dataesr/scientific-paragraphs-categorization huggingface dataset:
Initial studies had a more economic purpose but over recent decades, TU data have been used to describe the distribution and correlates of health-enhancing patterns of human time use. These studies require large multicountry population data sets, such as the harmonised Multinational Time Use Study hosted at the University of Oxford. TU data are used in physical activity research, as they provide information across the 24-h day, that can be examined as time spent sleeping, sitting/standing/light activity, and time spent in moderate-vigorous activities. TU data are also used for sleep research, examining eating and dietary patterns, exploring geographic distributions in time use behaviours, examining mental health and subjective wellbeing, and examining these data over time. The key methodological challenge has been the development of harmonised methods, so population TU data sets can be compared within and between-countries and over time.
Diarists self-report their actions at specified periods, often 15 min epochs, into a TUD that reflects and codes 24 h for that individual. Constructing such accounts is rendered problematical by recall failures, the likelihood of multiple concurrent activities (e. g. reading and eating), and of competing alternative descriptions of activities (e. g. "walking" vs "travel to work"). So optimal time use data would collect information of multiple descriptive characteristics of each successive "event", where the "event" itself is defined as the period during which all of these characteristics remain unchanged. Originally TUDs contained a main activity field, several concurrent or simultaneous activity fields, a location field and multiple "others co-present" fields  [3]. Current practice now often has a field about device use, sometimes adds one or more affect fields (estimating levels of enjoyment, stress or others), as well as fields containing measurements in real time taken from instruments (such as accelerometers or global position system [GPS] devices) carried by the diarist.
The Soviet Russian economist Stanislav Strumlin published a time budget account of Russian workers in 1925  [8]. And almost simultaneously the United States (US) Department of Agriculture (USDA) started to collect time diaries from farm, town, and later elite educated "college", women  [9] , providing the earliest time diary dataset whose original individual-level data survives for use by modern researchers  [10]. The Soviet interest in time allocations was driven by the idea of a Marxist central planned economy believing the worth or value of objects to be determined the necessary labour time it embodies. By the 1920s the Soviets had become attracted to the concept of Scientific Management, pioneered in the US and elsewhere by F. W. Taylor. He called his technique, using stop-watches to rationalize the process of production, 'time studies'.
The 12-country dataset that emerged from the Szalai study, one of the first cross-national comparative studies on any subject available to social scientists, popularised a design of time diary studies that continues, with minor variations, to the present time. This common underlying design means that ex-post harmonisation of the micro-level data of all the subsequent time-diary based studies whose samples have survived to the present time is, requires post-hoc harmonisation using already collected data. When surveys do not have identical response categories, an alternative is to find a common denominator by collapsing categories. In relation to time use surveys, most national activity classification are derived from schema pioneered by the Szalai multinational study in 1965  [3] , so this is a feasible task.
And the next substantial exercise in ex-ante harmonised cross-national comparative time diary research, organised in the planning of TUD collections by Eurostat from the mid-1990s, started from the same Szalai-based model  [15, 16] ; the two tranches of national data  (1999-2006 and 2009-2015)  collected so far include all the larger European Union countries. The US started to collect the American Time Use Survey as the seventh wave of the Current Population Survey in 2003. The objectives of this survey changed during the twentieth Century; the surveys were initially focused on patterns of time spent in work and family life, and using these surveys to assess one dimension of economic outputs. A particular focus was the changing time allocations for women at work and at home. Time use surveys captured all activities in a day and offered a window into the emerging problem of reconciling family responsibilities and seeking a career.
This inherent flexibility of TUD has led to its recent use in assessing health and lifestyle-related dimensions, including active time and sedentary time. The Multinational Time Use Study (MTUS), maintained by the Centre for Time Use Research, University of Oxford, is by far the largest available collection of comparative and historical time use materials, with 1. 2 M days from 85 surveys from 26 countries, all harmonised ex post, most of the data freely downloadable for use by academic researchers (http://www. timeuse. org/ mtus)  [17]. The recent year TUD samples for 25 countries are shown in Table  1. TUDs have been assessed for validity in relation to different criteria, and generally show good measurement properties. With respect to physical activity measurement, TUDs have been compared to objective accelerometry measures, and show better concordance than usual self-report physical activity questionnaires  [2].
Conclusions Maintaining TUDs is a worthwhile investment for health-related research, to maintain longterm cross-national, comparable trend information. Further, TUD also provides trend information on aspects of social inequalities, another fundamental precept of public health research. It may even be possible to compare the population effects longterm public policy interventions using TUDs, and hence use these surveys in the evaluation of some public health programs. Technology will complement self-report TUDs, and provide additional data and new ways of collecting time use information. These new methods may facilitate inexpensive larger scale data collections, but may also pose new challenges, such as maintaining representativeness in sampling, and preserving backwards comparability with the decades of TUDs using traditional survey methods.
The use of TUD in low-middle income countries is relatively rare, but could be particularly useful in monitoring rapid trends in urbanisation and industrialisation that lead to increased sitting time and reduced total physical activity; this is a potential benefit of TUD in more countries, especially those undergoing epidemiological and demographic transition. Globally, TUD remains as a valuable, and under-utilised, public health data resource, which can help to explain the present, and track trends over many decades, in a way that is unique among any population measurements.
This paper describes in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions modelling at ten sites. The long-term datasets (one maritime, one arctic, three boreal and five mid-latitude alpine) are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project. Periods covered by the in situ data vary between seven and twenty years of hourly meteorological data, with evaluation data 35 (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. 30-year (1980-2010)  time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected.
Although applied to all sites, the bias corrections are particularly important for mountain sites that are hundreds of meters higher 40 than the grid elevations; as a result, uncorrected air temperatures are too high and snowfall amounts are too low in comparison with in situ measurements. The discussion considers the importance of data sharing to the identification of errors and how the publication of these datasets contributes to good practice, consistency and reproducibility in Geosciences. Supplementary material provides information on instrumentation, an estimate of the percentages of missing values, and gap-filling 45 methods at each site. It is hoped that these datasets will be used as benchmarks for future model development and that their ease of use and availability will help model developers quantify model uncertainties and reduce model errors.
Introduction In the past decade, several long-term datasets aimed at providing high quality continuous 60 meteorological and evaluation data for cold regions modelling have been published (Table  1 ). The importance of such datasets is twofold. Their primary value is scientific: they help us to understand key surface processes by enabling the development and evaluation of existing and new geophysical models for climate research and forecasting. The second, perhaps less obvious, value of having multiple long-term datasets is for meta-research; the smaller the studies or sample size, the less likely 65 research findings are to be true  (Ioannidis, 2005). In a snow modelling context, this is corroborated by  Rutter et al. (2009)  who found low correlations in performance statistics for the same snow models but in different years.
Here, we describe ten long-term datasets (Table  1 ) from reference sites chosen to force and to evaluate models participating in the Earth System Model-Snow Model Intercomparison Project (ESM-70 SnowMIP)  (Krinner et al. , 2018) , an international coordinated modelling effort that investigates snow schemes. ESM-SnowMIP is closely aligned with the Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP;  van den Hurk et al. 2016) , which is a contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) including global offline land model experiments with meteorological forcing data provided by phase 3 of the Global Soil Wetness Project (GSWP3;  Kim, 75 2017). Two meteorological datasets are described for each site: one compiled from on-site measurements, the other derived from GSWP3.
Previous iterations of SnowMIP have provided 19 siteyears of data from four sites in SnowMIP1  (Essery and Etchevers 2004 ) and 9 site-years of data from five sites in SnowMIP2  (Rutter et al. , 2009) ; ESM-SnowMIP totals 136-site years of in situ data from ten sites and 300 site-years derived from GSWP3. 80 Measurement details at five of the sites have been described in dedicated publications within the last eight years. The other five sites are partially described in a number of publications which, combined, give a broad but not comprehensive overview of the data. All of the in situ measurements and the GSWP3 data are freely available either on the web or on request but, previously, post-processing would have been required to homogenize the in situ datasets compiled by different teams or to 85 downscale the reanalyses.
This situation causes two major issues. Firstly, different modelling teams are likely to apply different post-processing methods, leading to numerous versions of the same dataset being used for scientific studies. Secondly, although time spent identifying and processing data has never been quantified in scientific literature to our knowledge, it is a well-known but underacknowledged time consuming task for modelers. 90 The aim of this collaborative work is to provide easy-to-use, quality-controlled data in a format adopted by the climate modelling community to facilitate consistency, continuity and reproducibility in snow research  (Menard and Essery, 2019). As such, it complies with efforts in geosciences to foster best practices on data accessibility and documentation  (Gil et al. , 2016).
The seven teams who collated the in situ datasets have provided updates since previous publications and details about 95 instrumentation, gaps in the original data and methods for gap filling. Such additions are first steps towards being able to quantify uncertainty in observed data, without which "meaningful evaluation of a model is impossible"  (Clark et al. , 2011). Similarities and differences between sites are discussed in individual measurement sections. Table  1 : Data ownership and reference papers for the sites. Asterisks denote dedicated data 100 description papers; the others are modelling papers in which a short description of a site is included.
Wind speed Wind speeds provided in the datasets are measured at variable heights but were normalised to 10 m height assuming a logarithmic wind profile for Fig. 1-g   2012 ), all trees sheltering the north side of the experimental area were cut in summer 1999; mean wind speed at 10 m height was 1 m s -1 prior to the event but 1. 26 m s -1 afterwards. Nevertheless, a Mann-Kendall (MK) test shows a 225 significant increasing trend in wind speed from 1999 onwards despite the tree growth mentioned in  Lejeune et al. (2018). Average wind speed at the exposed Senator Beck is the highest of all the sites and is almost four times more than at the nearby sheltered Swamp Angel. MK shows a significant decreasing trend in wind speed at Reynolds Mountain East and increasing trends at the Senator Beck basin sites.
235 Humidity is measured at all of the sites except Weissfluhjoch using capacitive sensors. These sensors respond to changes in relative humidity  (Anderson 1995) , but vapour fluxes in models are driven by specific humidity gradients. Conversion is therefore required from relative to specific humidity. At temperatures below 0°C, there are two possible definitions of relative humidity because of the different saturation vapour pressures over water and ice. Sensors calibrated following the WMO 240 convention of reporting relative humidity with respect to water at all temperatures are used at most of the sites. The consequences of this choice (shown in Fig. 4 ) are not very significant at warmer sites such as Col de Porte or Sapporo but are clear in data from colder sites such as Old Jack Pine and Swamp Angel; relative humidity is never observed much above the ice saturation point for a particular temperature.
A chilled mirror dew point hygrometer is used at Weissfluhjoch, and reported relative 245 humidity can reach 100% or higher even at sub-zero temperatures. In homogenizing the datasets,   Although automatic sensors are increasingly being used to measure SWE, the most reliable methods to obtain snow mass are still manual  (Pirazzini et al. , 2018). They work by weighing snow mass in samplers of known volume or area, such as small cutters in snow pits or tubes to extract vertical snow 260 cores. Nevertheless, such measurements are prone to errors: wet snow can stick to instruments, manual measurements can never be replicated in the same place because they are destructive, and subjectivity and skill do play a part so consistency can be hard to achieve if multiple people collect the data. Replicate measurements of SWE and snow depth can be used to estimate uncertainty, which can be 265 caused by measurement errors, spatial variability or a combination of both.
, 2011). Model simulations suggest that impurities reduce albedo at Sapporo by 0. 05 in winter and 0. 18 during melt  (Niwano et al. , 2012). Information about errors and uncertainties in albedo due to incoming radiation measurements is in Section 2. 1. 2. Successive IPCC reports have noted that Earth System Models often underestimate soil temperatures at high latitudes  (Randall et al. , 2007; Flato et al. , 2013; Koven et al. , 2013). This has implications for 315 assessing the permafrost carbon feedback, i. e. the amplification of surface warming from carbon emissions released by thawing permafrost. Long term datasets are therefore essential to evaluate model performance and to improve model representations of soil / atmosphere interactions.
Discussion A number of errors were identified in the datasets in the course of the study. Firstly, we noted that 395 snowfall at the Old Aspen was much lower than at the Old Black Spruce and Old Jack Pine during the by decomposing time series: trend analyses showed an increase in wind speed at the Senator Beck basin sites from October 2012 to the end of the dataset in October 2015. Both sites measure wind 400 speed at two heights; the lower wind speed measurements were used for the first seventeen years of the dataset, but the upper wind speed was accidentally used for the last three years. At the same sites, instrument re-calibration led to a small but statistically significant increasing trend in longwave radiation. These errors were included in the preliminary ESM-SnowMIP results shown in  Krinner et al.
(2018) ; erroneous years will either be neglected in future publications or models will be forced with 405 the corrected datasets which are published alongside this paper (see Section 6). While unfortunate, such errors are symptomatic of long-term data sets for which consistent maintenance and data collection is problematic. Firstly, by definition, long-term monitoring stations might have been installed before metadata were kept electronically (and before the word "metadata" was invented in 1983; Merriam-Webster, 2018) and when information about changes of instruments 410 or re-calibrations were in notebooks which might never have been digitised, have now been lost or never even existed. Equally, improvements in data storage capacities mean that temporal sampling intervals are shorter than they were.
For example, measurements at Reynolds Mountain East were initially made every 15 minutes and averaged to hourly values; currently, 10 second samples and 5 minutes averages are aggregated to hourly values for most variables. Such factors are known to affect 415 the values of meteorological variables  (Hupet and Vanclooster, 2001)  but it is beyond the scope of this study to attempt to quantify their contributions to errors or variations in the datasets. Secondly, immediate use of the data allows instrument malfunctions to be identified quickly. For example, a power supply failure was not identified at Sodankylä for 52 days in September and October 2011 because data were being collected but not used; more frequent QC checks are now in place. Thirdly, 420 long-term monitoring stations are susceptible to funding cycles and to changes in climate change policies by successive governments.
For example, the BERMS sites, which were established in 1994, had the most frequent site visits from 2001 to 2008, but changing priorities led to less frequent snow surveys after 2008 with only one in the 2009/2010 snow season. Finally, while automated QC protocols are in place, some checks require a subjective interpretation of the data and can therefore 425 depend on just one person to identify errors due to malfunction, snow deposition on instruments etc. Reliance on subjectivity or local knowledge -which in some cases is advocated as mentioned in Section 2. 1. 3 to choose the best method to correct undercatch in precipitation -diminishes the likelihood of the dataset being reproducible. In a discipline like geoscience where uncertainties and errors are required to be quantifiable, it is important to acknowledge that subjectivity is not.
Conclusion It is hoped that one of the legacies of ESM-SnowMIP will be for the datasets presented in this paper to be used as benchmarks for model development and to facilitate improvements in snow modelling. 455 Cold region processes have been a major source of uncertainties in previous IPCC reports. The sparsity of long-term high quality datasets in cold regions in the past may have contributed to this if one considers that ESMs are run globally but their snow schemes are generally evaluated at a small number of sites; the first iteration of SnowMIP  (Etchevers et al. , 2002)  sixteen years ago included only one long-term (15-year) dataset and three short-term (less than two snow seasons) ones. Meta-460 research argues that it is misleading to emphasize statistically significant findings of any single team; what matters instead is the totality of the evidence  (Ioannidis, 2005).
At Reynolds Mountain East, longwave radiation measurements started in 2002. For consistency across the RME dataset, which Earth Syst. Sci. Data Discuss. , https://doi. org/10. 5194/essd-2019for journal Earth Syst. Sci. Data Discussion started: 4 February 2019 c Author(s) 2019. CC BY 4. 0 License. starts in 1988, all longwave radiation is modelled but measured data are used to provide information on seasonal and diurnal variations (e. g. cloud cover, turbidity, canopy and terrain exposure 185 conditions). Details of the methods used to model  are in Reba et al. (2011).
Acknowledgments 480 The authors would like to thank all the staff and students who have collected the data presented in this paper over the years. Work by CM and RE was supported by  NERC  grant  NE/P011926/1. CNRM/CEN is part of  Labex OSUG@2020  ( ANR10 LABX56 ). CDP is part of  Observatoire des Sciences de l'Univers de Grenoble (OSUG) ,  Observation pour l'Experimentation et la Recherche en Environnement CryObsClim and Systemes d'Observation et d'Experimentation au long terme pour la 485 Recherche en Environnement  des glaciers, GlacioClim. CDP contributes to OZCAR ( Observatoires de la Zone Critique Applications et Recherches ), one of the  French components of the eLTER European Research Infrastructure (International Long-term Ecological Research Networks ). It is also labeled as a member of the  World Meteorological Observation Global Cryospheric Watch Cryonet network and of the INARCH network.
In order to simplify the analysis, we made the assumption that these four epigenetic pathways exert their function mainly as repressors even if, for example, HDACs can also act as activators in some specific cases  [17]. All the mentioned epigenetic pathways can interact. For instance, there is evidence of an interaction between HDAC and PRC1 (and possibly PRC2)  [18]. Notwithstanding their importance, the number of experimentally validated miRs involved in regulatory circuitries with the above-mentioned epi-Rs, from here on referred to as epi-miRs, is still very small, their identification episodic, and no attempt was performed up to now for a systematic search. The main goal of this paper is to fill this gap. We propose a simple protocol to identify candidate epi-miRs and study their mechanism of action combining gene expression datasets coming from experiments of overexpression of a given miR and data of knockdown, knockout, drug-mediated downregulation or chromatin immunoprecipitation (ChIP) experiments of a given epi-R (from here on referred to as epi-R ablation).
GSEA gene sets Gene sets were downloaded from the MSigDB database (v5. 2 updated October 2016) [22,23], which contains gene sets collected from various sources, from on-line pathway databases to PubMed publications. We selected a panel of gene sets broadly related to epigenetic pathways as discussed in the main text. We chose, when possible, multiple sets for a specific component, spanning over different tissues or experimental conditions. We ended up with a panel of 43 sets, summarized in Supplementary File 1. These sets can be divided into two main classes: results of ChIP experiments, whose entries allow to directly identify putative targets of the selected factor; results of gene expression experiments after knockdown, knockout or drug-mediated downregulation a particular epi-R. In this case, we downloaded separately both the upregulated and the downregulated genes.