dataesr/scientific-paragraphs-categorization huggingface dataset:
Data portal The ISMN can be accessed at ismn. earth and consists of a project website containing, e. g. , information about networks, data, quality control, and partners, and a data interface where users can view, query, and download the data contained in the database. The data interface displays the location and information of networks and single stations, and allows for plotting the available data to gain an impression of data availability and quality. Data can be selected for time period, area, single networks, or entire continents. Alternatively, the data download can be selected via an advanced SQL query, which allows users to make more specific selections (e. g. , for sensor brand, or a certain depth range). The selected data are directly extracted from the database and downloads are organised per network.
For each network, the download contains (i) the measurements and their quality flags, (ii) information about the file data organisation, (iii) a description of the ISMN quality flags, (iv) a metadata file compliant with ISO 19115 and INSPIRE (Infrastructure for Spatial Information in the European Community) metadata standards, and (v) information about static site characteristics (e. g. , land cover, climate class, soil characteristics). The data set files are formatted according to the CEOP (Coordinated Energy and water cycle Observations Project)  (Williams, 2010)  standard, or a slightly modified version of it, in order to improve data usability  (Dorigo et al. , 2011b). These standards use the ascii format but a NetCDF format is foreseen for the near future.
Data provider and user involvement The ISMN is entirely built on the voluntary, free-of-charge contributions from scientific and operational providers. This prevents the ISMN from being too prescriptive towards the data providers in terms of delivery intervals, automation and formatting. Hence, a careful balance is needed between inclusiveness on the one hand and data quality standards on the other. The ISMN facilitates between users and data providers by reporting data quality issues and user feedback to the providers very six months. This is done by means of a report on data usage statistics for each individual network, e. g. , on the number of downloads, the usage of their data in scientific publications, and flagging statistics. Together with obtaining visibility and citations, obtaining feedback on data usage and data quality is one of the primary motivations for data providers to join the ISMN.
More than 3200 active users have registered since 2009 (status December 2020). Data download is free of charge but user registration (compliant with the latest GDPR privacy standards) is required to prevent misuse of the data and to track (undisclosed) statistics on data usage for the half-yearly provider reports. News feeds on the ISMN web page and a bi-annual newsletter informs the users about new networks, new data sets, data quality issues, important publications, workshops, and more. In addition, a dedicated forum and classical email exchange allow users to raise and get response to issues. Moreover, open source software packages are available for reading and plotting the data (Section 6). 3 Quality control.
e. , GLDAS-Noah). The spectrum-based flags are the result of a series of conditions applied to the soil moisture measurement time series and their first and second derivatives. The geophysical consistency and spectrum-based methods are only applied to the soil moisture observations, while geophysical dynamic range verification is applied to all dynamic variables in the database. Recently, a few refinements of the original flagging procedures as described and assessed in  Dorigo et al. (2013)  were implemented to increase the flagging accuracy: -The outlier detection method (Flag D06) now allows spikes to last two consecutive time steps instead of the initial one hour. This occurs when all conditions of Eq. [4-6] in  Dorigo et al. (2013)  are met, but the peak value remains unchanged for an additional hour.
7%). We assessed the refined flagging procedures by applying them to 10 networks with hourly data that include stations with multiple soil temperature, air temperature, or precipitation sensors. Despite the very low overall impact of the refined flags, for some networks they are substantial (Figure  3 ). Measurements that are detected as erroneous by the quality control procedures are not deleted from the database, but tagged as such (Table  3 ). The flag is provided as additional attribute ("ISMN Quality Flag") to the observation upon download and can take one of the main categories C (exceeding plausible geophysical range), D (questionable/dubious), M (missing) or G (good). The D flag is raised when either a geophysical-consistency or a spectrum-based check is positive. An additional number indicates the actual cause for flagging (Table  3 ).
A soil moisture observation may receive multiple C-and D-type flags but the good and missing flags are exclusive. Seven networks provide their own soil moisture quality flags, which are added to the ISMN database in addition to the ISMN flags common to all time series. Examples are flags for data quality (without further methodological description) or simple thresholds. For instance, RISMA tags soil moisture observations for frozen soils when the average temperature of two adjacent soil layers is below 0°C  (Pacheco et al. , 2014). By contrast, the ISMN flag D01 "Soil temperature <0°C" only considers the corresponding depth. For RISMA, the frozen soil flags provided by the network and those computed by the ISMN (D01 "soil temperature <0°C") agree for 87. 8%.
, 2014; Ojha et al. , 2014; Qin et al. , 2018; Kumar et al. , 2019a; Tian et al. , 2019a; Nicolai-Shaw et al. , 2015b; Dong et al. , 2020; Wang et al. , 2017; Verrier, 2020; Deng et al. , 2020a) , flood dynamics  (Esposito et al. , 2018) , or the impact of soil moisture on plant growth  (Hottenstein et al. , 2015). The ISMN has been used to develop and test new statistical validation approaches  (Zwieback et al. (2013) , Gruber et al. McCalmont, 2017)), soil moisture metadatabases  (Liao et al. , 2019; Xia et al. , 2015) ), or validation services  (Kumar et al. , 2012) , and to assess or improve alternative sensing techniques and constellations  (Xaver et al. , 2020; Kapilaratne and Lu, 2017; Nguyen et al. , 2017; Mahecha et al. , 2017). 5 Challenges and opportunities 5. 1 Scientific challenges.
Automation Part of the contributions to the ISMN (6 networks with over 900 stations) are inserted into the ISMN in NRT. This process is fully automated, including data downloaded from data providers, harmonisation, flagging, insertion into the database, and updating metadata tables. However, because of different data recording and handling mechanisms at the provider side and differences in data sharing mechanisms and policies the ISMN is also partially manually operated and will probably also have to continue in this way in the future. The differences between the fully automated and the manual approach is confined to data download and data ingestion into the processing chain. Data harmonisation and quality control is automated for all data sets (see Figure  C1 ). A major challenge in the automation process is the enormous heterogeneity of input data formats.
Transparency and traceability The ISMN data collection is constantly evolving: New data records are added and existing ones are extended or, if necessary, reprocessed and corrected. Not only the data, but also the underlying code changes. These updates and any retroactive changes made to the data archive are tracked within the ISMN but not yet readily passed on to the users. The updates can lead to differences in analyses on the user side, e. g. , when considering obvious changes such as temporal extensions or new stations, and lead to non-reproducible results. Therefore, any update must be traceable and clearly communicated, requiring a system to track and store these changes. Version tracking and digital object identifiers (DOIs) are ways to identify each database access and therefore allow tracing back to past states of the ISMN.
Conclusions In this study we reviewed the first decade of operations of the ISMN. Besides satisfactorily fulfilling its initial target, i. e. , supporting satellite soil moisture product validation and calibration, many additional more or less foreseen uses have emerged. In from ESA, all network data sets have been freely contributed by dedicated researchers. To guarantee the availability of these resources for climate and environmental monitoring also for the next decade, we plead with governments and international bodies for systematic funding of both the ISMN and its participating data-providing networks. To maximise geographic coverage and data usage, the policy of the ISMN has always been to integrate datasets without strict requirements on sensors, sampling protocol, or data quality.
The emerging strongly heterogeneous data set characteristics call for far-going quantification and traceability of errors, from sensor calibration to data download and from the point measurement to the spatial-temporal support of the application. Thus, besides expanding its coverage to data-poor regions and landscapes, the ISMN shall focus the next 10 years of operation on developing methodologies that allow observations to qualify as Fiducial Reference Measurements for soil moisture applications. https://doi. org/10. 5194/hess-2021-2 Preprint. Discussion started: 28 January 2021 c Author(s) 2021. CC BY 4. 0 License.
A2 AMMA-CATCH The AMMA-CATCH observatory gathered data from densely instrumented mesoscale sites in West Africa (Benin, Niger, Mali). The network is devoted to long-term regional monitoring of global change impacts on the critical zone. Height stations in Benin and four stations in Niger of the network are included in the ISMN from 2006 to present, including surface soil moisture and root-zone soil moisture until one-meter depth. For more information, see  Galle et al. (2018).
A8 CAMPANIA The CAMPANIA network consisted of two stations located near the city of Naples, southern Italy. It was managed by the "Centro Funzionale per la Previsione Meteorologica e il Monitoraggio Meteo-Pluvio-Idrometrico e delle Frane". The ISMN contains data from the operational start in 2000 until the end of 2008. The data sets include soil moisture measured at a depth of 0. 30 m, precipitation, and air temperature. For more information on the performance of the network, see  (Brocca et al. , 2011). Sciences in Beijing  (Liu et al. , 2001). The data set were transferred to the ISMN from the Global Soil Moisture Data Bank  (Robock et al. , 2000)  and contain measurements amde at the 8th, 18th and 28th of each month.
A17 GROW GROW gathered crowd sourced observations to assess the temporal and spatial consistency of various satellite-derived soil moisture products. 6,500 low cost sensors were deployed in 24 GROW places, in 13 Countries across Europe. A subset of 150 sensors were transferred to the ISMN  (Zappa et al. , 2019 (Zappa et al. , , 2020)) ) and contain measurements of Soil Moisture (%), Temperature (C) and metadata: Latitude and Longitude of the Sensor, Name of the Sensor, Date of Observation. Complete data set is licensed and available at https://doi. org/10. 15132/10000156. More information on the quality of the data can be found in  Xaver et al. (2020).
A27 IOWA The IOWA network was located in two catchments in the southwestern part of Iowa, USA. Soil moisture observations from 6 stations, until 2. 6 meter depth, with an interval of twice a month during 1972 to 1994 (April to October) are included in the ISMN  (Robock et al. , 2000). This network was formerly included in the Global Soil Moisture Data Bank.
A34 MAQU The Maqu monitoring network (  (Su et al. , 2011; Dente et al. , 2012)   from the Harmonized World Soil Database and subsequently calculating the wilting levels for all stations using the equations of  Saxton and Rawls (2006). Soil moisture measurements are provided three times a month ( on the 8 t h, 18 t h and 28 t h)from 1964 to 2002 during the warm period of the year, which runs from April until the end of October (e. g. ,  Robock et al. (2000) ).
A44 RISMA The Real-time in situ Soil Monitoring for Agriculture (RISMA) network was established in 2011 by Agriculture and Agri-Food Canada at agricultural locations in Ontario, Manitoba, and Saskatchewan  (Ojo et al. , 2015; J. , 2011; Canisius, 2011). There are currently 23 RISMA stations collecting hourly soil moisture/temperature data at depths to 1 -1. 5 metres in combination with meteorological data. Calibrated, quality controlled soil moisture and weather data are provided to the ISMN on an annual basis.
A47 SASMAS The Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) monitoring network commenced commissioning in late 2002 with a total of 26 stations in operation by 2003 across a 6,500 km2 catchment  (Rüdiger et al. , 2007). Soil moisture is observed up to a depth of 90 cm (where possible) at depth intervals of 0-30, 30-60, and 60-90 cm. The network was developed as a nested catchment with three different types of scales (low-resolution across the entire catchment, medium density across to smaller subcatchment, and very high density across a 175 ha single reach. This site also hosted the second NAFE campaign for which additional data was collected.
A65 WSMN The Wales Soil Moisture Network was founded in July 2011. It consists of a total of 9 monitoring sites located in Mid-Wales representing a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and the most typical land use/cover types. The data set acquired in the network are composed of 0-5 (or 0-10) cm soil moisture, soil temperature, precipitation as well as other ancillary data  (Petropoulos and McCalmont, 2017). Figure  E1. Overview of all mandatory metadata information available for each station within the ISMN database. Table  F2. Overview of scientific studies using ISMN data for model evaluation and development. ORCHIDEE-  MICT Yin et al. (2018).
A46 RUSWET-AGRO, RUSWET-GRASS, RUSWET-VALDAIThe three historical RUSWET networks were agricultural prediction campaigns conducted by the State Hydrological Institute of the Former Soviet Union within the area of present Russia. Measurements were taken from 1952 until 2002 and initially distributed through the Global Soil Moisture Data Bank. Altogether, the networks operated 337 sites at which soil moisture was measured three times per month via gravimetric sampling. At RUSWET-VALDAI also soil temperature, precipitation, and air temperature were collected. RUSWET contributes both the northernmost (on Mc-Clintock Island) and the earliest (June 8th.
(2011). A62 VDSThe VDS network is run by VanderSat, a dutch company specialised in providing global satellite-observed data and services over land. The VDS network consists of four stations located near the city of Bago in Myanmar. The network was installed to validate satellite soil moisture products in the tropics. The network has two measurement periods, one from June 2017 to July 2018 and one from March 2020 onwards. A63 VASThe "Valencia Anchor Station" networis operated by the Climatology from Satellites Group and Jucar River Basin Authority of the University of Valencia, Spain. The network is located in Spain and consists 3 stations. The datasets are available for the years 2010 and 2011.
Acknowledgements. The authors greatly acknowledge the financial support provided by  ESA  through various projects:  SMOSnet International Soil Moisture Network  (contract No. 4000102722/10/NL/FF/fk  and operations contract No. 3-13185/NL/FF/fk ); IDEAS+ (contract No. TVUK/AG/18/02082 );  QA4EO  (contract No. TPZV/UK/AG/19/02321 ). Additional funding for methodological advances has been received from the  EU   FP7   EartH2Observe  project (Contract No. 603608 ), the  EU H2020   GROW  project (Contract No. 690199 ), and the  QA4SM  project funded by the  Austrian Space Applications Programme 14  (Contract No. 866004 ). We acknowledge the endorsement of various international bodies, including CEOS, WCRP GEWEX, GCOS, GTN-H, and  GEO. We greatly thank all staff members from the participating networks for their continued technical support.
Code and data availability. Upon registration, all data and metadata described in this paper can be downloaded for free from https://ismn. earth. A python package to read and plot the data and metadata ((TUW-GEO/ismn: v0. 4) can be accessed on doi. org/10. 5281/zenodo. 3966399. An example of output of this package is shown in Figure  1. A python package containing the ISMN quality control procedures for in situ soil moisture is available on GitHub (https://github. com/TUW-GEO/flagit).
50 -1. 60 12 21 15 GTK RUSWET-AGRO Geological Survey of Finland (name of in situ network in Finland) Name of in situ network in Former Soviet Union 1. 60 -1. 70 0 0 0 HiWATER_EHWSN RUSWET-GRASS Heihe Watershed Allied Telemetry Research Wireless Sensor Network (in China) Name of in situ network in Former Soviet Union 1. 70 -1. 80 3 35 18 HOBE RUSWET-VALDAI Hydrological Oberservatory in Denmark Name of in situ network in Former Soviet Union 1. 80 -1. 90 1 1 1 HSC_SEOLMACHEON SASMAS 1. 90 -2. 00 Hydrological Survey Center (HSC) in -situ network in South Korea Scaling and Assimilaton of Soil Moisture and Streamflow 8 20 20 HWSD SCAN Harmonized World Soil Database Soil Climate Analysis Network 2. 00 -2. 10 13 10 8 HYDROL-NET_PERUGIA SWATS Name of in situ network in Italy Surface Energy Balance System, sensor network of ARM ICN SKKU Illinois Climate Network (name of in situ network in USA) Name of in situ network in South Korea IIT_KANPUR SMOS Indian Institute of Technology Kanpur (name of in situ network in India) Soil Moisture and Ocean Salinity IGiK SMOSMANIA Institute of Geodesy and Cartography Soil Moisture Observing System -Meteorological Automatic Network Integrated Application SNOTEL Table continued on next page SNOwpack TELemetry Table continued on next page.
However, the metabolic costs of cellular and systemic recalibrations in mitochondrial disorders have not been defined. Here we examine this question in clinical datasets and in two in vitro models. Clinically, OxPhos dysfunction causes a broad spectrum of multi-system disorders where symptoms include, among others, fatigue and exercise intolerance  12, 13. As a result, most patients with mitochondrial diseases curtail physical activity and exercise  14, 15. A common misconception arising from this clinical picture is that a reduced mitochondrial capacity to oxidize substrates  16  coupled to minimal physical activity levels would promote an energy conservation response, resulting in positive energy balance and body fat accumulation, leading to obesity. However, patients with mitochondrial diseases are rarely obese.
Finally, we report that OxPhos defects and hypermetabolism are linked to accelerated telomere shortening and epigenetic aging in fibroblasts, and provide a publicly available longitudinal dataset to query epigenetic and transcriptional signatures conserved across both cellular models. Our analyses highlight how the associated resource dataset can serve as a discovery platform to identify potentially targetable pathways contributing to hypermetabolism, as well as downstream mechanisms linking hypermetabolism to cellular and clinical phenotypes. Together, these translational data implicate hypermetabolism as a pathophysiological feature of mitochondrial diseases and lifespan reduction.
In patients with mitochondrial diseases, resting heart rate, which correlates with whole-body REE  48  , was on average 10. 7% higher than healthy controls (p<0. 01, Figure  1C ). This tachycardia reached up to +46% when patients and controls performed mild exercise at the same absolute workload (data not shown). Both at rest and during mild physical activity, as initially reported in a small study  49  , patients had on average 244% higher blood or urine catecholamine levels (p<0. 05, Figure  1D ), particularly norepinephrine (NE), a neurohormone sufficient to elevate REE when administered systemically to healthy individuals  50. To estimate REE in mitochondrial disease patients, we used resting whole body VO2 expressed relative to body weight, which, although imperfect, was available in the largest number of studies.
OxPhos defects upregulate energy-demanding cellular programs From our longitudinal RNAseq dataset, we noted changes in the totality of genes related to the ribosomal machinery, which is produced in proportion with cellular biosynthetic demands (Extended Data Figure  10A ). Despite their significantly reduced growth rate, both SURF1 (+19%, p=2. 4e-10, g=2. 59) and Oligo-treated cells (+50%, p<0. 01, g=0. 86) showed a marked time-dependent upregulation in the ribosomal machinery, consistent with the hypersecretory phenotype (secreted proteins must be transcribed and translated) as well as the elevated metabolic demands of translation that competes with cell growth  42, 74  (Extended Data Figure  10B ). To characterize the genome-wide gene regulatory changes associated with these cellular phenotypes, and to gain insights into the potential cause(s) of hypermetabolism in response to OxPhos dysfunction, we next deployed time-sensitive models of gene regulation and DNA methylation.
Both SURF1 and Oligo-treated cells displayed a significant perturbation of transcription and translation processes (Figure  6D ). Upregulated genes were enriched for pathways related to Golgi vesicle transport, fibroblast growth factor (FGF) binding, VEGF receptor signaling pathway, and the unfolded protein response, a signature consistent with increased secretion and inter-cellular signaling activity. Downregulated genes were over-represented for processes relating to cell division, consistent with the slower division rates (i. e. , quiescence or senescence) of SURF1 and Oligo-treated cells. Lifespan gene expression trajectories in this dataset showed some noteworthy features of OxPhos dysfunction and hypermetabolism at the single-gene level: i) ISR-related genes are robustly upregulated in a time-dependent manner by up to ~16-fold for the transcription factor CHOP (DDIT3), and ~60-fold for its downstream target GDF15; ii) the age-related upregulation of senescence-related genes (e.
DNA methylation recalibrations in OxPhos-induced hypermetabolism To examine nuclear DNA methylation (DNAm) and create a resource dataset with broad utility for pathway discovery, we measured DNA methylation levels at 865,817 CpG sites (Illumina EPIC array) in Control, SURF1, and Oligo-treated cells at multiple time points across their cellular lifespan (n=66). We then leveraged these high dimensional data by building mixed-effects models that consider the underlying data structure (donors, longitudinal observations) to identify robust conserved DNA methylation changes associated with OxPhos dysfunction and hypermetabolism. Visualizing the general data structure using t-SNE showed, that: i) as expected, the methylome signature of each donor was relatively distinct; ii) DNAm exhibited consistent age-related shifts, iii) SURF1 cells clustered separately from control, while iv) Oligo cells caused a modest time-dependent shift away from their respective controls (Figure  7A ).
Next, we leveraged our DNAm dataset to quantify biological age using validated multivariate algorithms or "clocks" (DNAmAge, or epigenetic clocks) trained, in human tissues, to predict chronological age and mortality  55, 83. Five different validated clocks that rely on different CpG sets and include a modification that improves their accuracy  84  were applied directly to our fibroblast time series DNAm data. These results showed that relative to the rate of epigenetic aging in control cells with normal OxPhos function, the rates of biological aging per population doubling were accelerated by an average of 131% in SURF1 cells (p<0. 05, g=1. 5), and to a lesser extent in Oligo-treated cells (+54%, p<0. 05, g=0. 6, Figure  8F-G ), thus independently supporting the findings of accelerated telomere shortening.