dataesr/scientific-paragraphs-categorization huggingface dataset:
As the survey results have shown, for some themes (e. g. , habitat/vegetation/crop) potential data from on-site field measurements are often replaced by remotely sensed or existing database information (Appendix Table  1 ). This suggests a strong need of on-site measurements for these data for modelling site-specific processes because on-site measurements generally are more accurate than remotely sensed or modelled data. Plausible causes for the lack of on-site measurements are the absence of harmonized measurements, and the time and personnel-consuming requirements for data gathering. In line with the observatory missions, models using LTER data more strongly embrace management and predictive applications  (Stoll et al. , 2015)  ranging predominantly from site and sub-catchment to region, from days to decades, compared to CZO models that focus more on process/system understanding and are more geophysical and subsurface oriented with a wider range of scales from lab to global, from seconds to millennia (Appendix Figure  6 ).
Current status To answer the question how data-rich LTER and CZO sites are used in holistic, integrated models, a 'level of integration' was calculated for each model by normalizing to the scale 0 (low) to 1 (high) the number of disciplines (e. g. , atmospheric circulation, biodiversity, biogeochemistry, and others, see Appendix Figure  6 ) and compartments (e. g. , Atmosphere, Hydrosphere Lithosphere, and others) indicated by the respondents, and equally weighing them. The models using data from the LTER and CZO networks cover a wide range of integration, from very specific singular process models (little integration and variables), to highly integrated models that cover many disciplines and compartments, and have large numbers of variables. On average, a rather high level of integration is present for the models, meaning that the LTER and CZO data model applications cover on average multiple disciplines (mean=3.
These factors will impact infiltration of meteoric water charged with carbonic acid (H 2 CO 3 ), influencing the breakdown of minerals and the redox conditions under which metals can be mobilized. To explore the larger scale consequences of changes in root system architecture on soil water and riverine chemistry requires coupling of models from two scientific fields, a reactive transport model with a land-surface and hydrologic model to the RT-FluxPIHM Model (Figure  4 )  (Bao et al. , 2017; Li et al. , 2017a). To further understand how these systems evolve in response to land cover change, we can couple these models with ecosystem models, such as Biome-BGC  (Thornton et al, 2005). One limitation to such complex numerical models is the numerous datasets needed for parameterization. However, working with datasets derived from LTER, CZOs and NEON allows evaluation of model performance against data that characterizes key processes embedded within integrated models.
However, the development and use of parsimonious models is advantageous for targeted processes within disciplinary boundaries, yet, the applicability of conclusions drawn is highly limited  (Basu et al. , 2010; Li et al. , 2017b). It is an interesting question whether data access limits model integration, or whether a lack of integration is mainly caused by the model development side. Model integration is data-demanding. with a particular focus on ecological driven processes. Whereas the conceptual models for LTER and CZO sites orient to a minimal level of integration, fundamental process understanding, with specific parsimonious models, nevertheless remains an essential part of the mission.
Current status In an ordination of the model variables (see Appendix), models that used data of both LTER and CZO sites cluster in the center of the ordination of the surveyed models (Figure  5 ), which indicates that those models use a fairly similar and large group of variables. A significant number of models are co-located in the center, focusing mostly on hydrology and geophysical processes. The Group-A models associated with CZOs concentrate in the second quadrant, distinguished by the focus on modelling process in the petrology, regolith and bedrock. The Group-B models associated with LTER data are mostly located in the first and fourth quadrants (Figure  5 ) resorting under the ecology discipline related to habitat and biota. Disciplines separate from physics-oriented to biotic-oriented models, along with compartments from below-ground (negative) to above-ground (positive).
Open issues and implications to join the networks and communities' expertise The survey revealed that models using data from both CZO and LTER networks generally cover a larger range of variables compared to models applied to only one network. Although few up to now, these examples show the opportunity these observation network data offer to integrate a larger number of compartments in models. Biotic data are applied in models of the CZO community (for instance, see Data Integration example), but they are few in number. Nevertheless, there is strong evidence for the important role of biota in coupled processes such as energy, carbon and nutrient cycling, and weathering  (Filser et al. , 2016; Richter and Billings, 2015; Wall et al. , 2015). The outlier position of models focussing on biota and habitat variables in the ordination (Figure  5 ) indicates the need for integration of the biotic compartment in model development  (Vereecken et al.
, 2016b). Therefore, harmonization and standardization of biotic observations is particularly important for those biotic variables related to processes and feedbacks with the hydrologic and biogeochemical cycles. In this survey, many biotic models were opposed to below-ground compartments in the ordination (Figure  5 ). This demonstrates the lack of data application for biotic processes in the subsurface, e. g. , the representation of the weathering microbiome or root system architecture and dynamics  (Smithwick et al. , 2014). Recent initiatives address the missing integration of below-ground biota in the terrestrial Earth system science and models (Key to Soil Organic Matter Dynamics and Modelling -Keysom-Biolink project,  Filser et al. , 2016) , along with the provision of substantial datasets on soil biota and biodiversity (global soil biodiversity database,  Ramirez et al.
Strategies for network integration With respect to investigating specific aspects of ESD, the interactions of biotic and abiotic processes as well as belowground and above-ground processes are key links, where geosphere focussed research by CZO and ecology focussed research by LTER could benefit observation and model integration wise. This also counts for the highlighted lack of integration of underground biotic elements, although the underground biota performs a crucial ecosystem functioning role  (Deruiter et al. , 1994; Wall et al. , 2015). Data harmonization could be achieved based upon the concept of blending the conceptual frameworks of Ecosystem Integrity  (Müller et al. , 2000)  and the Essential Biodiversity Variables  (Pereira et al. , 2013)  as suggested  Haase et al. (2018)  and the Critical Zone approach  (Chorover et al.
In contrast to these, individual variables associated with biodiversity (60 times in total), and saprolite and bedrock properties (50 times in total) were much less used in the models. Most likely, this is because they are much more sampling intensive in terms of time and resources compared to atmospheric variables (e. g. a weather station). The variables used reflect also the current most frequent model applications in terrestrial Earth system science. CZO and LTER data are mostly applied for coupled hydrological-geophysical models being the most data intensive types of models, which may as well be reflected in these statistics. Biogeochemistry, petrology and biology communities appear as less data intensive. As could be expected, the CZO models most frequently apply variables of saprolite and bedrock (54%, plus 26% joint CZO soil texture and physics'.
Models using data of 'habitat mapping', and 'vascular plant diversity' were associated with the LTER community. The majority of variables used in the models are sourced from on-site measurements (55% on average). 45% of the data is sourced from other sources, including remotely sensed, modelled, external database, or literature. The fraction of on-site measured data use is slightly higher for the variables in the atmospheric compartment (58%) compared to, for example, the fraction of variables represented by habitat/vegetation/crop. Compartment wise, the fractional use of empirical, site-based measurements is highest for the variables belonging to the surface waters (68 %). The most commonly remotely sensed data in the survey were variables of the biosphere, especially 'habitat mapping', 'leaf area index', 'vegetation structure (height) and dynamics', 'above-groundbiomass', 'snowpack distribution and duration', 'eddy flux of evapotranspiration', and 'CO2'.
Model wise, the average model used 14 variables, 9 of those as model input and 5 of those for calibration/validation (Appendix Table  1 ). The model 'level of integration' was calculated by normalizing the model-wise number of disciplines and compartments indicated by the responses to a scale of 0 to 1 (high) and equally weighing both to obtain the 'level of integration' between 0 and 1. On average, a rather high level of integration is present for the models; meaning that the CZO and LTER data model applications cover on average multiple disciplines (mean=3. 6±2. 1 standard deviation) and ecosystem compartments (mean=2. 7±1. 6). Model 'level of integration' is strikingly similar in CZO and LTER data applications. The richness in variables was positively correlated to the number of disciplines (R 2 = 0.
Earth Syst. Dynam. Discuss. , https://doi. org/10. 5194/esd-2017-94 Manuscript under review for journal Earth Syst. Dynam. Discussion started: 6 November 2017 c Author(s) 2017. CC BY 4. 0 License. 2. 2 Example of data-model linkage from an LTER site Next to the common, cross-site measurements, observation network datasets often also include some specific elements for individual sites, gathered from the perspective of prediction and model development, to deal with specific questions of ecosystem responses to global change. An example is the vegetation dynamics modelling in the French LTER Zone Atelier Loire (Van Looy and Piffady, 2017), which uses predicted hydrological changes in river flow regimes and droughts to predict changes in land use and vegetation in the floodplain. It enables the construction of population dynamics models for characteristic tree species Black poplar and White elm for the LTER site where count data of the species populations is present.
Acknowledgements Financial support from  NSF ,  NEON ,  LTER  and  CZO  made this collaboration possible. HWL and SW acknowledge the  National Science Foundation  ( NSF ) for ongoing support. NEON is a project sponsored by the  NSF  and managed under cooperative support agreement ( EF-1029808 ) to Battelle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of our sponsoring agencies. We dedicate this work to the memory of Henry L. Gholz who was strongly involved in developing these strategies when the Rocky Mountains took him from us (RIP).
The numerical model applications necessary to test LTER and CZO conceptual model assumptions are integrated, process-based, spatially explicit models at the watershed scale that predict emergent behavior. The high level of multidisciplinary model inputs requires numerically expensive models and more importantly a sharp learning curve of the users. In our survey 10% of the models already used data from both LTER and CZO networks in model applications. This implies that these models already integrated processes of interest to the two communities. It does not necessarily mean that the data are co-measured. Where CZOs mainly focus on understanding near surface structure and dynamics, integration with LTER might fill many of the ecological gaps in CZO studies by providing the scientific expertise, research platforms, and datasets necessary to analyze environmental change Earth Syst.
PRELIMINARY NUMERICAL RESULTS In this section, we present preliminary numerical results. A Matlab (R2012a) program is developed using CPLEX 12. 6 to solve MIP 1 , MIP 2 , MIP 3 and their corresponding LP relaxations. The numerical experiments have been carried out on an Intel(R) 64 bits core (TM) with 2. 6 GHz and 8 Gigabytes of RAM. CPLEX solver is used with default options. Each entry in matrices {C, D, H} and in matrices {A, B, R, Q} is randomly and uniformly distributed in the intervals [0; 10] and [0; 5], respectively. The scalar values c = r = 100. The BigM values M and L are set to M = 100 and L = 10 10 , respectively. We set the parameter ε = 10 -8 in Algorithm 3. 1. In Table  1 , first we solve MIP 1 with up to 15 nodes while generating all cycle elimination constraints. Subsequently, in Table  3 , we solve MIP 1 with the iterative procedures presented in section 3.
Acknowledgements Thanks: The author would like to thank  Giovanni Bonaschi  and  Giacomo Di Gesú  for informative discussions on Large Deviations Principles while carrying out this work, and the two anonymous referees for helpful suggestions of a variety of improvements to this paper. Funding: This study was funded by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-10-LABX-0098 ).
Search Strategy. This study was conducted in Jeddah, Saudi Arabia, between August and November 2015. A search strategy was developed for the retrieval of all spine surgeryrelated articles. This study was performed by accessing databases and using the following protocol: "Search term" AND "Country name. " The time interval was restricted to 1/1/2000-30/6/2015. Abstracts were screened, and if all inclusion criteria were met, then the full-text was accessed for more data.
Data Items and Data Collection Process. Several items were collected from each article included in the analysis, namely, journal name, impact factor (IF), year of publication, affiliation, country, study design, LOE, citation numbers, and database. Regarding IF of individual journals, we collected 2015 IF for each journal. It is important to note that some articles were published in journals that were closed before 2015, so the corresponding IF for the last year of publication for such journals was included. Those items were collected in Excel spreadsheet. After the evaluation of each article, the result of the study was compared using several parameters similar to the ones published in another study  [2] , namely, Country, in other words, the country with the largest number of publications compared to the publications of the rest of countries.
Limitations We think that this study has a limitation regarding the interpretation of the results since most of the similar articles focus on either the publications of specific journals or publications about the particular geographical area, making the comparison biased. In-depth details of IF of journals such as yearly IF, 5-year impact, and article influence would have been reliable indicators of the quality of articles, but this was limited in this study, since a significant number of publications published in journals are not indexed in Thomson Reuters. Moreover, some studies might have been missed since we have restricted our search to only two databases "PubMed" and "Google Scholar"; these are major databases that usually include the majority of orthopedics, neurosurgery, and spine journals.
Introduction The European Union Statistics on Income and Living Conditions (EU-SILC) are becoming increasingly popular in socioeconomic and demographic analysis. 3 The main advantage of EU-SILC is its large country coverage, with a provision of harmonized socioeconomic measures for both individuals and households. EU-SILC is mainly used for economic analysis, with the demographic information (such as the number of children present in the household) used as controls. However, EU-SILC is also increasingly used for demographic analysis, particularly in fertility research, as the large international sample allows both marginal effects to be modeled and institutional determinants to be taken into account. EU-SILC provides not only cross-sectional data but also a follow-up of individuals and households, albeit for only a relatively short period (see the schematic for more detailed information about the database).
Description of the database The European U nion S tatistics on Income and Living Conditions (EU -SILC) is a European survey provided by Eurostat. This survey was created in 2003 to replace the European Community Household Panel (ECHP) and now includes 31 European countries. Since then, Eurostat has released a new wave every year. The survey gathers harmonized and comparable data at the individual and the household level on income and living conditions, as well as on adults' demographic and socioeconomic characteristics (sex, age, education, labor market position, parenthood etc. ). EU-SILC is composed of two datasets, one cross-sectional and one longitudinal. The annual cross-sectional data is produced from the longitudinal panel (integrated design). The longitudinal dataset of EU-SILC is a rotational panel of four years, which means that for the majority of countries, individuals are observed for a maximum period of four years.
Source: Eurostat Guidelines for EU-SILC, 2012 Some countries provide a follow-up of longer than four years (nine years in France, five or more years in some Eastern European countries). By contrast, there is no longitudinal database for Germany. The majority of countries joined the survey in 2004 and 2005, while several Eastern European and Mediterranean countries joined in later  (Malta, Croatia, Romania, etc. ). The survey contains information on both individuals and households. It is possible to identify adult women, their partner if they have one, and any children who live in the same household. EU-SILC does not report information on the number of children directly. However, children are observed with a proper identification number when living in their parents' households. For individuals aged 15+, EU-SILC provides both a register file and a personal file.
). This information is rarely available in other, more 'demographic' surveys. One exception is the Gender and Generations Surveys, but this survey has more limited country and time coverage (just three waves, and only the first wave is nationally representative). Also, in the GGS, information on socioeconomic characteristics of the partner is not available, and employment measures are less detailed than in the EU-SILC. Other surveys, such as the European Labor Force survey, contain information on labor supply but not on income. Some surveys exist that contain both demographic and economic variables, with individuals being tracked for more than only four years. But the limitation of these datasets is their national focus, since these long-run surveys generally cover only one given country (the German Socioeconomic Panel or the American Panel Study of Income Dynamics, for example).
We use the WB WDI rather than the Human Fertility Database (HFD) in this section in order to obtain the largest possible country coverage. The HFD is then used in our later analysis, as it provides information on age-specific fertility by birth order, but only for a subgroup of European countries. The WDI measures seem relatively unbiased, as they are limited to populations where the registration of births by official statistical agencies is virtually complete and where population estimates over the range of reproductive ages are reliable. To measure total fertility rates with EU-SILC, we observe children born in 2010 in the cross-sectional database of 2011, for women aged 15+. To obtain the TFR, we calculate the sum of age-specific fertility rates for each country. We compare this measure to the total fertility rates for 2010, given in the World Bank's World Development Indicators.
• Third, attrition is potentially linked to childbirth. Parents who are planning to have a child, who are about to have a child, or who have just had a child might move due to this event, which would increase the risk of dropping out of the survey (sampling attrition bias). Attrition affects not only the longitudinal database but also the cross-sectional EU-SILC samples, due to its integrated design (see the schematic in the introduction for more information about the integrated design in EU-SILC). In theory, as both the cross-sectional and the longitudinal data are based on nationally representative probability samples (see, for example, Eurostat, 2013, p. 20), the weighting procedure in EU-SILC should compensate for these losses. For each country Eurostat defines the minimum number of individuals and households required for sample sizes, and the database provides a series of weights (household design and cross-sectional weights, individual base weights, individual cross-sectional and longitudinal weights).
However, the documentation provided by Eurostat about how weights are constructed is unclear (see also  Iacovou, Kaminska, and Levy 2012) , and Eurostat gives no precise information about the effectiveness of the weighting procedure conducted in each country. Consequently, the degree to which the weighted data is really representative of each country is unknown, both in terms of particular demographic characteristics such as fertility and in terms of particular socioeconomic characteristics. The fact that we identify a downward bias in total fertility rates suggests that the weighting process is inefficient in compensating for the underrepresentation of childbirths/ newborn children in EU-SILC.
Attrition Among the three potential causes mentioned above, attrition merits particular attention due to its causal link with fertility. In general, efforts are made by the interviewers to follow up households for the foreseen time period, even when the household moves or splits. In the longitudinal survey, Household ID and Personal ID never change, not even when the person moves to another household (Eurostat 2013). Information on household and individual movements is included separately in the longitudinal database (coded as "household status" and "membership status" in EU-SILC). However, tracing individuals and households who move between waves is not always successful. In the French SRCV, for example, only 65% -70% of individuals who have moved in one year are followed http://www. demographic-research.
org 533 up in the next year. The likelihood of being followed up when moving is highest for the principal respondent, and much lower for the other household members. Longitudinal weights provided in EU-SILC are supposed to take into account the phenomenon of non-response, but they are constructed based on the implicit hypothesis that individuals who moved but could have been followed up have the same characteristics as those who moved but could not have been followed up  (Burricand and Lorgnet 2014). When comparing the events of childbirth reported by the longitudinal database to the ones reported by the cross-sectional EU-SILC database, we find that the longitudinal weights are calibrated to represent the cross-sectional population (for more detailed information, see Figures A-2 and A-3 and their interpretation in the Appendix).