dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus far, studies using learning data derived from online environments rarely reach beyond one institutional context. This is a fundamental aspect of our work, demonstrating that more crosscontextual explorations must be carried out in order to appropriately generalize experimental and analytic findings. Finally, our study sheds light on the limitations of digital data to understand social architectures online. At the onset we encountered the limitations of digital data for longitudinal network analysis, which were consistent across three universities. Data patterns of intermittent participation were also consistent. The sparsity of repeated interactions was common across all three datasets, i. e. students rarely interacted with the same peers across more than one semester. We made a major methodological decision enabling longitudinal analysis of social relations, as we extracted pairs with repeated interactions to model social processes.
From the 519 papers that we examined, we extracted data on sites the authors called "ponds" and other variants (e. g. , 'small ponds' , 'fish ponds' , but NOT 'lakes'). We filtered waterbodies that were ≤ 20 ha surface area and ≤ 9 m depth (global distribution; n = 1327) to include waterbodies slightly greater than the maximum depth and maximum surface area used to define ponds in prior studies  34, 35. To compare ponds to lakes and wetlands, we used existing lake (US and Europe; n = 55,173) and wetland (US; n = 400) databases; waterbodies were classified as lake or wetland by the scientists or managers who published the database. Wetlands were classified as < 1 m in depth with no defined surface area and lakes were all > 0. 02 ha with no defined depth (see "Methods'' for details). From the waterbodies that scientists called "ponds," hydroperiod and origin varied over a large range of characteristics.
Does ecosystem structure and function distinguish ponds from lakes and wetlands? We evaluated the relationship between key metrics of ecosystem structure or function with three quantitative variables that often showed up in pond, lake, or wetland definitions: surface area, maximum depth (hereafter depth), and emergent vegetation cover. Our metrics of ecosystem structure or function include nutrients (total phosphorus (TP), total nitrogen (TN)), water chemistry (pH), primary producer biomass (chl a), metabolism (gross primary production-GPP, respiration-R, net ecosystem production-NEP), and heat and gas distributions and movement (diel temperature ranges-DTR, methane fluxes, gas transfer velocities). The data was collated from global surveys of literature and federal or international databases (see "Methods") with ultimately ten comparisons for surface area, six comparisons for depth, and four comparisons for emergent vegetation cover with a range of sample sizes for each comparison (n = 67 to 7931, see Tables  S3, S5 , S7).
Methods Literature survey. To compile biological, physical, and chemical characteristics of ponds, we conducted a literature search based on three seminal papers establishing the ecological importance of ponds: Oertli et al. 35  , Søndergaard et al. 18  , and Downing  8  , each of which has > 100 citations and is more than ten years old. We conducted a backwards and forwards search in April 2019 to compile all papers cited by these three papers, and all papers that cited them, yielding 519 unique papers. We extracted physical, chemical, and biological data for papers that reported data for individual waterbodies defined as ponds by the publishing scientists. To ensure consideration of all potential ponds, we checked that waterbodies selected were small (≤ 20 ha in surface area) and shallow (≤ 9 m in maximum or mean depth), boundaries that are slightly greater than the maximum depth (8 m)  35  and maximum surface area (10 ha)  34  used to define ponds in a few prior studies.
We received responses from 42/50 states and evaluated all definitions provided and their associated legislation. Pond, lake, and wetland data. We compared chemical and biological characteristics among various lentic waterbodies as defined by scientists as ponds, wetlands, or lakes. For ponds, we used data from the literature data extraction as described above (n = 1327). Wetland data came from the US EPA's 2011 National Wetland Condition Assessment, which surveyed wetlands with standing water < 1 m in depth and variable surface area  54, 55. We selected wetland sites that were freshwater and had water chemistry data (n = 400). Lake data was extracted from LAGOS-NE (lakes ≥ 4 ha; n = 51,101)  56  , EPA's 2012 National Lake Assessment (lakes ≥ 1 ha; n = 1130)  57, 58  , and the European Environmental Agency's Waterbase database (lakes > 0.
02 ha; n = 2942)  59. From these sources, we compared nutrients (total phosphorus (TP), total nitrogen (TN)), water chemistry (pH), and primary producer biomass (chl a) among waterbody types (ponds, wetlands, and lakes). We also examined differences in six additional metrics of ecosystem function across waterbodies using data from a variety of sources and ranging in sample size from 67 to 198 global sites (gross primary production-GPP, respiration-R, net ecosystem production-NEP, diel temperature ranges-DTR, methane fluxes, gas transfer velocities). We extracted metabolism metrics (GPP and R) from an existing literature review  60  and two published studies of various sized lentic ecosystems  41, 61. DTR, calculated as the diel difference between the maximum and minimum surface temperature for each waterbody, were extracted from multiple studies  38, 39.
Acknowledgements This work was supported by the  Global Lake Ecological Observatory Network (GLEON). We thank participants in the GLEON 19 pond ad hoc meeting for discussions that formed the basis for this project. MRA was supported as part of the  BEYOND 2020 project  (grant -aid  agreement no. PBA/FS/16/02 ) by the  Marine Institute  and funded under the  Marine Research Programme  by the  Irish Government. Funding to KSC and KBSK was from the  US National Science Foundation  ( EF-1638679  and  EF-1638539 ). KKH was supported by the  Adam S. Thomas Endowment  and by the  St. Olaf Collaborative Undergraduate Research and   Inquiry Program. This material is.
In New Zealand, for example, there is a lack of data showing the consequences of degradation in the environment on the economy and society (Parliamentary Commissioner for the Environment, 2019). Equally, the New Zealand Treasury has developed the Living Standards Dashboard as a measurement tool to inform on crossgovernment policy priorities  (New Zealand Treasury, 2019a). However, it is not clear how the natural capital indicators were chosen and how they connect to New Zealander's well-being. Our process can provide the first step towards an evidence-based approach to identity which indicator(s) should be developed or used to track natural capital and its influence on well-being. At a policy level, identifying the important connections between ES/NCP and well-being domains can help target policy initiatives to achieve specific well-being outcomes.
g. dose reduction strategies in CT) through centres of excellence (3) To create an EU network of a multidisciplinary team of experts to "train the trainers" (4) To develop short duration courses focused on 1 key objective (e. g. how to measure dose output in CBCT) (5) To create, and maintain up-to-date, a centralised peer reviewed database of E&T in RP and RAM resources augmented reality software options, which can engage learners more effectivity than some traditional teaching pedagogies and should be incorporated in proposed training frameworks  [29].
Acknowledgements Advisory Board members:  Werner Jaschke  (CIRSE);  Michael Lassmann  (EANM);  Constantinos Koutsojannis  (EFOMP);  Sonya Mc Fadden  (EFRS);  Salvador Pedraza  (ESR);  Dirk Verellen (ESTRO) ;  Katrien Van Slambrouk (HERCA) ;  Jenia Vassileva  (IAEA);  Maria Perez  (WHO) External Experts:  Roberto Sánchez  ( Medical Physics Expert );  François Jamar  ( Nuclear Medicine Physician) ;  João Casalta  ( Radiation Oncologist );  Ana Geão  (Radiographer, Nuclear Medicine);  Charlotte Beardmore  (Radiographer, Radiotherapy).
Actions to use strengths to maximise opportunities Actions to use strengths to minimise threats To use MEDRAPET as a basis to formulate and implement a European standard for mandatory E&T IN RP courses and certification (face to face and online learning) based on consensus to meet the needs of the various professional groups (2) To combine experience and learnings from European radiation protection courses & state of the art research with training needs analysis to produce accredited online education programmes (including high quality authentic simulation) accessible to clinicians and researchers alike. Trainer needs to be addressed; including continuous education for the trainers (1) To use the recent SRAs in order to promote the inclusion of RP and RAM in all clinical research involving radiology, nuclear medicine and radiation oncology (2) To align the SRA for RP and RAM in the medical field with the EURATOM community (1) To develop strategies bringing together national authorities, educational institu- tions, professional societies, safety campaigns and manufacturers to create aware- ness for the need of harmonisation of procedures involving RAM (2) To use existing EU guidance documents on E&T in RP and RAM as a tool to engage/empower national professional societies of health professionals to achieve implementation of LLL programs in RP and RAM (3) To disseminate the outputs of E&T in RP and RAM elements of EU projects to all relevant stakeholders (4) To increase the cooperation between EURAMED and the existing European networks related to RP, as a strategy to create awareness amongst the stakeholders for the importance of RP and RAM (1) To allocate part of the financial support of the E&T in RP and RAM projects to promote and disseminate the results amongst the health professionals' communi- ties Existing structures and training (1) To systematically review previous recommendations and their impact & imple- mentation (engaging individuals with experience from previous programmes) RP training needs assessment (1) E&T dissemination/ & E&T model(s) development (1) To encourage collaboration between regulators, higher education institutes and professional societies to ensure existing regulations (EURATOM) regarding harmonisation/ education and training are implemented in member states and follow a consist-accreditation ent standard (2) To promote professional exchange between member states with different standards of RP (3) To use existing networks and collaborations to create a European-level accreditation / endorsement of E&T in RP, supported by regulators Financial Supports.
We present the first extended validation of a new SYNERGY global aerosol product (SY_2_AOD), which is based on synergistic use of data from the Ocean and Land Color Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR) sensors aboard the Copernicus Sentinel-3A (S3A) and Sentinel-3B (S3B) satellites. Validation covers period from 14 January 2020 to 30 September 2021. Several approaches, including statistical analysis, time series analysis, and comparison with similar aerosol products from the other spaceborne sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), were applied for validation and evaluation of S3A and S3B SY_2 aerosol products, including aerosol optical depth (AOD) provided at different wavelengths, AOD pixel-level uncertainties, finemode AOD, and Angström exponent.
Iterations on the product validation results with product developers, such as the round robin approach (Holzer-  Popp et al. , 2013) , are a good example of how communication between the validation team and product developers should be organised to better utilise validation results for improvement of product quality. In this paper we introduce global validation and evaluation results for the SYNERGY (SNY) aerosol optical depth (AOD) product, SY_2_AOD  (North and Heckel, 2019) , for the period from 14 January 2020 to 30 September 2021. The SY_2_AOD product is retrieved from spatially and temporally collocated data measured with two instruments: the Sea and Land Surface Temperature Radiometer (SLSTR) and the Ocean and Land Color Instrument (OLCI) aboard Sentinel-3 (S3A and S3B) satellites. The SYNERGY retrieval algorithm was originally developed for the retrieval of AOD from the Advanced Along-Track scanning Radiometer (AATSR) and MEdium-spectral Resolution Imaging Spectrometer (MERIS)  (North et al.
On the contrary, all satellite extractions included all quality flags and contextual parameters present in the Sentinel-3 operational products. Satellite extractions were created automatically for each station, at each overpass, and centred on the station location. They were then associated with relevant ground-based measurements when these data were available and validated. "Empty" matchups, i. e. when the whole satellite extraction is associated with a fill value for AOD, were not filtered out from the database, except in the case of operational issues with the Sentinel-3 instruments. As these fill values were mainly due to cloud contamination or aerosol retrieval failure, they may provide information about the performance of e. g. cloud screening in the SY_2 algorithm and were therefore relevant to validation objective.
Free access (upon subscription) to this matchup database has been provided on the ESA LAW web portal (https://law. acri-st. fr/home, last access: 10 January 2022). To explore the performance of different processors, four separate datasets were created and validated separately. The first dataset (called "all" in the following) consists of all available data, regardless of which processor was used. The second dataset ("dual") contains data retrieved with the dualview processor. The third ("singleN") and fourth ("singleO") datasets are created using the single-view processors applied to nadir or oblique views, respectively. The total number of matchups from dual, singleN, and singleO groups is higher than the total number of all matchups because in the 11 × 11 pixel area around the reference ground-based measurement there could have been pixels retrieved with different processors (e.
AERONET The AERONET is a federation of ground-based remote sensing aerosol networks (https://aeronet. gsfc. nasa. gov/, last access: 25 February 2022). For more than 25 years, AERONET has provided a long-term, continuous, and readily accessible public domain database of aerosol optical, microphysical, and radiative properties for aerosol research and characterisation, validation of satellite retrievals, and synergism with other databases. An extensive description of the AERONET sites, procedures, and data provided is available from the AERONET website and in  Holben et al. (1988)  and  Giles et al. (2019). Ground-based sun photometers directly observe the attenuation of solar radiation without interference from land surface reflections. They provide accurate measurements of AOD with uncertainty ∼ 0.
01-0. 02 (Eck et al. , 1999)  in the spectral range of 340-1640 nm. For the AOD validation, AERONET version 3 data  (Giles et al. , 2019)  -automated near-real-time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth (AOD) measurements -have been utilised. Version 3 AOD data are computed for three data quality levels: Level 1. 0 (unscreened), Level 1. 5 (cloudscreened and quality controlled), and Level 2. 0 (qualityassured). The Level 2. 0 AOD quality-assured dataset is now available within a month after post-field calibration, reducing the lag time from up to several months. Since AERONET is a network of ground-based sun photometers, and while some of the AERONET stations are in coastal land areas and on islands, the open ocean is poorly covered with AERONET.
AOD at 550 nm AERONET does not provide AOD at 550 nm (this dataset will be referred to in the following as aAOD 550 ). AERONET AOD 440 (aAOD 440 ) and the AERONET Angström exponent for 440 and 870 nm (aAE 440-870 ) are used to calculate aAOD 550 following the AOD spectral dependence feature (a power-law relationship; Angström, 1929). However, aAOD 440 is not measured at all AERONET stations. For those stations, aAOD for another wavelength (400 nm or 500 nm) has been used to interpolate aAOD to 550 nm. As shown in Fig. 1 , AERONET stations are not evenly distributed globally. For the study period, more than 85 % of the matchups were from the NH. Thus, most global results were strongly influenced by the results obtained for the NH. In the case that validation results are similar for the globe and the NH, results for the globe are not visualised.
χ 2 test for evaluation of the prognostic uncertainties The goodness of the predicted uncertainties was estimated with the χ2 test, as in Eq. (  3 ) χ2 = 1 N -1 N i=1 δ i , (3) with individual weighted deviation δ i described in Eq. (  4 ). δ i = syAOD i -aAOD i -mean(syAOD -aAOD) 2 PU 2 i + AU 2 (4) If χ2 ∼ 1, prognostic uncertainties describe the AOD error well. If χ2 1, PUs are strongly underestimated; if χ2 1, PUs are strongly overestimated. χ2 was calculated for the whole dataset and for different AOD bins to reveal if the goodness of the PU uncertainties is AOD-dependent. For the whole dataset, χ2 = 3. 1, which means that PUs are slightly underestimated. For the binned AOD, χ2 varies strongly (Fig. 14 ). For aAOD < 0. 4, which is ca. 90 % of all values, χ 2 fits into the interval [1.
8, 3. 2]. Thus, for most of the matchups, PU is only slightly underestimated. For AOD > 0. 4 PU underestimation is more pronounced. No significant dependence of δ i on AOD error or surface reflectance provided in the SY_2_AOD product has been revealed (Fig. S9 , Supplement). Though the number of matchups in the whole dataset is high (which provides confidence in χ2 test results), it was noticed that high δ i (up to 155) exists, which may bias the evaluation of the PU with χ2. To remove possible contribution of the outliers to the χ2 test results, cases with δ i > 10 (which make up less than 5 % of the total number of matchups) were removed from the analysis. For the dataset with the removed outliers, χ2 = 1. 2, which means that PUs describe the AOD error well. The influence of δ i outliers is more pronounced for AOD bins, in which number of matchups per bin is lower and thus the contribution of the outliers to the results is more expected.
Spatial intercomparison of seasonal and annual S3A and MODIS Terra AOD products Two types of monthly datasets have been created from SY_2_AOD and MODIS Terra daily data to study the differences at the monthly, seasonal, and annual (MSA) level. In the first monthly dataset, all pixels available in the SY_2_AOD and MODIS Terra daily products have been used to build a monthly aggregate for each instrument. Intercomparison of these "all-pixel" monthly aggregates (which are similar to the official monthly products provided for users) is important because it will help in e. g. understanding the difference in climate data records built from the provided monthly AOD products which include all available data. A second monthly dataset, the "collocated" product, has been aggregated using only collocated daily pixels.
Inter-   S9 ). comparison of collocated monthly aggregates shows the difference in monthly AOD based on differences in retrieval approaches. Annual AOD from all-pixel and collocated monthly datasets for SY_2_AOD and MODIS Terra, respectively, as well as the corresponding differences are shown in Fig. 30. Seasonal plots for collocated aggregates and the difference between them are shown in Fig. 31. Statistics for difference plots (area, land, and ocean means) have been calculated from pixel-to-pixel difference, but not as the difference between the AOD averaged over AOI, land, and ocean. Differences between SY_2_AOD and MODIS Terra MSA AOD exist in both the all-pixel and collocated datasets. For both datasets, SY_2 AOD averaged over AOI is higher for the whole area, as well as for land and ocean.
The difference is smoother for the all-pixel datasets. Even though difference plots show that regional offset between the two datasets is of-ten within GCOS requirements for AOD quality (0. 03) over ocean (SY_2 AOD is in general lower) and the whole AOI, the difference in AOD over land is often higher (up to 0. 11 as averaged over AOI in DJF, all-pixel dataset). Regional differences in seasonal AOD from the collocated dataset are considerably higher (Fig. 31 ). For all land subregions (except for "desert", JJA), S3A AOD is higher than MODIS AOD. The offset is highest for the "coast+dust" region in DJF and for the "Africa,BB" region in  SON (0. 18 and 0. 15, respectively). A general tendency of decreasing offset towards JJA months has been observed. However, though the offset is often high, time series for both products are within an overlap (grey area) of the standard deviations for individual products.
The highest negative offset (between 0. 05 and 0. 1) is observed in JJA in the desert region. Regional differences in seasonal AOD from the all-pixel dataset are less scattered (Fig. S14 , Supplement). For the open-ocean regions ("ocean, clean", and "ocean+dust"), S3A AOD is in general lower than MODIS AOD for all MSA; the exceptions are January and February in the ocean+dust region (Figures not shown). On the annual scale, the offset between S3A and MODIS AOD is -0. 02 for ocean+dust and -0. 03 for ocean and clean. AOD in the collocated dataset is higher compared to the all-pixel dataset for both S3A SY_2 and MODIS Terra. Comparing with all pixels and collocated, the SY_2 AOD product looks less smooth over northern Africa in DJF and MAM. Over land, overall performance has a much higher rms error of approximately 0.
Bright desert surfaces also have less stable retrieval, with land-ocean contrast suggesting that high values in dust plumes are underestimated over land. Further uncertainty is introduced by an error in a priori estimates of aerosol properties not retrieved, principally single-scattering albedo (SSA). It is clear that retrievals using dual view give higher quality by making use of more information to allow less reliance on surface spectral assumptions. Retrieval over land surface in the Northern Hemisphere shows generally higher retrieval error, including regions of boreal forest where we would ex-pect higher-quality retrieval due to the low surface signal. In some cases, this will be due to weak masking of snow and ice cover as well as the presence of retrievals made at high solar zenith angles (over 70 • ) often excluded in other aerosol datasets.
Conclusions and recommendations for future evolutionWe have presented the first validation of a new SYNERGY global aerosol product, derived from the data from the OLCI and SLSTR sensors aboard the Sentinel-3A and Sentinel-3B satellites. Combined, the two satellites provide close to daily global coverage and provide aerosol measurements with a latency of 2-3 d. In this study we have compared the aerosol product with ground-based photometer data from four networks: AERONET, SKYNET, SURFRAD, and MAN, as well as with MODIS combined Dark Target and Deep Blue algorithms. The aim of this study was to provide global characterisation of the current aerosol retrieval and to guide future algorithm development. Over ocean, the performance of SYNERGY-retrieved AOD is good and consistent with the reference MAN dataset (rms ∼ 0.
Acknowledgements. This research has been performed in the framework of the  ESA/Copernicus LAW  and  OPT-MPC  projects supported by the  EU Copernicus programme  (grant nos. 4000129877/20/I-BG  and  4000136252/21/I-Bgi , respectively). Financial support. This research has been supported by the  EU Copernicus program  (project  LAW , grant no. 4000129877/20/I-BG ; project  OPT-MPC , grant no. 4000136252/21/I-Bgi ) and  ESA. Review statement. This paper was edited by  Alexander Kokhanovsky  and reviewed by  Stefan Kinne  and two anonymous referees.