dataesr/scientific-paragraphs-categorization huggingface dataset:
g. tables, figures) appropriate? -Have abbreviations and symbols been properly defined? -Is the context of prior research properly described, citing relevant articles and datasets? Quality of the dataset Although publication of a data paper does not guarantee the overall quality of the dataset, there is a need to check whether suitable and reproducible methods have been used to obtain the data, and whether the data are displayed in a sensible way. The dataset must be a longterm resource, stable, complete, permanent and of good quality. The review will address following questions: -Are the data logically and consistently organised? Are they easily readable and usable? -Is anything missing in the manuscript or the data resource itself that would prevent replication of the measurements, reproduction of the figures or other representations? -Are the methods used to generate the data (including calibration, code and suitable controls) described in sufficient detail and suitable to maintain of integrity of the dataset? -Have possible sources of error (including methods, calculation and interpretation) been appropriately addressed in the protocols and/ or the paper? https://doi.
These two examples of a new kind of papers are quite different, yet they have in common that they are both linked to research datasets and above all, to the dissemination and reuse of research data which is their main purpose. The boundaries between data papers and data journals and other categories of scientific communication are partly blurred, not only due to a lack of reference definitions but https://doi. org/10. 5771/0943-7444-2021-4-307 Generiert durch Birger Hjorland, am 18. 12. 2021, 14:32:41. Das Erstellen und Weitergeben von Kopien dieses PDFs ist nicht zulässig.
The objective of The International Journal of Robotics Research is "to facilitate and encourage the release of high-quality, peer-reviewed datasets to the [. ] community"(Newman and Corke 2009, 587). -Studies in Family Planning tries to promote "interdisci-plinary research and integrative analyses by making acces-sible to researchers, policymakers, students, and donors' data that may be useful in answering critical questions of interest to [. ] readers" (Friedmann, Psaki and Bingen-heimer 2017, 291). -The French journal Review of Information and Commu-nication Sciences (RFSIC) invites data papers to describe the scientific process, methods and tools that result in re-search data in a Bruno Latour perspective, "since they never just magically appear" (Le Deuff 2018,  §2).
These two examples of a new kind of papers are quite different, yet they have in common that they are both linked to research datasets and above all, to the dissemination and reuse of research data which is their main purpose. The boundaries between data papers and data journals and other categories of scientific communication are partly blurred, not only due to a lack of reference definitions but https://doi. org/10. 5771/0943-7444-2021-4-307 Generiert durch Birger Hjorland, am 18. 12. 2021, 14:32:41. Das Erstellen und Weitergeben von Kopien dieses PDFs ist nicht zulässig. The French Agri-cultural Research Centre for International Develop-ment (CIRAD) produced a list with fifty-four academic journals accepting data papers relevant for agricultural science, including the mega-journal PLoS One.
It is quite impossible to make an estimation of the real number of such mixed data journals and their data papers. 3. Data papers are more than simple descriptions of data sets. Even a superficial analysis of data papers reveals that one part of articles labelled as data papers do not only de-scribe datasets but add data analysis and discussion of re-sults. Atomic Data and Nuclear Data Tables, Dataset Pa-pers in Science and Open Archaeology are three "pure" data journals which explicitly accept data papers with re-sults and discussion of results. This means that a (un-known) part of data papers in fact are more than simple data papers stricto sensu because they communicate re-sults of data analysis. 4. There are other emerging types of articles, similar to but not identical with data papers.
Pure and mixed data jour-nals are open for other categories of articles which are neither traditional journal items (research articles, re-views, comments etc. ) nor data papers. Sometimes the difference may be a question of terminology. For in-stance, F1000Research accepts "brief descriptions of sci-entific datasets that promote the potential reuse of re-search data and include details of why and how the data were created" called "data notes" 32 , in other words, data papers. But there are other examples: a. Data services paper: "papers on data services, and pa-pers which support and inform data publishing best practices (including) the development of systems, techniques or tools that enable data analysis, data vis-ualisation, data collection and data sharing (and) pro-cesses and procedures used in the development of da-tasets" (Geoscience Data Journal).
com/webofscience platform/dci. 3. Software (or "data as software") is also briefly discussed in Kratz and Strasser (2014). 4. WoS is both the name of a platform (which was origi-nally called Web of Knowledge), which contains many databases, as well as on a single database, called WoS Core Collection, which basically is an integration of for-mer databases such as Science Citation Index, Social Sci-ences Citation Index and Arts & Humanities Citation Index. DCI is part of the platform, but not of the "core collection". 5. ISO 5127: 2017 defined paper as synonym for article: "3. 4. 1. 27. 09 paper (2): <document> text (3. 2. 1. 05) doc-ument (3. 1. 1. 38) delivered before an audience or con-tributed to an edited volume (3. 4. 1. 27. 12) or a scientific journal (3. 4. 1. 28. 19)". or family documents (3.
Also, journal articles often include a brief abstract of the con-tent. Periodical articles are indexed, usually by author and subject, in periodical indexes and abstracting ser-vices, known as bibliographic databases, when available electronically. Compare with column, editorial, and es-say. See also: cover story and feature. " 8. WoS writes: "An Article is generally published in a jour-nal. A Proceedings Paper is generally published in a book of conference proceedings. Records covered in the two Conference Proceedings indexes (CPCI-S and CPCI-SSH) are identified as Proceedings Paper. The same records covered in the three indexes (SCI-E, SSCI, and A&HCI) are identified as Article when published in a journal. " Book Chapter is a separate category in WoS. 9. MEDLINE describes two document types related to data, one of which (data set) is already described from our description of DCI (https://www.
nlm. nih. gov/ mesh/pubtypes. html): "Database: Work consisting of a structured file of infor-mation or a set of logically related data stored and re-trieved using computer-based means. " "Dataset: Works consisting of organized collections of data, which have been stored permanently in a formal-ized manner suitable for communication, interpreta-tion, or processing. " 10. An example of a data handbook is, for example, United Nations' Statistical Yearbook: https://unstats. un. org/ unsd/publications/statistical-yearbook/. 11. Reitz (2004), on the other hand, defined paper in a narrow sense: "Paper. […] Also refers to a brief composition, especially one prepared for presentation by the author at a conference or other professional meeting. Conference papers may be published in proceedings or trans-.
pdf, last access: 18 March 2022), which is supplemented with a coupling interface to allow boundary data exchange with other components (ocean, dynamic vegetation, aerosols, and atmospheric chemistry). The NEMO3. 6  (Madec and the NEMO team, 2008; Madec et al. , 2015)  and LIM3 (  Vancoppenolle et al. , 2009; Rousset et al. , 2015)  models are the ocean and sea ice components, respectively. Biogeochemical processes in the ocean are simulated by the PISCES model  (Aumont et al. , 2015). Both LIM3 and PISCES are codewise integrated in NEMO. Dynamical vegetation, land use, and terrestrial biogeochemistry are provided by LPJ-GUESS  (Smith et al. , 2014; Lindeskog et al. , 2013). Aerosols and chemical processes in the atmosphere are described by  TM5 (van Noije et al. , 2014). The ice sheet model PISM  (Bueler and Brown, 2009; Winkelmann et al.
Of the above-named model components, NEMO, LIM3, and PISCES exchange data directly via shared data structures. Thus, EC-Earth3 is implemented following a multiexecutable MPMD (multiple programs, multiple data) approach. The model components run concurrently, and a message-passing interface (MPI) is used for parallelization within the components. A potential configuration of all components is illustrated in Fig. 1 , which also shows coupling links and frequencies. Note that a configuration including all possible components is not implemented in practice. In order to manage different configurations at both build time and runtime, EC-Earth3 includes tools to store and retrieve configuration parameters for different model configurations, computational platforms, and experiment types. This allows consistent control of the build and run environments and improves reproducibility across platforms and use cases.
Initial and forcing data (see Table  13 ), in the form of data files, are provided centrally for the EC-Earth community, and the data are versioned and check-summed for reproducibility. For EC-Earth3 a tool was developed to convert the native model output to CF-compliant ("Climate and Forecast" standard) NetCDF format (i. e. , Climate Model Output Rewriter, CMOR), thus fulfilling the CMIP6 Data Requests for the model intercomparison projects (MIPs) that the community is contributing to  (van den Oord et al. , 2022 (van den Oord et al. , , 2017 , https://github. com/EC-Earth/ece2cmor3/ (last access: 18 March 2022), https://doi. org/10. 5281/zenodo. 1051094, van den Oord, 2017).
Atmospheric tuning of EC-Earth3 and EC-Earth3-Veg The atmospheric component of EC-Earth has been tuned with the goal of achieving a reasonably small radiative imbalance at the top of the atmosphere (TOA) at standard resolution (T255L91 -to which we refer in the following) in present-day atmosphere stand-alone (AMIP) runs using the CERES_EBAF_Ed4. 0 dataset as a reference  (Loeb et al. , 2018). In particular, the goal was to minimize the mean weighted absolute error in the global means of the net radiative flux at the surface, the TOA longwave flux, longwave cloud forcing, and shortwave cloud forcing, with the first two fluxes considered the most important. The net radiative flux at the surface included the latent heat contribution associated with snowfall, which is not included in the latent heat flux stored by IFS.
No indirect aerosol effect has been implemented. -A SST and sea ice forcing dataset specially developed for HighResMIP is used for AMIP experiments  (Kennedy et al. , 2017). The major differences compared to the standard SST forcing datasets for CMIP6 are the higher spatial (0. 25 • vs. 1 • ) and temporal (daily vs. monthly) resolution. -The vegetation and its albedo are prescribed as presentday climatologies that are constant in time. Under HighResMIP, simulations are performed with EC-Earth3P-HR in high resolution and in the standard-resolution EC-Earth3P (T255 for IFS and 1. 0 • for ORCA/NEMO). A full description of EC-Earth3P-HR including technical implementation and post-processing can be found in  Haarsma et al. (2020). EC-Earth3P-HR was not tuned differently compared to the standard resolution at the time due to very high computational demands.
Changes in the spectral distribution of the optical properties, the single-scattering albedo, and asymmetry factor of anthropogenic aerosols due to long-term changes in their size distribution and composition are ignored by MACv2-SP. Stratospheric aerosols are prescribed using the CMIP6 dataset of aerosol radiative properties, which covers the period 1850 to 2014 and for the more recent period is based on satellite data assembled by  Thomason et al. (2018). The dataset consists of monthly resolved zonal mean fields, which are provided at the 14 shortwave (SW) and 16 longwave (LW) bands of the IFS's radiation schemes. For the SW scheme, the extinction, single-scattering albedo, and asymmetry factor are specified, whereas only the absorption is taken into account for the LW scheme, since aerosol scattering in the LW is neglected in the atmospheric component of EC-Earth.
In both implementations, it is possible to set the forcing fields to a constant background distribution computed as the time average over 1850 to 2014. This background forcing is applied in preindustrial control and future simulations, as recommended in the CMIP6 protocol. The land use forcing dataset (LUH2) from CMIP6  (Hurtt et al. , 2020 ) cannot be used directly as input to IFS because it does not provide the same vegetation cover or type categories as those used by the land surface scheme in IFS (HTESSEL; van den  Hurk et al. , 2000; Balsamo et al. , 2009; Dutra et al. , 2010; Boussetta et al. , 2013)  but instead provides agricultural management information and land use transitions that are annually updated. The vegetation cover, leaf area index (LAI), and vegetation type that are needed for the land surface scheme and albedo parameterization in IFS can be simulated by the dynamic vegetation model LPJ-GUESS  (Smith et al.
, 2014). This happens automatically in the EC-Earth3-Veg configuration wherein the dynamic vegetation model, which uses the LUH2 dataset as an input, is active, but for all other configurations the required vegetation cover and type need to be precomputed. This is done by first making all CMIP6 experiments with the EC-Earth3-Veg configuration and saving the vegetation variables that can then be reused when making the same experiment with other model configurations. The orbital parameters of the original IFS CY36R4 are fixed for present-day conditions following the recommendations of the International Astronomical Union (ARPEGE-Climate  Version 5. 1, 2008) , which is sufficient for simulations of the recent past or near future. However, for paleosimulations in PMIP the orbital parameters need to be variable or set fixed for a different time period.
Dynamic vegetation and terrestrial biogeochemistry LPJ-GUESS  (Smith et al. , 2001 (Smith et al. , , 2014;; Lindeskog et al. , 2013; Olin et al. , 2015a, b) , a process-based secondgeneration dynamic vegetation and biogeochemistry model, is the terrestrial biosphere component of EC-Earth globally simulating vegetation dynamics, land use and land management following the LUH2 dataset  (Hurtt et al. , 2020) , and both carbon (C) and nitrogen (N) cycling in terrestrial ecosystems. LPJ-GUESS has been evaluated in numerous studies  (Smith et al. , 2014; Wårlind et al. , 2014)  and reproduces vegetation patterns, dynamics, and productivity, C and N fluxes and pools, and hydrological cycling from global to regional scales, in line with independent datasets and comparable models (e. g. ,  Piao et al.
g. ,  Smith et al. , 2014; Olin et al. , 2015a, b)  as are biogenic volatile organic compound (VOC) emissions  (Hantson et al. , 2017). Meteorological inputs imposed on LPJ-GUESS are daily fields of surface air temperature and 25 cm soil temperatures, precipitation, and net shortwave and net longwave radiation from IFS/HTESSEL (Table  5 ). LPJ-GUESS calculates its own soil moisture for potential plant uptake in all patches in each of the six simulated stands independently of the single grid-cell-averaged hydrology scheme used in HTESSEL. Vegetation dynamics are simulated on six stand types in the land portion of the grid cell (excluding large water bodies based on the static LUH2 ice and water fraction information), five stands having dynamic grid cell fractions consistent with the LUH2 dataset, namely natural, pasture, urban, crop, and irrigated crop, and one, peatland, having a fixed grid cell fraction derived from the GLCC global map used in the standard HTESSEL configuration -see Sect.
3. 2. The LUH2 dataset, including land cover fractions, management options (N fertilization in this case), and land cover transitions, are read in yearly after aggregation to the atmospheric and land surface model resolution in a preprocessing step. A total of 10 woody and two herbaceous PFTs compete in the natural stand  (Smith et al. , 2014) , whereas two herbaceous species, one each conforming to the C 3 and C 4 photosynthetic pathways, are simulated on pasture, urban, and peatland fractions. The crop stands each have five crop functional types (CFTs) representing the properties of global crop types and encompassing the classes found in the LUH2 database, namely both annual and perennial C 3 and C 4 crops, as well as C 3 N fixers  (Lindeskog et al. , 2013). At the end of each day, LPJ-GUESS calculates the effective cover for low (high) vegetation, C l (C h ), and LAI for low (high) vegetation, LAI low (LAI high ), taking into account phenology and stand fractions in the grid cell.
, 2021). A recent description of the chemistry scheme applied in EC-Earth has been presented by  Williams et al. (2017). Emissions of aerosols and chemically reactive gases are taken from the CMIP6 historical datasets for anthropogenic sources  (Hoesly et al. , 2018)  and biomass burning  (van Marle et al. , 2017)  or the corresponding CMIP6 scenario datasets  (Gidden et al. , 2019). To force the CH 4 simulation to follow the pathway provided by CMIP6, its surface mixing ratios are nudged towards the monthly zonal means from CMIP6 interpolated to daily values. Moreover, because TM5 lacks a comprehensive stratospheric chemistry scheme, the CH 4 mixing ratios in the stratosphere are nudged towards a monthly zonal mean observational climatology representative for the 1990s (interpolated to daily values), scaled by a global factor based on the CMIP6 time series of global annual mean surface values.
78 ppmv. Ozone is simulated by TM5. As for CH 4 , TM5 applies a nudging scheme for O 3 in the stratosphere. In EC-Earth3, the mixing ratios are nudged towards daily zonal means obtained from the CMIP6 dataset. For aqueous-phase chemistry in the troposphere, the acidity of cloud droplets is calculated assuming a uniform CO 2 mixing ratio following the CMIP6 time series of annual global mean surface values. TM5 simulates tropospheric aerosols, namely sulfate, black carbon, primary and secondary organic aerosol, sea salt, and mineral dust, in four size ranges describing nucleation, Aitken, accumulation, and coarse modes using the M7 aerosol microphysical model  (Vignati et al. , 2004). In addition, it simulates the total mass of ammonium, nitrate, and methane sulfonic acid (MSA). Optical properties of the aerosol mixture are calculated based on Mie theory in combination with the mixing assumptions described by  van Noije et al.
(2014). For calculation of the SW radiative effects of the aerosol mixture, TM5 provides the extinction, single-scattering albedo, and asymmetry factor at the 14 wavelength bands of the SW radiation scheme (using the same wavelength values as in MACv2-SP). In addition, TM5 provides the particle number and component mass mixing ratios for each of the M7 modes, plus the total mass mixing ratios of nitrate and MSA. LW absorption is calculated based on the mass mixing ratios of the M7 components using absorption efficiencies from IFS. The contribution of the aerosol mixture described by MACv2-SP and/or TM5 to SW extinction and LW absorption is removed above the tropopause, where the stratospheric aerosol dataset from CMIP6 is applied. The tropopause level is diagnosed online following the thermal tropopause definition of the World Meteorological Organization (WMO, 1957), as detailed by  Reichler et al.
Precipitation Global mean precipitation (pr) patterns are well represented in EC-Earth3 in comparison with ERA5 for present-day conditions (Fig. 6 ), except for the double Intertropical Convergence Zone (ITCZ) pattern noticeable as a distinct peak in the zonal mean pr at 9 • S. This is a persistent model bias common to most global climate models. The largest pr biases relative to the observational dataset Global Precipitation Climatology Project (GPCP;  Gehne et al. , 2016)  are found in the tropics, in particular in the Southern Hemisphere (SH) and to a lesser extent in the Northern Hemisphere (NH). EC-Earth3 precipitation is closer to ERA5 but has less precipitation at the Equator. The overestimation of pr by the model in the SH and the double ITCZ are likely consequences of the strong warm temperature bias over the Southern Ocean that leads to a more southward-displaced tropical rainfall belt and more vigorous hydrological cycle  (Hwang and Frierson, 2013).
Sea ice We compare EC-Earth3 historical ensemble mean sea ice variables and spread to observations and reanalysis datasets. The ensemble mean of EC-Earth3 slightly overestimates the Arctic sea ice area (Fig. 10 ), whereby the ensemble spread encases the OSI SAF satellite observations  (Lavergne et al. , 2019). The trend in the Arctic sea ice area over 1980-2014 is captured well by the model during March and September (Fig. 10 ). Interestingly, the modeled summer Arctic sea ice area minimum occurs in August instead of September as in observations. This result is in agreement with previous studies using NEMO-LIM forced by atmospheric forcing  (Rousset et al. , 2015; Docquier et al. , 2017)  and several coupled CMIP6 models including NEMO as an ocean component  (Keen et al. , 2021). The exact reason for the minimum sea ice area occurring in August is not clear  (Keen et al.
org/10. 5194/gmd-15-2973-2022 Geosci. Model Dev. , 15, 2973-3020, 2022 Overall the representation of the NAO in EC-Earth3 shows minor improvements with respect to EC-Earth2. 3: the root mean square error (RMSE) of EC-Earth3 is 0. 64 compared with 0. 52 for EC-Earth2. 3. Previous assessments of coupled models have found that the CMIP5 and CMIP3 models represent NAO very similarly as in reanalyses, with no general improvements of CMIP5 compared to CMIP3 models  (Davini and Cagnazzo, 2014). In EC-Earth3, the ENSO spatial patterns and amplitude are well represented (not shown). In order to assess the variability of ENSO in the EC-Earth3 ensemble we calculated the Niño3. 4 SST power spectra (Fig. 18 ) for the model and for the HadISST observational dataset  (Rayner et al. , 2003). HadISST shows the highest peak at 5.
5 years and a range of periods between 3. 3 and 3. 7 forming the second highest peak. The mean of the EC-Earth3 ensemble spectra shows the highest peak at around 3. 7 years. Some individual members show their main peak at about 5. 5 years, similar to the HadISST dataset. Most of the ensemble members (two with the highest peaks) show their maximum in the 3-4-year period. EC-Earth3 shows larger ENSO spectral power than EC-Earth2. 3, with an ensemble mean closer to the HadISST observations, especially in frequencies with high energy. Thus, we see a clear improvement in the representation of ENSO in EC-Earth3. Similar improvements were seen earlier for CMIP5 models  (Jha et al. , 2014). While the frequency distribution in EC-Earth3 is distinctly improved compared to EC-Earth2. 3, a challenge is still seen in representing distinct peaks in the power spectrum.
Land fluxes (computed from two historical EC-Earth3-Veg runs, one with dynamic land use change and management and one with land use fixed at 1850 levels) have increased following increased atmospheric CO 2 and temperature, but these show a stronger interannual variability in both emissions from land use change (ELUC; Fig. 23a ) and terrestrial CO 2 sink (SLAND; Fig. 23b ) compared to the estimates from the DGVMs in GCP 2019. This interannual variability originates from both climate differences between EC-Earth3-Veg and the observation-based forcing, which includes the right timing of weather events like El Niño in the offline simulations used for the GCP  (Harris et al. , 2014)  and the mismatch between the dynamic vegetation in LPJ-GUESS compared to the land use dataset LUH2  (Hurtt et al.