Climate Information; the Site-Specific Report (SSR) and the Data Access Platform (DAP). Each tool was designed to serve different users and their needs. The SSR target non-scientific audience who is looking for quick overviews of climatic change and want to have a ready-made analysis for a location. The DAP was designed for users with scientific expertise who want to make their own additional analysis having direct access to climate information and data. the content and design resulted from the feedback process that was implemented during the workshops, with the results presented here.
Site-Specific Report (SSR) In the SSR, the user can select a location by filling in a city, coordinates (latitude, longitude) or click directly on the map. Further options are RCPs and future time periods (2011-2040, 2041-2070, and 2071-2100). When selections are made, an overview is generated using the best available climate models-CORDEX data are set as priority-depending on the selected location. When a location is not covered in the CORDEX data then CMIP5 data are used. Both CORDEX and CMIP5 data are bias adjusted as explained in the previous section. For the overlapping areas in the CORDEX domain, a priority is made to pre-select the domain with the higher number of models. The change in six top indicators (mean temperature, precipitation, aridity, soil moisture, and water discharge and water runoff) is listed at the top of the generated overview (Figure  4 ).
Data Access Platform In the DAP (Figure  6 ) the user can make detailed selections for which data to visualise over the world. The right side of the DAP provides a short user guidance film and quick access to the User Guidance and Knowledge base pages. It also includes information about how to cite the data at https://climateinformation. org/. There are three menu buttons available at the top: "Indicators", "Climate Models", and "Past & Future". In the Indicator menu the user can select an indicator type and an indicator. It is possible to view 30-year averages as annual values or monthly values. In the climate model selection, the user can select between global models (CMIP5) and regional models (CORDEX, bias adjusted or not). Since this is a prototype, the menu of the DAP is currently under development and a future redesign of the menu should be expected.
This will not however alter the way the data are presented in the DAP. In summary, the CI and WI are available from CMIP5 and CORDEX data at global and regional level for a reference period and three future periods. The indicators in the historical period are shown in absolute values, while for the future periods the indicators are shown as changes with respect to the historical period (%, days, or • C, depending on the indicator). The user can select different RCP (2. 6, 4. 5, 8. 5) depending on the availability of each domain. The CI are provided at global scale at 2-degree resolution using CMIP5 ensemble, at regional scale at 0. 5-degree resolution (bias and not bias adjusted) using the CORDEX data for each domain. The WIs are provided at a catchment resolution using either the CORDEX or CMIP5 forcing data.
Some users with no background in climatology and who are policy makers or managers are more interested in key messages and summaries such as the ones in the SSR tool. More technically advanced users (researchers or consultants) require or are more willing to use data that they can process themselves and include in their analysis. The DAP tool was designed for this purpose. To avoid potential misuse of the data, the Climate Information platform was equipped with user guidance up-front and also an extensive knowledge base with basic explanations of climate-science terminology and procedures. Participants mostly use a climate service for a quick overview of climate trends, but many also want to access and download data and learn about climate science (Figure  9  left). Those that generally want the quick overviews also indicated that it was to become more knowledgeable, to teach others, or to make a synthesis (from raw data) (Figure  9  right).
The main reasons climate information was needed were to decide if more detailed analysis was required, to become more knowledgeable, and to get data for their own impact model. Based on this user understanding, the SSR tool was then refined to provide clear information (metadata) regarding the indicators and to summarise key message regarding the future changes and confidence metrics. In the DAP tool a detailed graphical representation of the past and future changes was implemented presenting the full range of the ensemble with the median value and upper and lower quantiles. the participants were researchers (27% of respondents) with policy makers and consultants also being very common (Figure  8  middle). The majority (64%) of workshop participants had more than 10 years of experience and these were mostly in researcher or consultant roles.
The main reasons climate information was needed were to decide if more detailed analysis was required, to become more knowledgeable, and to get data for their own impact model. Based on this user understanding, the SSR tool was then refined to provide clear information (metadata) regarding the indicators and to summarise key message regarding the future changes and confidence metrics. In the DAP tool a detailed graphical representation of the past and future changes was implemented presenting the full range of the ensemble with the median value and upper and lower quantiles. Temperature and precipitation were the most commonly selected variables of interest, with soil moisture also often selected (Figure  10 left ). The participants seem to know which variables were most important for their purpose.
Understanding Users Questionnaire For the free text exercise on Understanding the Users 80 responses were received in total from the three workshops (Democratic Republic of Congo excluded). The results are summarised and presented in Appendix C. A total of seven main types of users were identified based on the current role that the participants provided: Administrator (3 replies), Coordinator (15 replies), Meteorologist (7 replies), Policy makers or Government level role (16 replies), Scientist, Research or Data analysis role (23 replies), Technician or Technical role  (13) , and Other (3 replies). The participants in all workshops have a basic level of understanding climate change, its impacts and societal importance (e. g. , under insight in most groups, Appendix B). Users are generally motivated by a desire to improve decision making processes for sustainable societies facing climate change.
The technical group were specific about being motivated to using tools and data to understand climate change. Of the seven groups, four (coordinators, meteorologists, scientists, policy makers) mentioned that networks and institutional collaborations were needed to improve capacity. All groups want further support, education and guidance in climate change science. This resulted in platform development of the Guidance and Knowledge Base sections. A significant effort was allocated to improve the Knowledge Base in the platform with respect to the results of the feedback exchange. The guidance available in the platform is design to educate the user in different steps required when using climate information and the tools in the platform. The Knowledge Base is being updated to incorporate even more FAQ that answer specific questions identified in the feedback process.
At the same time, this enhances the applicability of climate research and scientific knowledge in policy and context-specific domains. This can subsequently lead to improved interventions at the national and local scale. Although this has not been the focus of our project at the time of implementation, it is an essential part of the WMO methodological approach and will be addressed in follow up efforts in the future. The Climate Information prototype is still under development. A next step is needed for this information to be used into client-tailored climate services, including relevant impact-based data and forecasts and decision-making tools that the user can make use of. This necessitates a process of partnership-building, iterative dialogue, and feedback among providers and users of the service.
Acknowledgments: The data presented in this study are openly available in https://climateinformation. org/ (climateinformation. org is developed by SMHI on behalf of WMO, WCRP and GCF). We acknowledge all the participants of the WMO workshops for their engagement during and after the workshops regarding the Climate Information platform. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP and CORDEX, and we thank the climate modelling groups (listed in Appendix A of this paper) for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organisation for Earth System Science Portals.
Using climate data adds value to projects, need data (precipitation, water runoff, water quality, soil moisture) to provide sound basis for proposals. Motivated to use generated indicators later to check original assumptions. Want to know more about tools for climate change data, be the central agency to help collate required data. Motivated to contribute to decisions based on trusted climate science. Need to make decisions, research, add value to project assessments, improve political decisions for adaptation to climate change. Need new approaches to problem solving, need to protect the environment and create resilience for the future. Driven to write better proposals for GCF. Want to improve the networks, cooperation between national and international institutions, know climate of the region Need to build collaboration between institutes, combine databases, verify local/seasonal trends, the right tools to analyse climate data, to improve knowledge management capabilities.
Need to have basic understanding climate data, be able to transform international information into relevant national information. Need to build technology and institutional collaboration, use known tools and methods that are adjusted for the region, gain expertise and data ICT, Statistics, GIS, WRMA, Agriculture, basic level of understanding but need capacity for model choice, data inputs to generate useful outputs for proposals. Also need capacity in evaluating different datasets. Limited modelling experiences. Need understanding of climate change processes, links between global and local scales, and understand uncertainties. access, training in analysis and research. Need institutional collaborations, coordinated platforms and systems for data sharing and management (with (Cabo Verde). UNFCCC roster of experts Have an expert on training in sustainability.
climate change data, skills in interpreting Need qualified personnel, departmental structure. holistic approach in quality assurance). A Everything is needed Specialised education, training and guidance needed for using climate information, to build databases, to understand climate modelling, to conduct specialised studies. English language needed; collaboration with international experts. Remote assistance needed. Showcases needed, training in how to access data, to use and understand new products as they become available. Training and guidance in Education and guidance in modelling, examples of 'how to', data collections, using provided tools and methods. Both face-to-face and virtual interaction platforms needed. Develop regional capacity to learn and deliver, then extra-regional institutions provide support.
Climate projection tools. Training in downscaling, bias adjustment and local climate assessments climate change in general. Education and specialised (thermometers etc) observation equipment proposal writing and climate risks and uses, needed. Training in face-to-face training continuous workshops, making abilities, needed. Develop decision Education and long term guidance, specifically in modelling, database guidance needed. Support can be provided through communities of practice, online forums, youtube tutorials so can develop water availability indices management, training in using the platform are and models.
In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample.
Introduction In a typical scientific investigation, one researcher or a small team of researchers presents analytical results testing a particular set of research hypotheses. However, as many scholars have argued, there are often numerous defensible analytic specifications that could be used on the same data, raising the issue of whether variations in such specifications might produce qualitatively different outcomes  (Bamberger, 2019; Cortina, Green, Keeler, & Vandenberg, 2017; Gelman, 2015; Gelman & Loken, 2014; Leamer, 1985; Patel, Burford, & Ioannidis, & 2015; Saylors & Trafimow, in press; Wicherts et al. , 2016). This question generally goes unanswered, as most datasets from published articles are not available to peers  (Aguinis & Solarino, in press; Alsheikh-Ali, Qureshi, Al-Mallah, & Ioannidis, 2011; Savage & Vickers, 2009; Vines et al.
Underscoring the pitfalls when published analyses of complex datasets focus on a single primary specification, two papers were recently published in the same surgical journal, analyzing the same large dataset and drawing opposite recommendations regarding Laparoscopic appendectomy techniques  (Childers & Maggard-Gibbons, 2020). In the crowdsourced approach to data analysis, numerous scientists independently analyze the same dataset to test the same hypothesis  (Silberzahn & Uhlmann, 2015). If similar results are obtained by many analysts, scientists can speak with one voice on an issue. Alternatively, the estimated effect may be highly contingent on analysis strategies. If so, then subjectivity in applying statistical decisions and ambiguity in scientific results can be made transparent. The first crowdsourcing data analysis initiative examined potential racial bias in organizational settings, specifically whether soccer referees give more red cards to darkskin toned players than to light-skin toned players  (Silberzahn et al.
, 2018). The project coordinators collected a dataset with 146,028 referee-player dyads from four major soccer leagues and recruited 29 teams of analysts to test the hypothesis using whatever approach they felt was most appropriate. The outcome was striking: although approximately two-thirds of the teams obtained a significant effect in the expected direction, effect size estimates ranged from a nonsignificant tendency for light-skin toned players to receive more red cards to a strong tendency for dark-skin toned players to receive more red cards (0. 89 to 2. 93 in odds ratio units). Effect size estimates were similarly dispersed for expert analysts, and for analyses independently rated as high in quality, indicating variability in analytic outcomes was not due to a few poorly specified analytic approaches.