dataesr/scientific-paragraphs-categorization huggingface dataset:
One of the key resources of the Atlas repository is the direct availability of climate datasets aggregated regionally over the new AR6 WGI reference regions  13  (datasets-aggregated-regionally folder). This dataset consists of monthly precipitation and near-surface temperature from the IAD spatially averaged over the reference regions for CMIP5, CMIP6 and CORDEX model output datasets, separately for land, sea, and all (land-sea) grid cells in the region. Regional averages are area-weighted by the cosine of latitude in all cases. This product extends the regional averages presented in Iturbide et al. 13  by including (1) data from CORDEX for all reference regions overlapping the area of the corresponding CORDEX domain by more than 80%, (2) an extended set of CMIP6 models, and (3) for reference, an observation-based product (W5E5  18  ) is also provided in the same format, enabling evaluation studies.
These datasets are provided in CSV format as plain data matrices (monthly time series in columns for each AR6 reference region). This format has been extended to include the corresponding metadata as a human-and machine-readable header. Moreover, a sample script (in the R language) used to compute the regional averages and produce these CSV files is also included, along with other functions facilitating their exploitation (e. g. computation of delta changes or depiction of boxplots and scatterplots). An example of use of the regionally-averaged datasets provided in the repository are the scripts to compute time periods corresponding to different GWLs in the warming-levels folder. GWLs are global surface temperature anomalies at a given level (e. g. + 1. 5 °C) with respect to the average values corresponding to the period 1850-1900 (a proxy for pre-industrial conditions)  19.
GWLs are widely used in policy-making as goals for greenhouse gas (GHG) concentration stabilization. In AR6, the use of GWLs has become common practice to complement scenario-based information on future climate, such as at the end of the century (2081-2100). GWLs can be an effective means of integrating available scenario-based information, independent of when the level of global warming is reached. The warming-level folder within the Atlas repository also provides tables as CSV files and plots with the central year of 20-year periods when different GWLs (+1. 5, +2, +3 and +4 °C) are reached for all CMIP5 and CMIP6 Global Climate Model (GCM) simulations considered in the Atlas. Moreover, the scripts and notebooks used to generate these tables and plots are also included. They also allow the creation of new tables and plots for other GWLs or moving-window lengths, using only the repository resources.
See also the new reusability example below, which computes non-overlapping decadal GWLs to produce GWL scaling plots. Finally, there are two key resources for reproducibility and reusability of the results. The first is the reproducibility folder, which contains scripts to reproduce the exact figures shown in the AR6 Atlas chapter. They provide end-to-end sample code to produce the final Atlas figures, providing all processing and plotting steps in all their complexity. The second resource is the notebooks folder which provides simpler analyses of the data, illustrating the basic processing workflow on datasets available in the Atlas repository (either the aggregated datasets which are part of the repository or samples of the Interactive Atlas dataset). They are implemented as Jupyter notebooks  20  , which are open-source and run on a web application which can be accessed either locally or from a remote client using any modern web browser.
Implementation of FaIR principles. In the case of the WGI Atlas, FAIR principles are implemented through the Atlas repository described above. In particular: Findability of the repository itself is promoted by its deployment on the widely-used GitHub platform. Additionally, Zenodo snapshots provide a unique, persistent identifier DOI. For all climate data inputs used in the Atlas, the repository lists the unique URLs (and DOIs, when available) for both data and metadata. This collection of unique identifiers is searchable, using either the automatic GitHub filter on comma-separated values (CSV) files or their version rendered as a searchable HTML table. Metadata are rich regarding both the data file content description, as well as the data provenance. The former is enforced by the CF conventions (https://cfconventions.
org), which is the climate science standard for metadata. The latter was achieved by means of DOIs pointing to World Data Center for Climate (WDCC) entries describing model configuration and references. Accessibility to the data and metadata in the Atlas repository is open. The full contents can be retrieved anonymously via HTTP, either from GitHub or Zenodo. All the content is licensed under a Creative Commons Attribution (CC-BY 4. 0) license, except for some CORDEX datasets with non-commercial restrictions (CC-BY-NC 4. 0 license). The original data and metadata sources listed are accessible through trusted repositories, in particular via authenticated HTTP connections to ESGF, the Copernicus Climate Change Service (C3S), and the Potsdam Institute for Climate Impact Research website. Interoperability is achieved through the use of human-and machine-readable file formats such as CSV, with additional header information (metadata) to ensure coherency and joint data/metadata transfer.
These files can be read by most programming languages and software utilities. A few data tables requiring text formatting are distributed in Microsoft's Office Open XML (OOXML) format, which provides open specifications currently implemented in a number of multi-platform office suites. Other data file formats are also open (netCDF, GeoJSON, etc. ) or de facto standards (e. g. shapefile) provided to facilitate user access. The code is also in the multi-platform, free software programming languages R and Python, widely adopted by the climate science community. Code usage examples are provided in Jupyter notebooks, which rely on an open source JSON plain text file format to combine both narrative explanations and code. They can be executed interactively by the open source Jupyter notebook server software in any modern web browser.
Their static content is also directly rendered by GitHub. The rest of the files provided in the Atlas repository are also stored in open formats, namely PNG (raster images), PDF or SVG (vectorial graphics), or Markdown (formatted plain text files). Reusability is at the core of the Atlas repository design and construct. For this reason, special care has been taken to describe the scope of the provided data, indicating the processing that has been applied to the original data source. This goes to the extent of providing the processing code along with all software versions used, as explained above. All data source versions are provided, and versioning has also been applied to the data and code produced within the repository. Most of the Atlas repository is licensed under CC-BY 4. 0, which promotes maximum reusability allowed by the underlying data sources.
CMIP6 participants licensing data under share-alike or non-commercial clauses granted a waiver to WGI and the IPCC-DDC to distribute CMIP6-derived report products, such as the Atlas regionally-aggregated datasets, under a CC-BY-4. 0 license. In addition to the scripts available for reproducibility, several notebooks provide reusable code snippets along with background information, explanations of the code and results, guidance to extend the analyses shown, and references to further information. A new sample notebook with additional reusable code in Python is provided as a companion to this article (see sections below on Reusability example and Code availability). Atlas FAIR review. As an integral part of the IPCC AR6, the WGI Atlas chapter was subject to the formal IPCC review process  21.
A total of 34 issues were opened as a result of this process. The issues are currently closed, but still accessible through GitHub under the "review" label. This exchange is public and a snapshot of the repository related to the review is accessible through the GitHub tag "v1. 6-review". The main weak points unveiled by this review, that were addressed in the final version of the Atlas, as described in the previous Sections, were: • Insufficient content description: README files were missing or needed to be extended to provide additional information, such as external references for the methodologies applied in the repository, and detailed description of the repository structure in the root README file, which is crucial to guide users. • Lacking details on the description of the interactive execution environment and execution problems: information on the software versions and the platform requirements were not explicitly included, and the Atlas Hub at some point was not accessible, which was finally deployed through MyBinder.
• Unnecessary content: obscure content of the repository that was not necessary was removed. • Code cleaning: repetition to be removed, additional explanation comments and headers to be included, and unnecessary and disrupting output elements, such as long warning messages, to be cut down in the notebooks. • File formats: proprietary formats (e. g. shapefiles) needed to be replaced with alternative open formats. • Lacking metadata and processing steps: sources of CORDEX and CMIP5 datasets were lacking unique version identifiers, and in the header of CSV files, information was missing to fully describe their contents. Some steps in the data processing, such as the interpolation software and version, were not included. • Lacking observational data: suggestion was to include also the observational data in the repository, which would be useful for many users to evaluate model results, especially if regridded to the common grid.
• Notebook location and description: it was suggested that notebooks previously scattered across the repository to be gathered in one common location which would be easily accessed by users, and to include sections with more guidance for users to be able to extend their work beyond the examples available in the repository. • Alternative programming language: As most of the code and notebooks are given in the R programming language, their adaptation to alternative programming languages such as Python would be an added value. • Licensing and authorship: unclear and mixed licenses needed to be better specified and organized. The suggestion was to release the scripts and notebooks under a Creative Commons license (as the data), instead of the specific General Public License (GPL) software license originally set.
External code review would be facilitated in future work by including and documenting an automated test suite (unit and integration tests). Errata and further development. When the WGI AR6 report was published on August 9 th 2021, a final version of the repository was published with the tag "v2. 0-final" and a Zenodo snapshot was produced for long-term archival  6. No further development will take place on this repository since the report content is now frozen with its publication. This means that modifications to the existing resources will take place only for maintenance and to correct for any errata, and all changes will be fully documented. Errata in IPCC reports, such as for chapter figures, need to be documented according to the IPCC Error Protocol. Following the experience of the review described above, error or problem reporting for the Atlas repository related to code and data underpinning figures in the Atlas has been implemented using the GitHub issue tracking system.
The Atlas GitHub repository will remain active with basic maintenance and improvement activities, fixing and/or documenting the problems reported (with full tracking on the actions taken) and enhancing reusability with expanded notebooks and examples. These will always be linked to an issue filed in GitHub, explaining the reason to modify the repository and keeping track of these minor changes. The idea is to provide basic maintenance, rather than further develop a repository which is considered final. Further development of the underlying open tools supporting the Atlas code, in particular the climate4R framework for climate data access and post-processing  16  , will take place in the original repositories. New data, metadata and sample scripts or notebooks exploiting them can be contributed to this alternative repository, which is also hosted by GitHub.
Reusability example: GWl scaling plots. In this section, we provide a working example on the use of the Atlas repository resources (code and data) to reproduce and extend some of the results provided by the Interactive Atlas. In particular, we focus on the Global Warming Level (GWL) scaling plots, a variant of the empirical scaling relationship, that can be used to quantify the sensitivity of regional changes in different impact-relevant indices as a function of global warming  23. The use of GWLs as an analysis dimension for climate change studies has become key in the last IPCC report as a means to overcome the uncertainties inherent to future socio-economic pathways. GWLs allow focusing on the regional impacts for a given level of global warming, regardless of when or how this Table  1.
Questions and key aspects to be addressed that were suggested in the revision process of the Atlas repository. warming level will be reached. Despite its potential limitations, this approach has been widely adopted by the climate change impacts and adaptation communities  24. The Interactive Atlas includes the possibility to analyze regional information using GWL plots (Fig. 2 ). However, these plots were not included in the Atlas chapter nor in the repository and constitute a good example of reusability, since they can be easily produced building on the Atlas repository resources. The analysis in this section has been coded as a new annotated notebook in Python, which can be executed using the software environment provided in the repository. To restrict our analyses to the data available in the Atlas repository, we will focus on regional mean temperature and precipitation.
Ensemble averages are also shown in this plot for each decade as overlaid dark red dots. These allow us to easily read the evolving mean ensemble global warming and regional response from the corresponding axes. For this exercise, we recreated the Interactive Atlas GWL plot using Python code (Fig. 3 ) to exploit the resources provided by the Atlas repository. Moreover, we extend the plot provided by the Interactive Atlas by adding the linear scaling factor (β) and the coefficient of determination (R 2 ), as a measure of the linearity of the relationship. These are derived from a standard least squares fit, also shown in the new figures as a solid line. Finally, the line representing a unit scaling factor is added for reference (dashed line). Figure  3  compares mean temperature GWL plots for CMIP6 and CMIP5 for 3 different regions.
FAIR principles should be considered as an integral part of future IPCC reports and the efforts required to achieve this should not be underestimated, with adequate human and technical resources allocated to support this task in all chapters, within both the TSU, the IPCC-DDC and the chapter author teams. Transparency and reproducibility could be favored by supporting and promoting common infrastructures and resources as part of the preparation of an IPCC report. Promising examples are the new workspace resources provided for the first time to IPCC AR6 authors by the IPCC-DDC to undertake server-side analyses of global and regional climate datasets, bringing analysis closer to data 2. In addition, leading scientific journals are increasingly requiring authors to make their data and code public, and the IPCC could channel these efforts to be at the forefront of scientific transparency.
Code review could be done in parallel with the formal review of the chapters, but using modern software review tools such as those used in AR6 WGI (GitHub/GitLab), and involving software development communities, such as Pangeo (https://pangeo. io), in the review process. Quality assurance, including technical validation of code, should be carefully planned early in the process, including automated testing, with both unitary and integration tests, implemented as a key component of a continuous integration workflow. The use of well-established data processing frameworks, e. g. climate4R  16  or ESMValTool  26  , could help in this process and help simplify the code in those tasks for which solutions already exist.
Methods The application of FAIR principles to the Atlas repository of the WGI Sixth Assessment Report (AR6) was conducted developing a GitHub repository including information about data provenance (with links/DOIs for findability of the complex datasets used), intermediate datasets (for reproducibility of key results) and code/ notebooks (for reusability). An informal review was conducted to gather expertise from IPCC WGI, WGII and WGIII authors, volunteers from the IPCC TG-Data, and experts from the open data community. They used Table  1  as guidance, but were otherwise free to openly comment on the GitHub repository issue tracking system. The full discussion, leading to the current form of the repository, is available on GitHub. Custom code to re-use the Atlas repository resources as shown in the reusability example above is available on Zenodo 27 (see also the Code Availability section).
acknowledgements We acknowledge partial funding from projects  ATLAS  ( PID2019-111481RB-I00 ) funded by  MCIN/ AEI/10. 13039/501100011033  and IS-ENES3 which is funded by the  European Union 's  H2020 programme  under grant agreement No  824084. We also acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling and Working Group on Regional Climate, responsible for CMIP and CORDEX, respectively. We also thank the climate modeling groups for producing and making available their model output, as described in the data-source folder of the repository. We also acknowledge the  Earth System Grid Federation infrastructure , an international effort led by the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison , the  European Network  for Earth System Modelling and other partners in the  Global Organisation for Earth System Science Portals (GO-ESSP ).
Code availability All code and notebooks in the Atlas repository are available at https://github. com/IPCC-WG1/Atlas and on Zenodo 6 under the Creative Commons Attribution license, CC-BY 4. 0. The new notebook reusing the Atlas repository dataset to produce GWL plots (Fig. 3 ) is available at https://github. com/SantanderMetGroup/2022_ Iturbide_FAIRprinciplesIPCC and also on Zenodo  27.
Data sources and sample The sampling frame of this study is the annual 'Selling Power 500: The Largest Sales Forces in America' list (www. sellingpower. com). Selling Power ranks the top 500 US-based firms in terms of the size of their sales force. Selling Power lists have been used in previous studies (e. g. Panagopoulos et al. , 2018). This study worked with the most recent data. Specifically, the sample period of this study was from 2014 to 2020 (7 years). Our initial sample included all manufacturing and service firms (a total of 577 firms). Private firms were discarded owing to the unavailability of data; firms for which data were not available in Compustat were also discarded, leaving a final study sample of 341 firms (341 firms × 7 years). However, data for some firms for some years were still missing, so the final dataset used in this study was unbalanced.
The total number of observations was a maximum of 2207 (for details, see Table  4 ). The sample consisted of firms belonging to 48 industries (according to two-digit SIC codes). The mean and the standard deviation of the size of the sales force of the sample firms were 3062 and 5030, respectively. Data relating to the measurement of the outcome variables, explanatory variable, moderating variables, and control variables were collected from Wharton Research Data Services' Financial Ratios Suite and Compustat.
Independent and moderating variables Selling capability. Selling capability was measured using data envelopment analysis (DEA; see online Appendix A for details). We used DEA window analysis to measure the selling capability of each firm for each year. We used an inputoriented, variable return-to-sale DEA model to measure selling capability, because firms have more control over sales inputs than over sales outputs. Furthermore, sales outputs do not always increase proportionally when sales inputs are ramped up. Consequently, a variable return-to-scale model is appropriate. This study used three selling inputs: tot it = V it K phy it +K int it , Compustat where V it is the firm's market value measured as the sum of outstanding equity and book value of debt, minus the current asset; K phy it is the book value of property, plant and equipment; and K int it is the aggregate of externally purchased and internally created intangible capital.
Serial correlation One widespread challenge in the panel data structure is the autocorrelation in the idiosyncratic error term, which biases the standard errors. The results are less efficient in the presence of autocorrelation  (Drukker, 2003). We conducted a Woolridge test using Stata's xtserial module, the results of which (F = 33. 073, p = 0. 0000) confirmed the presence of serial correlation in the idiosyncratic error term of the model. As autocorrelation was detected in the model, an appropriate model estimation method that can produce robust results must be used, which will be explained below Endogeneity test We conducted a Durbin-Wu-Hausman test using the lag values as instrumental variables (internal instruments) to check for endogeneity in the key explanatory variable of theoretical interest (selling capability).
Diagnostic tests and model estimation method We conducted a number of tests (Table  3 ) on our panel data because they pose an array of econometric challenges, such as serial correlation, unobserved heterogeneity, and endogeneity. Model estimation method. This study used a twostep system-GMM estimation technique incorporating Stata's xtabond2 module  (Roodman, 2009)  for several reasons. Firstly, as indicated above, this study used two-period lagged values of the dependent variable on the right-hand side of the equation (autoregressive model). The system-GMM is particularly designed to estimate such autoregressive models because it can generate reliable coefficient estimates by accounting for the dynamic panel bias stemming from the inclusion of the lagged values of the dependent variable  (Marino et al.
, 2015; Roodman, 2009). Secondly, as shown above, the Hausman test confirmed the use of the fixed-effect model, and the GMM incorporated firm fixed effects and accounted for unobserved heterogeneities across sample firms. Thirdly, the Woolridge test confirmed the presence of serial correlation in our dataset, for which GMM is an appropriate estimation method  (Steigenberger and Wilhelm, 2018). Fourthly, our Durbin-Wu-Hausman tests confirmed that some of the key explanatory variable were endogenous, and the GMM produced endogeneity-robust results in the presence of endogenous regressors  (Roodman, 2009). Finally, GMM allows for the use of lagged values of endogenous variables as internal instruments to generate endogeneity-robust results  (Marino et al. , 2015; Steigenberger and Wilhelm, 2018).