dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the CS-25  [17]  defines certifications specifications and associated means of compliances for aircraft systems. Following these means of compliances, manufacturers show to the certification authorities that they developed and deployed systems that conform to these specifications and standard. A non-compliance leads to a new iteration of the UCD process or of the SCD process, and then to a new iteration of the RCD process in order to verify that the compliance issue is solved. For example, the CS-25 specifies that aircraft systems have "to be designed so that qualified flight-crew members trained in its use can safely perform their tasks associated with its intended function"  [17]. Then, in order to be accepted during the RCD process, each identified user tasks (identified during the UCD process) must match a system function (defined during the SCD process with architecture and data models) for the command and control system design.
1) and used this data to derive an architecture from DSCU (5. 2). At this point, we realized that the wording of OQCR states and attributes could be refined for each components of the architecture (5. 2). A Flight Warning System expert then joined the project to help us with step 5. 3. The Flight Warning System expert contributed largely to the integration step (5. 4). We had several meetings with various stakeholders to validate the outcome of each step. After each review, we recorded a list of modifications and amendments of the architectures, models and presentation layouts. We then validated the new versions of the architectures, the models and the presentation layouts with engineers and experts before pursuing the project at a larger scale. This section presents an extract of the application of the SCD process to the aircraft system: Engines.
Example of the Application of the SCD Process to the Aircraft Engines Data Collection (step 5. 1 in the Fig. 3 ). The documents gathered to understand the functioning and the use of aircraft engines are: • the specification of the engines (sections concerning the behaviour, envelope, services and needed resources) in the FCOM (Flight Crew Operation Manual) [2], • the list of possible alarms for the engines and their associated recovery procedures (FCOM too), • the usage instructions with associated C&C interfaces screenshots in the FCOM and the FCTM (Flight Crew Training Manual). A screenshot of a possible state of the extant Command and Control (C&C) interface for engines in an A350-900 cockpit is presented in Fig. 4. The information presented is related to "ALL ENG FLAME OUT" (both engines stopped running) alarm.
5 ). During the analysis process, we found that the engines may be in an unknown state when they have not been turned on yet. Indeed, we cannot know its quality of operation state. In the same way, in case of a data update failure from the systems to the C&C interface, the whole state of the system is unknown. In consequence, we customized OQCR description by adding a value "UNKNOWN" that applies to each OQCR state descriptor (O and Q) and each attribute (C and R). Unitary System Processing. (step 5. 3 in Fig. 3 ) Unitary Alarms Identification. The ENGines documentation contains all the alarms that may occur with this system. The FCOM indicates 60 possible alarms for the engines  [2]. We present here two examples assignment of alarms to the components of the DSCU architecture: the caution alarm "THRUST LOCKED" and the warning alarm "ALL ENG FLAME OUT".
The formulation is the following: "Can it be said of a demonstrator (ambiguous object) who. (followed by one of the 10 items retained in its negative form which corresponds to the first negative) that he or she is a Yellow Vest (object of social representation studied)"? We chose a scale of response in 5 points ranging from 1 (He or she is certainly not a Yellow Vest ) to 5 (He or she is very certainly a Yellow Vest) with a median response (I do not know). If the respondent does not agree it is the second negative of the model. The results lead to levels of refutation; the highest confirm the centrality of the elements. The data was gathered through a campaign of messages via social networks Facebook. The questionnaire posted was carried out by means of the application of Google Form.
Three infrared passive reflecting markers were placed in specified anatomical location using bone palpation: index and thumbnails, styloid radial of the wrist. After recording and 3D reconstruction, the position of each marker was filtered with a Butterworth low-band pass filter, with a cut-off frequency of 6 Hz. Then, from the markers' spatial position, movement parameters were computed using a homemade software Handimain developed by Inserm ImpAct engineers. Thus, movement initiation, i. e. , Reaction Time (RT) and reaching parameters were computed: Time Task (TT) to perform the movement (TT = RT + MT), i. e. , the time between go-signal and the end of the movement i. e. , the glass grasp, Movement Time (MT), Acceleration Phase (AP), and Deceleration Phase (DP). For the grasping parameters, Maximal Grip Aperture (MGA), Time to Maximal Grip Aperture (TMGA) and Finger Closure Time (FCT = MT -TMGA) were also computed.
These movement parameters were determined in a semi-automatic procedure with trial-by-trial validation by one expert experimenter (PR). Statistical analysis was conducted with Statistica ® 13 using repeated measure ANOVA with two main factor, group (thirsty or control) and glass (water or candle). Unilateral t tests were performed as previous data showed that thirsty subject reacted faster than controls  (Veling and Aarts, 2011). Additionally, according to previous results  (Veling and Aarts, 2011) , it could be postulated that motor inhibition occurring in no-go trial spread to the following trial. To test this specific hypothesis planned comparisons were used to compare inhibition effect by candle no-go or water no-go trials on the following go trials with candle glasses, both in the control and the thirsty groups.
Stimuli and design We selected 180 five-letter target-flanker word pairs from the French Lexicon Project database  (Ferrand et al. , 2010). The targets had a mean frequency of 3. 86 ZipF  (van Heuven, Mandera, Keuleers, & Brysbaert, 2014)  and were specifically chosen such that if a critical letter was replaced with a different letter drawn from the corresponding flanker word (referred to as the migration flanker), a new word could be formed, referred to as the illusory word (e. g. , the replacing the second letter in the target word farce with the second letter from the flanker word folie produces the illusory word force). Illusory words were therefore orthographic neighbors of the target words. Control flanker words were chosen so that no possible combination of letter migrations could result in an existing five-letter French word.
e. , five-letter words orthographically similar to the target word), blank responses, or completely different five-letter words. A detailed break-down of these percentages is shown in Table  2. We used generalized linear mixed models to analyze differences in error rates across conditions, with participants and items as crossed random effects  (Baayen, Davidson, & Bates, 2008; Barr, Levy, Scheepers, & Tily, 2013). This was done using the glmer function from the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015). We report regression coefficients (b), standard errors (SE) and z values. Fixed effects were deemed reliable if |z| > 1. 96  (Baayen, 2008). All analyses were done with the Rstudio (Version 3. 4. 2) statistical computing environment. We focus on letter migration errors and only report other effects when significant.
Inventory of policy-relevant environmental citizen science projects We used a three-step approach to identify 503 environmental citizen science projects of policy relevance in a repeatable way (Figure  2 ). We launched an EU-wide Internet survey, reviewed the databases of EU-funded projects (FP7, Horizon 2020, COST, LIFE) and performed a desk study using the results from recent systematic reviews of citizen science projects  (Chandler et al. 2017; Fritz, Fonte, and See 2017; Pocock et al. 2017 ) and surveying relevant citizen science directories. Only those projects with activities in Europe were retained. This survey does not claim to be exhaustive, but rather to present an ample and representative sample of the diversity of environmental citizen science initiatives ongoing in Europe at the time of this study.
Following Kieslinger et al. (  2017 ), a process-based evaluation was used to describe some of the operational strengths and weaknesses of the projects. Some of the key processbased criteria considered include, for instance, the levels of citizen engagement, support measures and training, the scientific grounding of the project, its data quality control and validation processes, and its endorsement by key stakeholders. The socio-economic dimension was assessed at this level in terms of the project's governance and funding structure. An outcome-based evaluation along the citizen-scientist, scientific, and policy dimensions was then performed by running four stepwise general linear regressions using the package MASS in R version 3. 5. 1 (R Core Team 2018). The response variables were chosen as follows: • Citizen scientist participation level was assessed by using the total number of participants in the project since its inception, as reported by the survey respondents.
• Scientific impact was assessed through the number of peer-reviewed publications from each citizen science project. Selected keywords were used to uniquely identify the projects, and all results from a Google Scholar search were then screened to identify relevant peer-reviewed, scientific publications (excluding book and thesis chapters) that either used project data or cited the project research. • Policy impact was assessed through two distinct response variables: The likelihood that the data from the citizen science project were effectively used for policy, as justified by adequate evidence from the respondents, and the number of phases in the policy process that the project contributed to (e. g. , contribution to policy formulation or data for policy evaluation). The explanatory variables included intrinsic characteristics of the projects, such as the age of the project (in years), its spatial extent (number of countries in which data were collected), the total number of data records (since the inception of the project), the number of staff members, and the main category of the project.
Scientific dimension All case studies adhered to fairly good scientific standards (Figure  5d, 5e, 5f ). Almost all projects provided metadata (91% of projects), had a fully disclosed and reproducible methodology (Figure  5d ), and involved some form of quality assurance (Figure  5e ). But we found that while 55% of projects were willing to share their data with the public, more than 35% of projects included some restrictions, and 9% did not provide any public access at all (Figure  5f ). The data produced by the case studies were widely used by scientists. According to the survey respondents, these data were used by the scientific community in 87% of projects (BIO Innovation 2018). The independent survey of peer-reviewed publications showed that the data from 82% of projects were used by scientists or were referred to as examples of citizen science good practice.
Socio-economic and policy dimensions Looking at the wider socio-economic conditions for successful citizen science projects, we found that most case studies were led by non-governmental and academic organisations, but seldom were any led by government agencies or the private sector (Figure  5g ). Projects differed in their funding structure, with the majority of projects receiving their main financial support from EU or governmental institutions, but funding from the private sector was negligible (Figure  5h ). Academia-led projects were mostly funded by the European Commission or national governments (47%). In contrast, NGO-led projects were more likely to get a large part of their funding from NGOs or alternative private funding sources (e. g. , donations, private companies). They tended to be medium-sized initiatives, employing two to five staff members and with broad stakeholder endorsement.
This is while the EU environmental policy agenda until 2020 focuses on issues such as green growth, the preservation of natural resources, and safeguarding citizens from environmentrelated pressure (EU-Ares 2016). Citizen science projects related to natural resources (air, water, land), and even more the efficient use of these resources remain a minority, and projects related to risks to health and wellbeing were barely represented in the inventory. The case studies showcase three pathways through which citizen science projects can achieve policy relevance. Projects that collect large amounts of data over broad spatio-temporal scales, such as Artportalen, the Pan-European Common Bird Monitoring Scheme, and Phenowatch, provide policy-makers with an evidence base that can be repurposed to address a range of questions.
2017 ). These concerns have lessened as the statistical methods to deal with large, imperfect datasets have improved. Yet, resistance from decision makers and government officials was still the third most frequently cited barrier to policy integration. The good scientific quality of the case examples usually led to scientific impact, with most projects fewer than ten years old cited by more than twenty other researchers. Two salient elements characterized the use of citizen science in peer-reviewed publications. First, coordination of the project by academic institutions did not appear to be a necessary condition to ensure scientific impact; academic support in the project appears to be sufficient. This suggests that projects managed by NGOs may produce data as reliable as those produced by projects managed by governments or academic institutions, as long as they receive academic support.
Creation of knowledge hubs would improve access to citizen science data and allow projects to pool resources for training and support, and to share and reuse tools and best practices at larger geographical scales. Such platforms could additionally ensure the sustainability of the data infrastructure and of-fer opportunity for ensuring data standards and interoperability by requesting appropriate metadata, encouraging open data, and providing incentives to make the data fully accessible. Together, these components could provide a one-stop shop for different policy domains, at which all relevant actors could find the knowledge and resources needed. An excellent example of such a platform is the Swedish Artportalen portal that centralizes most biodiversity data in one place, including but not exclusively citizen science.
Acknowledgements We would like to sincerely thank all citizen science projects that invested time in answering our surveys; this study would not have been possible without their support. We also thank  Elena Montani  ( DG ENV ) for overall guidance;  Assaf Shwartz ,  Margaret Gold , and colleagues at the  European Commission ; and  Marjan Van Merloo  ( DG RTD ),  Izabela Freytag  (EASME), and  Liam Cashman  ( DG ENV ) for their support and comments.
Stimuli The experiment was designed using PsychToolbox (Version 3. 0. 11; http://psychtoolbox. org/  [31] ) and written in Matlab (Version 2014a; The MathWorks, Inc. , Natick, MA). Visual stimuli consisted of a white disk (diameter = 1. 8˚) presented for 16. 7 ms (1 frame with a refresh rate of 60 Hz) on a black CRT screen placed 65 cm away from participants. The center of the visual stimuli was presented at an eccentricity of 14˚of visual angle from the horizontal meridian. Auditory stimuli consisted of white noise presented for 16 ms (incl. 5 ms fade-in and fade-out) presented at a comfortable hearing level (65 dB) using earphones. Seven delays were tested separating A and V by ±317, ±217, ±117, or 0ms. A negative delay corresponded to the auditory target being presented first (Fig  1A ).
Funding: VvW was supported by  Commissariat a' l'Energie Atomique et aux Energies Alternatives (CEA)/NeuroSpin  and by  Agence nationale de la recherche  ( ANR-16-CE37-0004-04 ). AMC was supported by the  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  research grant  17/25161-8. RSR was supported by  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cycling vibration test A self-developed Dynamic Cycling Comfort (DCC) measure system was designed to record the cycling profiles, as shown in Fig. 3 (b ). The bicycle (25 kg, using inflatable tyre) for the experiment was a shared bicycle (see Fig. 3  (a)) which is a common one seen in Chinese cities. Moreover, a skilled cyclist was recruited from a local cycling club to conduct the vibration test, who is a 28-years old healthy male (weight 83 kg, height 177 cm) with over 6,000 km of cycling experience. An acceleration data logger (HOBO Pendant G, made in USA), as shown in Fig. 3 (c),  was firmly installed on the left handlebar of the bicycle, its technical parameters are measurement range ± 29. 4 m/s², accuracy 0. 735 m/s², resolution 0. 245 m/s². The sampling rate was 100 Hz. Please note that the DCC system was designed to collect the vibration signal on the hand-bar instead of saddle and peals to represent the overall vibration that a cyclist perceived, and the vibration -cyclists' comfort relationship was pre-defined by conducting the questionnaire survey.
Acknowledgment This work was supported by the  China-UK Research and Innovation Partnership Fund (Newton Fund ) jointly funded by  China Scholarship Council and British Council , the  National Key Research and Development Program of China  ( 2018YFB1600200 ), the  National Natural Science Foundation of China  (Grant No. 51608043 ) and the  Project of Key Laboratory for Special Region Highway Engineering of Ministry of Education  (Grant No. 300102218508 ). The authors greatly appreciate the valuable help offered by anonymous volunteers during our field and perception tests. Finally, the.
Callus initiation and bulblets induction Initial explants were cultivated on the Murashige and Skoog (MS)  [27]  medium supplemented with various auxins (2,4-D, Picloram or NAA) at different concentrations (25 μM and 50 μM) and the cytokinin 6-benzylaminopurine (BAP) (0. 5 μM). The MS medium was supplemented with 3% sucrose and gelling with 0. 8% agar. The pH was adjusted to 5. 5 before autoclaving. The cultures were maintained at 25 ± 2°C under dark conditions and subcultured every 4 weeks. Treatments were arranged in a completely randomized 2×3×2 factorial design with five replications. Analysis of variance was performed using the SAS software and means were compared using LSD's test (p ≤ 0. 05). Six-month-old cultures (2g) were submerged in MS liquid-shake medium (20 mL) containing BAP (4 μM) and 2,4-D (10 μM) and precursor 4'-Omethylnorbelladine at various concentrations (0.
GC-MS identification Analyses were performed using QP2010-Shimadzu equipment operating in the EI mode at 70 eV. A agilent DB5MS column (30 m × 0. 25 mm × 0. 25 μm) was employed with a 29 min temperature program of 190-320 °C at 5 °C/min. The injector temperature was 280 °C, the flow rate of the carrier gas (helium) was 1 mL/min, and the split ratio was 1:5. The identification of the alkaloids was performed by comparing the measured data with those of authentic compounds (galanthamine, lycorine) or with literature data as specified in the text. An internal standard calibration method along with a nine-point calibration curve (R 2 =0. 99) using authentic galanthamine and lycorine were used for quantitative analysis of alkaloids.
Amaryllidaceae alkaloid profile by GC-MS and LC-MS There is no report in the literature concerning the alkaloid profiling of Iranian Narcissus tazetta and N. papyraceus bulbs. To investigate the various compounds present in the complex alkaloid fractions of four varieties of N. tazetta and one species of N. papyraceus, GC-MS and LC-MS were used. For the both methods of analysis, the identification of the alkaloids was performed by comparing the measured data with literature data  [20, 28]. Capillary GC-MS was used according to the method previously described by El Tahchy et al. [23]. Narcissus varieties bulbs (Fig. 1 ) and showed MS fragmentation patterns characteristic of the Amaryllidaceae alkaloids (Table  1 ). Tazettine was identified in all varieties of N. tazetta and not in N. papyraceus (G5).
In addition, the MZmine software identifies peaks via searches through an online database in which only a few Amaryllidaceae alkaloids are These results are complementary to those obtained by GC-MS analysis. Nine alkaloids were identified both with the GC-MS and LC-MS analysis in the various bulb extracts of Narcissus studied: trisphaeridine, assoanine, ismine, 3-epimacronine, hippeastrine, lycorenine, lycorine, galanthamine, and tazettine. However, the identification of each of these alkaloids in one genotype depended on the method of analysis considered. For example, assoanine, ismine, and tazettine were detected in the extract of N. tazetta var. Meskin (G2) by GC-MS analysis and were not detected by LC-MS. In contrary, 3-epimacronine, hippeastrine, and tazettine were detected in the extract of N.
To quantify Fos-positive neurons, we performed 3D volume imaging and used ClearMap to analyze the image stacks and perform statistical significance tests (see Methods). The group of mice presented with sweeps had significantly more Fos-positive neurons in the right ACx than the left ACx (Fig. 1a , b, compare top panels, p = 0. 05, n = 3 mice). A coronal projection of the 3D volume data revealed that the highest activation was largely in superficial cortical layers in the primary ACx (Fig. 1c, d, top ). A parasagittal cortical projection of the 3D volume data also showed that sweeps led to activation throughout the primary ACx in the right hemisphere (Fig. 1e -h top). In contrast, mice presented with vocalizations had significantly more Fos-positive neurons in the left ACx than the right ACx (Fig.
Light sheet imaging. Cleared samples were imaged in the sagittal orientation (right lateral side up) on a light sheet microscope (Ultramicroscope II, LaVision Biotec) equipped with a sCMOS camera (Andor Neo) and a × 2/0. 5 objective lens (MVPLAPO × 2) equipped with a 6 mm working distance dipping cap. Version v144 of the Imspector Microscope controller software was used. The microscope is equipped with LED lasers (488 nm, 561 nm, and 640 nm) with three fixed light sheet generating lenses. Scans were made at the × 0. 8 zoom magnification (× 1. 6 effective magnification), with a light sheet numerical aperture of 0. 1. Emission filters used were 525/50 and 680/30. The samples were scanned with a step-size of 3 µm using the continuous light sheet scanning method with the included contrast blending algorithm for the 640 nm channel (20 acquisitions per plane with a 50 ms exposure), and without horizontal scanning for the 480 nm channel (50 ms exposure).