dataesr/scientific-paragraphs-categorization huggingface dataset:
There is no general agreement on the reasons and approaches to correct the imbalance in the energy budget within EC measurements. In this product, the methodology used to calculate the energy balance corrected fluxes is based on the assumption that the Bowen ratio is correct  36. Fluxes are corrected by multiplying the original, gap-filled LE and H data by an energy balance closure correction factor (EBC_CF, in the dataset). The correction factor is calculated starting from the half-hours where all the variables needed to estimate energy balance closure are available (measured NETRAD and G, and measured or good-quality gap-filled H and LE). The correction factor for each single half-hour is calculated as in Eq. (  1 ), but is not applied directly. = - + EBC CF NETRAD G H LE _ ( )/( ) (1) First, to avoid transient conditions, the calculated EBC_CF time series is filtered by removing values outside of 1.
) CO 2 products. The processing steps applied to CO 2 fluxes were: calculation of net ecosystem exchange (NEE) from CO 2 turbulent and storage fluxes, applying a spike detection algorithm, filtering for low turbulence conditions using multiple friction velocity (USTAR) thresholds, gap-filling of all NEE time series generated by an ensemble of USTAR thresholds, estimation of random uncertainty, and partitioning of NEE into its ecosystem respiration (RECO) and gross primary production (GPP) components. Calculation of NEE. CO 2 storage fluxes (SC) express the change of CO 2 concentration below the measurement level of the eddy covariance system within the half-hour. NEE was calculated as the sum of the CO 2 turbulent fluxes (FC) and SC. Both FC and SC are part of the required data contributed by site teams.
Different methods have been proposed to estimate the USTAR thresholds and the related uncertainty as to how the approach works at a specific site  1. CP and MP USTAR threshold methods. Two methods to calculate USTAR thresholds were used: change-point-detection (CP) proposed by Barr et al. 41  and a modified version of the moving-point-transition (MP) described originally by Reichstein et al. 31  and Papale et al. 30. Both methods are similar in terms of data selection, preparation and grouping and aim to estimate the USTAR threshold value. Measurements collected when USTAR is below the threshold are removed. The difference between these methods is in how this threshold value is estimated. For both methods, the nighttime data of a full year are divided in four three-month periods (seasons) and 7 temperature classes (of equal size in terms of number of observations).
If not, the search for the plateau and threshold continued toward higher USTAR values. + >. × + + … + NEE USTAR x M EAN NEE USTAR x NEE USTAR x N EE USTAR x _ ( 1) 0 99 ( _ ( 2), _ ( 3), , _ ( 11)) (4) Bootstrapping USTAR threshold estimation. For each of the two methods, a bootstrapping technique was used. The full dataset (year of measurement) was re-sampled 100 times with the possibility to select the same data point multiple times (i. e. , with replacement), creating 100 versions of the dataset. The threshold values were calculated for each of them, obtaining 100 threshold values per method (CP and MP) and year, for a total of 200 USTAR threshold estimates for each year. This process and next steps are illustrated in Fig. 3. These 200 threshold values represent the uncertainty in the threshold estimation that could also impact the uncertainty of NEE.
On the other hand, a constant threshold across all the years would not represent changes in the ecosystem structure and EC system setup. For this reason, two approaches were implemented: • Variable USTAR Threshold (VUT): The thresholds found for each year and the years immediately before and after (if available) have been pooled together, and from their joint population, the final 40 thresholds extracted. With that, the USTAR thresholds vary from year to year; however, they are still influenced by neighboring years. This is identified in FLUXNET2015 variables by the "_VUT" suffix; • Constant USTAR Threshold (CUT): Across years, all the thresholds found have been pooled together and the final 40 thresholds extracted from this dataset. With that, all years were filtered with the same USTAR threshold.
This is identified in FLUXNET2015 variables by the "_CUT" suffix. If the dataset includes up to two years of data, the two methods give the same result, and only the _VUT is generated. For both the VUT and CUT approaches, 40 NEE datasets have been created, filtering the original NEE time series using 40 different USTAR values estimated as explained above. The values of the thresholds are reported in the AUXNEE product file. These 40 NEE versions have been used as the basis for all the derived variables provided. An example of the variability of the two methods (CP and MP) is shown in Fig. 4 , contrasting the distribution of the bootstrapped results for each method, showing comparable values for some years and divergent values for other years (of the same site). This highlights the importance of applying both methods in this ensemble-like way.
4  Example of the distribution of USTAR thresholds calculated for each year using the MP  30  method in blue and CP  41  method in green for the US-UMB site (dark green where they overlap). All these thresholds were pulled together to extract the CUT final 40 thresholds, while for the VUT thresholds, each year was pulled with the two immediately before and after (e. g. , 2005 + 2006 + 2007 to extract the 40 thresholds to be used to filter 2006). Note that the level of agreement between methods and between subsequent years is variable, justifying the approach that propagates this variability into uncertainty in NEE. of the NEE distribution, identified by the "_XX" numeric suffix) and the average value (identified in the dataset by the "_MEAN" suffix). Since the average value has a smoothing effect on the time series, an additional reference value of NEE was selected and identified in FLUXNET2015 variables by the "_REF" suffix, in an attempt to identify which of the 40 NEE realizations was the most representative of the ensemble.
g. , high quality storage measurement). The nighttime method uses nighttime data to parameterize a respiration-temperature model that is then applied to the whole dataset to estimate RECO. GPP is then calculated as the difference between RECO and NEE. The parameterization uses short windows of time (14 days) to account for the dynamic of other important respiration drivers such as water, substrate availability, and phenology (see Reichstein et al. 31  for details on the implementation and ONEFlux  22  for the code). The daytime method uses daytime and nighttime data to parameterize a model with one component based on a light-response curve and vapor pressure deficit for GPP, and a second component using a respiration-temperature relationship similar to the nighttime method. In this case, NEE becomes a function of both GPP and RECO, both of which are estimated by the model.
Information on the threshold values used for all versions of GPP and RECO (_NT and _DT, _VUT and _CUT, HH to YY resolutions) are in the auxiliary files for NEE processing (AUXNEE). Variables for reference GPP and RECO are also identified by a _REF suffix. The two methods for the partitioning (three for the cases in which the sundown method is applied) are not merged in any way, because their difference is informative with the respect to the uncertainty of the methods, as in the case of a model comparison exercise. Implementation approach. To increase the traceability of changes between versions of datasets and reduce uncertainty stemming from choices made at implementation time, we favored using original code implementations or thoroughly validated re-implementations of original codes. Thus, our code organization strings together loosely coupled components which implement each step, with clear-cut interfaces between steps.
This modular approach eases the maintenance and change efforts for any individual step, but adds complexity to evaluating changes for the entire pipeline. Different programming languages (Python, C, MATLAB and IDL, plus PV-WAVE for FLUXNET2015) were used to implement the different steps, all connected using a controller code that makes appropriate calls in the correct order. The ONEFlux 22 code collection replaced the PV-WAVE code with a re-implementation in Python, and also collates most of these steps into a cohesive pipeline (see also the Code Availability section). The IDL code, which applies the sundown partitioning method  44  , is not yet currently implemented in ONEFlux, because some additional testing and development are needed to make it robust and more suitable for general application.
Implementation details of individual steps are discussed next, with references to the outputs each step identified by an execution sequential number and the step name-e. g. , 01_qc_visual contains the results of the first processing step, the visual check step. Each of these steps correspond to a code module. Supplementary Fig. SM2  shows the steps and their inter-dependencies. Steps implemented in python. The main controller code for ONEFlux is implemented in Python. Besides being the glue code that executes each step, pre-and post-checks are also executed before and after each step. These checks guarantee that the input data meet the minimum requirements to run the step, that the minimum expected outputs were generated by the execution of the step, and that any errors or exception conditions were handled correctly.
Information about execution is recorded in a log for the entire pipeline, along with logs for individual steps. Besides the controller code, two of the three flux partitioning steps were re-implemented in Python (the nighttime and daytime methods, 10_nee_partition_nt and 11_nee_partition_dt), together with other specific steps such as data preparation for the uncertainty estimates (12_ure_input), and the creation and checking of final products (99_fluxnet2015). The original flux partitioning implementation in PV-WAVE was used for the LaThuile2007 and FLUXNET2015 datasets. Also, the tool for the downscaling of the ERA-I meteorological data is implemented in Python and runs on a server connected to the ERA data. Steps implemented in C. Several steps are implemented in the C programming language, allowing better control over execution performance of these steps.
These steps include: automated QA/QC flagging (02_qc_auto), USTAR threshold estimation using the MP method (04_ustar_ mp), the filtering and gap-filling of meteorological data, including the merging with the ERA-I downscaled data (07_meteo_proc), the filtering and gap-filling of CO 2 fluxes (08_nee_proc), the filtering, gap-filling, and energy corrections of energy fluxes (09_energy_proc), and the computation of uncertainty products (12_ure). The source codes and the compiled executables are provided for steps implemented in C, as well as build procedures in make/ Makefile format. Steps implemented in MATLAB. The estimation of USTAR thresholds using the CP method (05_ustar_cp) is the only step implemented in MATLAB. It is distributed both as source code and compiled code to be used with the MATLAB Runtime Environment, such that it does not require a license purchase.
Data records The FLUXNET2015 portion presented in this paper contains 1496 site-years of data from 206 sites  , characterizing ecosystem-level carbon and energy fluxes in diverse ecosystems across the globe (Fig. 1 , Supplementary Fig. SM1   251, 252  ), spanning from the early 1990s to 2014, with 69 sites having decade-long records. The da aset covers the distribution of ecosystem fluxes as reported in the recent meta-analyses  253, 254  (Fig. 6 ). The dataset is distributed in files separated by sites, by temporal aggregation resolutions (e. g. , hourly, weekly), and by data products (e. g. , FULLSET with all the variables and SUBSET designed for less experienced users). All data files for a site are available for download as a single ZIP file archive with site-specific DOI. The file-naming conventions details these options for each file (Table  1 ).
Site metadata are also available as a single file containing metadata for all sites, detailed later in this section and Supplementary Table  SM8. Note that DOIs are assigned at the site level, one DOI per site for all of that site's products. A DOI was not assigned to the whole FLUXNET2015 dataset, since this would make citation and assigning credit imprecise and hard to track. The FLUXNET2015 dataset provides data at five temporal resolutions. Site teams contribute either half-hourly (HH) or hourly (HR) datasets, depending on the integration/aggregation time decided by the site managers and function of the characteristics of the turbulence. References to half-hourly in this paper also apply to hourly data, unless explicitly stated otherwise. Half-hourly data are the basis of all the processing done for this dataset and are the finest grained temporal resolution provided.
The groups of products are: • AUXMETEO: Auxiliary data product containing information about the downscaling of meteorological variables using the ERA-Interim reanalysis data product (TA, PA, VPD, WS, P, SW_IN, and LW_IN). Variables in these files relate to the linear regression and error/correlation estimates for each data variable used in the downscaling. 1  for resolution options). The metadata follow the Biological, Ancillary, Disturbance, and Metadata (BADM  255, 256  ) standards and are provided in the BADM Interchange Format 29 (BIF). Table  3  illustrates the type of metadata included with selected metadata variables (See full lists and descriptions of the metadata in Supplementary Tables  SM2-SM7 ). Height and instrument models for the flux variables, as well as soil temperature and moisture depths, are reported in the Variable Information metadata.
Our results increase confidence in EC …". The approach of Campioli et al. 259  requires sites that have several additional (and rare) pieces of information; therefore, it is not generally applicable, particularly not across the sites used in this study. However, the eddy covariance site teams co-authoring this paper have compared and technically validated their measurements with respect to knowledge of their site. Unavoidably, measurement and processing uncertainties exist, and can be large for certain sites and ecosystem conditions. However, in general, flux values provided in this dataset are consistent with expectations, and eddy covariance remains one of the more reliable techniques for assessing land-air exchanges at ecosystem scales.
Usage Notes Detailed documentation on how to use and interpret FLUXNET2015 is available online at https://fluxnet. fluxdata. org/data/fluxnet2015-dataset/. Here, we present some of the main points to guide the usage of the data. risks in the application of standard procedures. When standardized procedures are applied across different sites, the possible differences owing to data treatment are avoided or minimized; this is one of the main goals of FLUXNET2015 and ONEFlux. However, there is also the risk and possibility that the standard methods don't work properly or as expected at specific sites and under certain conditions. This is particularly true for the CO 2 flux partitioning, which as with all models is based on assumptions that could not always be not valid. For this reason, it could be necessary to contact the site PIs that are listed in Supplementary Table  SM9.
Similarly to NEE, there are gap-filled and energy balance corrected versions of H and LE variables; therefore, it is also important to clearly refer to which version is used. The SUBSET version of the dataset includes a reduced number of variables, selected for non-expert users. We encourage users to carefully evaluate their requirements and options in the dataset, and if needed to contact regional networks, site teams, or even co-authors of this article for help and recommendations. For more detail, see the Methods section above. Temporally aggregated resolutions. All data products are provided at multiple temporal resolutions where feasible. The finest resolution is either hourly or half-hourly (indicated by the filename tags HR and HH, respectively). These data are then aggregated into daily (DD), 7-day weekly (WW), monthly (MM), and yearly (YY) resolutions, with appropriate aggregations for each variable, such as averaging for TA and summation for P.
Known issues. A list of known issues and limitations relevant to the dataset is maintained online: http:// fluxnet. fluxdata. org/data/fluxnet2015-dataset/known-issues/. releases of the FLUXNET2015 dataset. The original FLUXNET2015 release was in December 2015, followed by incremental releases in July 2016 and November 2016, and, finally, a release in February 2020 with fixes and additional metadata as described in this paper. More information on the releases can be found in the online change log: http://fluxnet. fluxdata. org/change-log/. A newer release replaces all previous ones, and only the newest release is available for direct download. Access to previous versions can be obtained upon request. Support to FLUXNET2015 data users. Scientists and staff responsible for the creation of the dataset offer support to data users and can be reached at fluxdata-support@fluxdata.
Updates and future versions. There is strong interest and engagement in order to ensure the availability of new data (new sites and new years), keeping the open policy and the high quality data that we tried to reach with this work. We expect that the processing pipeline and QA/QC procedures will continue evolving, in support of new products. However, the amount of both technical and coordination work, along with difficulty securing long-term international funding, hamper creation of new versions of the dataset. There are ongoing discussions among regional networks and FLUXNET on this coordination, but currently there is no plan for a follow-up version of FLUXNET2015.
SITE_VERSION indicates the version of the original dataset for the site used; CODE_VERSION indicates the version of the code of the data processing pipeline used to process the dataset for the site EXT File extension • csv: Comma-separated values in a text file (ASCII) • zip: Archive file with all temporal resolutions for the same site and data product Examples of file names and structures: FLX_US-Ha1_FLUXNET2015_FULLSET_HH_1992-2012_1-3. zip -FLX_US-Ha1_FLUXNET2015_FULLSET_HH_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_FULLSET_DD_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_FULLSET_WW_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_FULLSET_MM_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_FULLSET_YY_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_ERAI_HH_1992-2012_1-3. csv -FLX_US-Ha1_FLUXNET2015_ERAI_DD_1992-2012_1-3.
Code availability The ONEFlux collection of codes used to create data intercomparable with FLUXNET2015 has been packaged to be executed as a complete pipeline and is available in both source-code and executable forms under a 3-clause BSD license on GitHub: https://github. com/AmeriFlux/ONEFlux. The complete environment to run this pipeline requires a GCC compatible C compiler (or capability to run pre-compiled Windows, Linux, and/or Mac executables), a MATLAB Runtime Environment, and a Python interpreter with a few numeric and scientific packages installed. All of these can be obtained at no cost.
acknowledgements We thank the many people who helped with generating high quality data through the years at all sites, and the funding sources to the sites and networks, for the data collection, curation, and sharing that make this dataset possible. The eddy covariance data were acquired and shared by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, Swiss FluxNet and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by Laboratoire des sciences du climat et de l' environnement (LSCE). The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster and the AmeriFlux Management Project (with support by European Union H2020 projects and U.
However, these models impose rigorous distribution assumptions, require data with particular scaling properties, and have limited flexibility (cf. Belton & Stewart, 2002). In addition, the correlations found do not necessarily imply causation, so, to model and analyze complex systems' behavior more fully, causal links need to be examined in depth. These issues strengthened the current study's motivation to apply the proposed dual methodology. To achieve the main objectives, various secondary objectives also needed to be fulfilled. The first was to recruit a panel of experts with experience in urban investment. The second objective was to conduct an initial session with this group in which the factors attracting urban investment were identified, as well as their cause-and-effect relationships and internal hierarchy, thereby constructing a basic cognitive structure of the decision problem.
He also concluded, after the scenarios were explained, that the ability to reflect the dynamics of different variable behaviors over a given period could be the model's main advantage. This interviewee further pointed out that relying on a panel of experts makes perfect sense for the topic under study as they know the most about the subject, while including the general population would make no sense even though some studies reviewed in the literature have done this. Finally, he mentioned "that the model can be adapted to specific contexts is an asset that ensures it has practical applications and arouses a larger number of entities' attention" (again, in his words). Regarding the model's applicability, the vice-president identified a limitation, namely, that the model needs to be more intuitive and user-friendly and less complex than the version generated by the Vensim software.
Acknowledgements This work was partially funded by the  Portuguese Foundation for Science and Technology  (Grant  UID/ GES/00315/2019 ). Records of the expert panel meetings, including pictures, software output and non-confidential information of the study, can be obtained from the corresponding author upon request. The authors gratefully acknowledge the superb contribution and knowledge sharing of the panel members:  André Casaca ,  Daniel Martins ,  Elsa Soares ,  Francisco Abreu ,  Isabel Abreu ,  Rita Fonseca Lopes ,  Rui Carreto , and  Vítor Alfaro. The authors also would like to express their gratitude to  Hugo Santos Ferreira, Vice-President of the Portuguese Association of Real Estate Developers and Investors , for his availability and the important insights he provided during the consolidation of results.
Female and male KSR-iPSCs show loss of methylation at imprinted regions To screen for imprinting methylation fidelity in KSR-iPSCs, we employed allelespecific IMPLICON previously validated on F1 tissue samples from reciprocal BL6 x CAST crosses  (Klobucar et al. , 2020). This method combines bisulfite treatment of genomic DNA with amplicon high-throughput sequencing with a de-duplication step to generate base-resolution datasets with high coverage and allelic discrimination of the original DNA molecules. We focused on 8 imprinted clusters together with 2 unmethylated and 1 methylated control regions. As expected, no differences between the two parental alleles were observed for both unmethylated (Sox2 and Klf4 genes) and methylated controls (Prickle1 gene) in female and male MEFs and respective KSR-iPSCs, as well as, for the TX 2i ESCs (Fig.