dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, periods of weak correlation might occur again, especially since it is uncertain how the sea-ice-to-snow relationship will change with stronger anthropogenic global warming, once the Arctic is ice-free in summer or the local warming is strong enough to override the counter-intuitive snow cover increase. Thus, further studies are needed to investigate the interplay between Arctic sea ice and continental snow distribution. Future experiments should take into account year-to-year variability and realistic distribution of snow cover if links to the stratosphere are to be examined. Data availability. The MERRA2 reanalysis data are publicly available at the NASA EARTHDATA repository (https://disc. gsfc. nasa. gov/datasets?keywords="MERRA-2"& page=1&source=Models%2FAnalysesMERRA-2, National Aero-nautics and Space Administration, 2017).
The ERA20C reanalysis data are publicly available at the ECMWF data repository (https: //apps. ecmwf. int/datasets/data/era20c-moda/levtype=sfc/type=an/, European Centre for Medium-Range Weather Forecast, 2016). The 20CRv2c reanalysis data are publicly available at the NOAA Earth System Research Laboratory repository (https://www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html, National Oceanic and Atmospheric Administration, 2015). The blocking algorithm is publicly available at https://github. com/marco-rohrer/TM2D (Rohrer, 2018)  Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-11-509-2020-supplement. Author contributions. MW devised the study, the main conceptual ideas and the proof outline. MR assisted with data availability and performed the blocking algorithm.
Acknowledgements. The Twentieth Century Reanalysis Project datasets are supported by the  U. S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE)  programme and the  Office of Biological and Environmental Research (BER) , and by the  National Oceanic and Atmospheric Administration Climate Program Office. The ECMWF 20th Century Reanalyses and model simulations are supported by the  EU   FP7  project  ERA-CLIM2. We thank  Morgan Gray  for editorial support. Financial support. This research has been supported by  Swiss National Science Foundation  (grant no. 143219 ). The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association. Review statement. This paper was edited by  Gabriele Messori  and reviewed by three anonymous referees.
ACKNOWLEDGEMENTS We are grateful to  S. Legendre  ( LEHNA, Lyon ) and  J. Sudre  ( ISEM, Montpellier ) for his preliminary work on the Palembert material and to all of the team of the  Cloup d'Aural and the Quercy research team (M. Godinot, MNHN , Paris; ISEM, Montpellier;  G. Escarguel , LEHNA, Lyon;  C. Bousquet ,  Cloup d'Aural ) for their work in the field. We thank  S. Jiquel  for her help in the Montpellier University's collections. We are also grateful to  S. Agret  and  L. Marivaux  for kindly welcoming us in their office to take measurements and photos of the material. Finally, we acknowledge the anonymous reviewers for their constructive comments. This work was financially supported by the  ANR  program  DEADENDER  ( ANR-18-CE02-0003-01 ) -P. I. M. J. Orliac. This is ISEM publication  2020-168.
seNorge The seNorge snow model component is a single layer snowpack model which simulates different snow-related variables using a degreeday approach, such as snow water equivalent, 24 h and 72 h new snow amounts, snow height, bulk snow density and the amount of liquid water in snowpack  (Saloranta, 2012). It requires either daily or 3 h (high resolution mode) mean air temperature and sum of precipitation. The model consists of two main modules, a SWE (Snow Water Equivalent) model for snowpack water balance and a snow compaction and density module used to convert SWE to SD (Snow Depth). The SWE model for snowpack water balance is run before the snow compaction and density module  (Saloranta, 2012). Each output is available on a 1 km grid covering Norway and large parts of Sweden at a temporal resolution of either 3 h or 24 h.
Slovenia The National Meteorological Service of Slovenia started using SURFEX/ISBA-Crocus in 2016. The 2016-2017 and 2017-2018 seasons were used for familiarization with the operation of SURFEX and evaluation. From the season 2018-2019 Crocus was used operationally. In Slovenia, daily gridded SURFEX/Crocus simulations are performed. The domain is a regular 1 km grid with 401 × 301 points centered on Slovenia (the same domain as Inca-SI nowcast that is use operationally). Two daily runs are performed, an analysis and forecast cycle, which differ in the data used to construct the forcing files and the length of the simulation. The execution of the model is implemented using ECMWF's ecFlow workflow package (see Fig. 2 ). The scripts for constructing forcing files and visualizations are written in R.
Firstly, if the model results are part of the information routinely used by the forecasters in their daily duties, then the model needs to be able to operate without interruption and continuously provide data to be visualized and analyzed. Adequate measures are to be taken to fix potential discontinuations of model chains, be it for intrinsic model errors or for external reasons such as diverse causes of failure of the information technology system hosting the model chain (operating system stability, network connection and power reliability, delay in input data access, failure of database requests etc. ). Breaks in the data flow provided to the forecasters can be prejudicial to their daily activities. Secondly and perhaps more importantly, because of the long term (weeks to months) memory of the snowpack and the fact that incremental changes of its status occur throughout the snow season, breaks in the data stream to the snowpack model itself could have adverse consequences not only for the period of time considered by the data gap but for the rest of the snow season unless appropriate mitigation is implemented.
Finding the right balance between accuracy and continuity of the simulations can be challenging, but is an inherent component of the design of the model chain. For example, Météo-France snowpack model chains are constantly (24/7) monitored by the technical personnel also in charge of monitoring NWP model chains, and can act upon the unavailability of expected products from the model chains, or the issuance of error diagnostics. Ideally, error diagnostics should be handled by the model chain itself and their status should trigger further automated trials of the failed tasks. In the Météo-France chain of models, the main source of failure is due to the temporary unavailability of either NWP forecasts or observation datasets, which are used by SAFRAN, and failure to access the database containing the initial state of the snowpack (from previous day simulations) necessary to restart the model the following day.
Fig. 14 , from  Horton et al. (2014) , shows an example of a map of surface hoar formation together with an estimate of the load of new snow upon it. However, it should be kept in mind that at high resolution, the representation of variables strongly influenced by the topography (e. g. , snow height) can be very noisy and difficult to interpret from a regional perspective. Fig. 15  shows an example of a map displaying the penetration of a ram sonde tube, indicative of the mechanical strength of surface snow, for the entire geographical domain of Slovenia. Fig. 16  shows an example of series of maps of the regional averaged avalanche danger level diagnosed by MEPRA for various dates in the French Alps, using the Synopsis software  (Coléou et al. , 2016).
Such an assessment could make it possible to derive recommendations which would benefit both the forecasters and the model developers in their respective tasks. worldwide community. CNRM/CEN is part of LabEx OSUG@2020. We thank Marie Dumont (CNRM/CEN) for feedback on the manuscript. This publication has benefitted from funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 730203. Open-access publication fees were covered by Météo-France. S. Horton was financially supported by Avalanche Canada and the NSERC IRC in Avalanche Risk Management at Simon Fraser University. Comments and suggestions from Karl Birkeland and one anonymous reviewer were useful for improving the original manuscript.
METHODS AND ANALYSIS Design and setting The proposed study will use a sequential exploratory mixed methods design, which involves a first phase of qualitative data collection and analysis followed by a second phase of quantitative data collection and analysis that builds on the previous phase (figure  1 ). 27 Thus, quantitative data and results will assist in the interpretation of qualitative findings. In phase 1, we will collect qualitative data from open-ended semi-structured interviews with older adults receiving home care services and their caregivers in Ontario. In phase 2, we will collect quantitative data from web-based surveys completed by interdisciplinary health and social care providers who provide home care services across different provinces (Quebec, Ontario, Alberta and British Columbia).
Qualitative and quantitative findings will subsequently be integrated to enhance our understanding of what is needed for SDM from the perspective of older adults, caregivers and care providers. We applied the following guidelines in the development of this protocol: (1) criteria reported in an assessment of the quality of mixed methods studies in health services research by O'Caithan et al  28  and (2) the Checklist for Reporting the Results of Internet E-Surveys (CHERRIES). 29 The criteria reported by O'Caithan et al was used for describing the design for our health services research, and how we will integrate qualitative and quantitative data between the two phases. The items on the CHERRIES checklist was used to design the online survey for phase 2 of the study, including considerations for developing and pretesting the survey, survey administration and response rates.
As ageing is not a uniform process across populations, participants in the older adult group will consist of individuals who self-identify as older adults with home care experience. This approach aims to include individuals who share a worldview as an older adult. Participants under 18 years of age, such as caregivers caring for their grandparents, are excluded from this study as they may have additional concerns that can be further explored in future studies. Our recruitment method includes: (1) sending recruitment notices through professional and patient networks Phase 1 (qualitative data) Semi-structured open ended interviews with 15 to 30 older adults (and caregivers) Qualitative data analysed using thematic analysis Initial coding done independently by two coders according to initial thematic map Collate codes into emergent themes Team meetings for consensus on codes, themes and categories Modified thematic map grounded in both the literature and data Phase 2 (quantitative data) Quantitative data collected from survey of 500 health and social care providers Descriptive statistics of (minimum, maximum, mean, SD) Group comparisons (e.
Data collection Data collection in phase 1 and phase 2 of the study will be guided by the following research questions: ► What health-related decisions are older adults receiving home care and their caregivers faced with at home? ► What do older adults receiving home care and their caregivers need for making better decisions at home? ► What do interdisciplinary health and social care providers who provide home care services to older adults need for better supporting their clients with making decisions? In phase 1, qualitative data will be collected from open-ended semi-structured interviews conducted with older adults who have received or are receiving home care services, and their caregivers. Adopting standardised questions based on the ODSF,  21  the interview guide (see online supplemental file 1) includes questions on what important and difficult decisions they face at home, what makes these decisions difficult, how they feel when making these decisions, and what they need for making better decisions.
All interviews will be recorded and transcribed verbatim. Interviews will be conducted at the participant's home, or an alternative location of his/her choosing. In phase 2, quantitative data will be collected from webbased surveys answered by health and social care providers who work at SE Health across provinces (Quebec, Ontario, Alberta and British Columbia). The survey (see online supplemental file 2) will include: (1) Section Aabout you; (2) Section B-your views on decisions facing older adults (and their caregivers) in home care settings; and (3) Section C-your views on the decision-making needs of older adults (and their caregivers) in home care settings. The web-based survey is expected to take about 5-10 min and can be completed on mobile devices or a computer. The survey will be pretested for readability Open and pilot tested in a small group of participants before launching more widely across the SE Health organisation.
Analysis Phase 1 qualitative data will be systematically analysed using thematic analysis, which is a method for identifying, analysing and reporting patterns (themes) within data. 43 -Vivo V. 12 Plus software will be used to support the analysis. Thematic analysis allows for the systematic coding of qualitative data according to a preliminary thematic map, which will be informed by rich concepts from the ODSF. 21 his approach is flexible to allow the thematic map to be expanded on with emergent concepts and themes using a transformative perspective. Our thematic analysis will follow five steps. 43 First, we will familiarise ourselves with the data by reading and re-reading the transcripts and noting down initial ideas. Second, the transcripts will be independently coded by two coders across the entire data set in a systematic fashion, collating data relevant to each code.
Third, we will collate codes into emergent themes, gathering all data relevant to each theme. We will discuss the codes and themes as a team and come to a consensus. Discrepancies will be discussed until a consensus is reached. Fourth, we will review the themes in relation to the coded extracts and the entire data set to generate a modified thematic map of the analysis that is grounded in both the literature and data. Finally, we will refine the specifics of each theme and generate a report on the thematic map with clear definitions of each theme. We will select vivid and compelling extracts, referring back to the analysis and literature to produce a report of the analysis. Phase 2 quantitative data collected from the web-based survey will be analysed using SPSS Statistics 27. Descriptive statistics will be used to analyse the survey data (eg, frequencies, weighted average, median, variance), as well as other statistical tools (eg, Mann-Witney test to analyse the ordinal data (5-point Likert scale) and t-tests for group comparisons).
Qualitative and quantitative findings will subsequently be integrated to report on the decisional needs of older adults receiving home care and their caregivers from multiple perspectives. Our final report will be shared with phase 1 participants who expressed an interest in receiving study findings for member checking to strengthen results. We will use multiple types of triangulation to enhance the reliability of our findings: (1) method triangulation: data collected from semi-structured interviews will be triangulated with data collected from online surveys; (2) investigator triangulation: data will be individually coded by two coders and discussed with our research team to mitigate the risks of bias that might come from the analysis of individual researchers and add breadth to the study; (3) theory triangulation: data will be initially coded using a thematic map informed by well-established concepts from the ODSF, which will be expanded on with emergent themes from a transformative perspective; and (4) data source triangulation: data collected from interdisciplinary health and social care providers will be used to triangulate data from older adults and their caregivers, and vice versa, to test and improve reliability of our findings.
Our results also bring new support to the phylogenetic insurance hypothesis  20  , with a positive effect of phylogenetic diversity on population asynchrony found for bat and butterfly communities. This suggests that for these taxa, related species tend to share the traits involved in their response to environmental variations and perturbations  30  , and that we can use phylogeny as a proxy to assess the dynamical response of their populations to such environmental variations and perturbations. Diversity loss effects through population stability. The effects of species diversity and phylogenetic diversity on the weighted mean population stability are weaker and more contrasted among the three taxa (Table  1 , Fig. 4 , Supplementary Figs. 1 2 3 ),   1  Datasets. a Spatial distribution of the monitored bat, bird and butterfly communities across France.
However, population and community temporal variability are known to increase with the considered time scale  [40] [41] [42]  and as such, our estimates of temporal variability might underestimate the full variability of the studied communities. While this should not affect the effects of landscape composition we found, and indeed our results are robust when compared with analyses on two subsets of our datasets with different time series durations (see "Methods" and Supplementary Figs. 7 8 9 ), longer time series would be required to estimate the full variability of the studied communities. Another limitation of our study is that we assessed habitat degradation at the landscape scale and did not account for local conditions, such as management practices, that could also affect community variability.
These circuits were chosen to be close to the volunteer residency, with low-traffic roads for security and representative of the different land-cover types in the area. These circuits are divided into ten 2-km transects where bats are recorded, separated by 1km road portions where recording is not carried out. Surveys start 30 min after sunset and last approximately 1. 5 h. Weather conditions have to be suitable for the survey to be carried out: no rain, wind speed below 7 m/s, temperature above 12 °C. Bat activity is recorded through echolocation calls with ultrasound detectors connected to a digital recorder. Volunteers were trained to classify echolocation calls to the most accurate taxonomic level using Syrinx 2. 6  45. Data validation was done by program coordinators at the Muséum national d'Histoire naturelle for recordings with uncertain identification.
Following previous works (e. g. ,  46  ), the abundance of each bat species in a 2-km road transect was defined as the number of bat pass per species (a bat pass corresponds to a trigger of the bat detector in time expansion). We used data from transects surveyed between June 15th and July 31st (seasonal bat activity peak) from four to 6 years between 2006 and 2012. This represented a total of 152 local bat communities, where a total of 7 species were recorded (Supplementary Table  1 ). For bird communities, we used data from the French Breeding Bird Survey [http://www. vigie-plume. fr/]  47 , a monitoring program relying on keen birdwatchers to count birds annually in a given plot. Plots are squares of 2 × 2 km 2 randomly selected by the national coordinator, within which the surveyor places 10 points separated by at least 300 m, in order to cover all the habitats present in the plot.
Each plot is surveyed twice a year, the first session (to record nonmigrant birds and short distance migrants) between April 1st and May 8th, the second (to record trans-saharian migrants) between May 9th and June 30th, with at least 4 weeks between both sessions. Surveying dates must be the same (±5 days) every year, and counting takes place in the morning, starting 30 min after sunrise, with points always visited in the same order. At each point, the volunteer spends 5 min recording all birds seen or heard. Following previous work (e. g. )  47  , yearly species abundance at a site was calculated as the sum of the maximum number of individuals detected per point over the two sessions. For this study, plots surveyed at least eight years between 2001 and 2017 were selected, representing 269 local bird communities and 75 common species for which the amount of data available allows an accurate estimation of population dynamic (Supplementary Table  1 ).
For butterflies, we used data from the French garden butterfly observatory [http://www. vigienature. fr/fr/operation-papillons]  48. Participants identify and count Lepidoptera in their own garden, from a closed list of 28 common species or species groups (27 butterflies and one common diurnal moth, Macroglossum stellatarum). Since some of the taxa targeted by this scheme group several lookalike species (species groups), we only kept the 13 butterflies and 1 moth identified at species level for our analyses (Supplementary Table  1 ). The temporal variability of the butterfly community restricted to these 14 species reflects that of the community including the abundance of the species groups (Supplementary Fig. 6 ). For each species, abundances are recorded monthly, as the maximum number of butterflies seen simultaneously during the month.
Counting takes place from March to October, but for this study, gardens that had been monitored in July at least seven years between 2007 and 2018 were selected, except for the year 2014 , and butterfly (c) communities. Urban gradient and agricultural intensity gradient are independent variables while species diversity, phylogenetic diversity, population stability, population asynchrony, and community stability are dependent variables. Black and red arrows represent positive and negative relationships, respectively, with relationship strength depicted by arrow width. Solid and gray dashed arrows represent significant relationships and nonsignificant relationships, respectively (Supplementary Table  3 , see "Methods"). Standardized coefficients are shown next to the arrow basis. because of a crash of the database server, representing 130 local butterfly communities.
Our data selection presented and analyzed in the main text totalized 551 sites (Dataset 1), with variations in the number of years across sites and taxa. This leads to 65, 7, and 80 sites with time series of, respectively 4-6 years for bats; 3, 14, 37, 32, 29, 33, 49, 30, 30, and 12 sites with time series of, respectively, 8-17 years for birds; and 34, 12, 10, 17, and 57 sites with time series of, respectively, 7-11 years for butterflies. To test the robustness of our results to the presence of gaps in the time series, we also ran our analyses on two subsets of the dataset that included only time series of the same duration and with no missing year. The first subset was restricted to the longest fully overlapping observation period with no gap common to all sites, leading to times series of 4, 8, and 7 years for bats, butterflies and birds, respectively (Dataset 2).