dataesr/scientific-paragraphs-categorization huggingface dataset:
5 to 2 mm. A very light chamber preparation was made on posterior teeth in vestibular and proximal faces with a supragingival position, and a finish line on the cervical area of anterior teeth was positioned in the sulcus Subsequently, an optical impression of the two arches with the composite resin mock-up was taken with intra-oral scanner Trios 3 (3Shape A/S, Copenhagen, Denmark). The Trios intra-oral scanner was operated on the confocal principle, with the video bitrate image capturing method based on the real-time rendering technique. The software 3Shape Dental Designer was used for modelling veenerlays and crowns. Once the volume of the final restoration was defined by the complete mock-up, the preparation of teeth for the definitive adhesive restoration could be performed with calibrated burs to achieve an occlusal reduction of 1.
5 to 2 mm. A very light chamber preparation was made on posterior teeth in vestibular and proximal faces with a supragingival position, and a finish line on the cervical area of anterior teeth was positioned in the sulcus to optimize the aesthetic result (Figure  6 ). Subsequently, an optical impression of the two arches with the composite resin mock-up was taken with intra-oral scanner Trios 3 (3Shape A/S, Copenhagen, Denmark). The Trios intra-oral scanner was operated on the confocal principle, with the video bitrate image capturing method based on the real-time rendering technique. The software 3Shape Dental Designer was used for modelling veenerlays and crowns. Once the volume of the final restoration was defined by the complete mock-up, the preparation of teeth for the definitive adhesive restoration could be performed with calibrated burs to achieve an occlusal reduction of 1.
The resulting image of the virtual working model is shown in Figure  7. Once teeth were prepared, an optical impression of the preparations was made, and a temporary resin (Structur Premium, VOCO) was placed on the teeth with silicone keys (Figure  9 ) to protect them for a week, the time needed for the prosthetic laboratory to prepare the fixed prosthetic components. A digital cast was obtained by intraoral scanning, and the quality of the preparations was reevaluated. Veenerlays and crowns were designed by using the Dental Designer software program. A laboratory modelled the prosthetic elements according to the mockup, and then the design file was sent to the DWX-42 W machine from DG Shape. Figure  10  illustrates the teeth prepared on the virtual model (a) and the prosthetic elements according to the mock-up (b).
The laboratory's in-house dental technician applied make-up to the prosthetics components to enhance the aesthetic appearance and then applied the glaze layer, which was crystallized in an oven according to the manufacturer's recommendations. During the second clinical session, the temporary resin was applied, the teeth were A digital cast was obtained by intraoral scanning, and the quality of the preparations was reevaluated. Veenerlays and crowns were designed by using the Dental Designer software program. A laboratory modelled the prosthetic elements according to the mockup, and then the design file was sent to the DWX-42 W machine from DG Shape. Figure  10  illustrates the teeth prepared on the virtual model (a) and the prosthetic elements according to the mock-up (b). A digital cast was obtained by intraoral scanning, and the quality of the preparations was reevaluated.
Veenerlays and crowns were designed by using the Dental Designer software program. A laboratory modelled the prosthetic elements according to the mockup, and then the design file was sent to the DWX-42 W machine from DG Shape. Figure  10  illustrates the teeth prepared on the virtual model (a) and the prosthetic elements according to the mock-up (b). The laboratory's in-house dental technician applied make-up to the prosthetics components to enhance the aesthetic appearance and then applied the glaze layer, which was crystallized in an oven according to the manufacturer's recommendations. During the second clinical session, the temporary resin was applied, the teeth were The laboratory's in-house dental technician applied make-up to the prosthetics components to enhance the aesthetic appearance and then applied the glaze layer, which was crystallized in an oven according to the manufacturer's recommendations.
Contamination can occur during the assembly of the spacecraft and proceed all the way to analyses of returned material  (McCubbin et al. , 2019; Chan et al. , 2020). Sequences of any PCR-amplified SSU rRNA genes derived from such samples could easily be compared to those from known spacecraft and spacecraft assembly facility contaminants (e. g. , La  Duc et al. , 2014; Moissl-Eichinger et al. , 2015; Koskinen et al. , 2017; Regberg et al. , 2020). Slim as the possibility is, there could exist life as we know it that is otherwise very different from known life on Earth, e. g. , that evinces nucleic acid sequences and protein sequences that are so distinct from those in existing databases that one might conclude that they represent a life form that evolved on Mars rather than Earth. Such a conclusion would have to be made with the utmost care and with the hindsight that we are continually discovering novel terrestrial life forms and an increasing body of unannotated sequences in metagenomic datasets (i.
e. , microbial dark matter,  Rinke et al. , 2013). This has occurred partly through the development of new tools. Sequencing approaches revealed the existence of a third branch of life, the Archaea, only about 40 years ago; high-throughput DNA sequencing continues to unearth new microbial phyla. Also, the exploration of new, extreme habitats such as the deep ocean and the continental subsurface has greatly expanded our datasets. In other words, life as we know it is much more diverse than we knew just a few decades ago and may encompass even more forms by the time extraterrestrial samples are examined on Earth. Beyond self-replicating life, there are new viruses discovered on a monthly basis. This includes some very different classes of viruses, such as the giant viruses found across widespread habitats and ecological systems (e.
g. , two GC teams analyze aliquots of an extract). Investigations where two independent measurements made with complementary techniques are utilized to increase the predictive value of the results (two different techniques are used, e. g. , spectroscopy and spectrometry, to analyze aliquots of an extract). The use of complementary techniques increases the probability that a given result is true if the datasets agree and that they will trigger further investigations if disparate. Although use of the same technique more than once compensates for intra-technique variability, it is more critical to address the measurement accuracy in the safety assessment context (since precision is already accounted for in the selection and validation of the chosen techniques). The execution of the test sequence must follow an approach typically used in science and engineering when assessing public safety or environmental impacts, namely deploy two independent teams to perform the measurements or data analysis (see three cases above), with a third independent team responsible for decision making.
Of particular importance in this regard are the M2020 Witness Tube Assemblies (WTA), which are opened and sealed during different mission phases including pre-launch, launch, cruise, and Mars Entry Descent and Landing (EDL), and M2020 surface operations, as well as the M2020 drillable blank which can provide CK of the M2020 drilling operation. CK samples should also be collected during the construction of the SRF to establish a complete archive of potential contaminants, including biological contaminants that may come into contact with martian samples during sample analysis. Minimizing terrestrial biological contamination in the samples and a higher CK would reduce uncertainty in the scientific interpretation of the data and ease handling and treatment of the samples. To differentiate between martian or terrestrial origin, the field of omics will play an essential role.
Life detection and machine learning The sample safety assessment defined by the SSAF depends upon the simultaneous interpretation of numerous variables and criteria as well as proper statistical treatment of large datasets that exclude false negatives and false positives. In several ways, this challenge is similar to that of biogenicity tests for putative traces of fossil life in the Earth's rock record. Classical tests of biogenicity in deep time involve the evaluation of multiple biosignature characteristics and context-and contamination-related criteria that need to be satisfied to substantiate a claim (e. g. ,  Buick, 1990; Schopf et al. , 2010; Brasier and Wacey, 2012; Neveu et al. , 2018). The number and combinations of these characteristics and criteria, however, are subject to debate, since it is easy to include false positives or exclude false negatives.
In reality, there are no clear yes/no answers in biogenicity tests since all biosignatures have a certain probability that life created them and a certain improbability that an abiotic process created them (Des  Marais et al. , 2008). The qualitative nature of many individual biosignatures (e. g. , morphological characteristics) add further ambiguity, as they are often not standardized and depend on the interpretation and experience of individual observers. To overcome this inherent uncertainty in life detection and during efforts to exclude a biological origin, several recent studies have expressed the specific need for standardized criteria and a more quantitative approach in data treatment  (Chan et al. , 2019; Neveu et al. , 2018; Rouillard et al. , 2020 Rouillard et al. , , 2021)). The use of multiple well-defined and quantifiable variables for life detection could greatly benefit from recent advances in statistical methods and machine learning methods to find commonalities in large datasets.
While standard statistical methods passively draw inferences from a dataset, machine learning methods create mathematical models based on training data and use this ''experience'' to find predictive patterns in new datasets  ( Jordan and Mitchell, 2015; Hastie et al. , 2017). Typical tasks carried out by machine learning include classification, regression, ranking, clustering, and pattern recognition. A so-called supervised machine learning algorithm builds a mathematical model based on a training dataset that contains both input and desired output. The program thus responds to feedback. In contrast, an unsupervised machine learning algorithm builds a mathematical model without any desired output. It finds patterns in a dataset, and then attempts to find similar ones in newly supplied datasets.
, 2020). For the purpose of the SSAF, there are three categories to be considered: 1. No life 2. Life as we know it 3. Life as we don't know it For the first two categories, a supervised machine learning algorithm can potentially be created if appropriate training datasets can be supplied and unambiguous desired outputs can be formulated. Training datasets for ''no life'' would consist of the morphologic, compositional, and isotopic characteristics of structures, such as crystals, sand grains, and precipitates, for which an explicit geologic formation process exists. Training datasets for ''life as we know it'' constitute established biosignatures, such as DNA sequences, certain organic molecules, and certain complex morphologies. Many examples of known biosignatures are presented in various overview papers, most of which focus on the recognition of fossil life  (Buick, 1990; Neveu et al.
, 2018; Schopf et al. , 2010; Brasier and Wacey, 2012; Rouillard et al. , 2021a). There is, however, a growing literature on ''false biosignatures'', i. e. , physiochemical processes that lead to the formation of minerals or molecules with life-like features  (Cosmidis and Templeton, 2016; Garcia-Ruiz et al. , 2003; Jordan et al. , 2019; Kotopoulou et al. , 2021; Rouillard et al. , 2018 Rouillard et al. , , 2021b;; McMahon et al. , 2021). It may thus prove difficult to define clearly the exact difference between category 1 ''no life'' and category 2 ''life as we know it''. An important future scientific challenge lies in properly defining this difference, and subsequently creating widely accepted training datasets for categories 1 and 2 that can be used for developing a machine learning protocol.
This supervised machine learning protocol, however, would not work for category 3 ''life as we don't know it,'' since training datasets, as well as a desired output, are fundamentally missing from Earth-based analogue samples. It may be possible, though, to identify this third category by exclusion of the first two categories. This effectively involves searching for levels of complexity that are incompatible with ''life as we know it'' (category 2) and with the absence of life (category 1). Thus, a complete machine learning protocol would start with a supervised algorithm 1 to find ''no life. '' If it fails to find ''no life,'' then it is possible that there is some form of life there. Supervised algorithm 2 would then be applied to find ''life as we know it. '' If both algorithms fail, then the sample must fall into category 3 ''life as we don't know it.
'' An unsupervised machine learning algorithm can potentially be applied to find as of yet unidentified patterns that may be assigned as provisional biosignatures, which can then be searched for in other samples. In general, it is not envisioned at this point that such work will be entirely dependent on these forms of artificial intelligence. At this time, an experienced human observer is superior to a set of algorithms. However, the use of these machine learning methods in the treatment of large datasets could assist in finding patterns and focus attention on specific features of astrobiological interest. For instance, in a large set of close-up images of martian sediments, it may be useful to have a supervised machine learning program with a category 1 algorithm for ''no life'' that has been trained in grouping crystal types, grain sizes, and distinguishing common sedimentary patterns, followed by a category 2 algorithm for ''life as we know it'' that checks for a list of known biosignatures.
Development needs 16. An analogue test program is necessary to: (a) Assess and improve the capture rate and the associated subsampling strategy. (b) Optimize the selection of the instrumentensemble to be used for the test sequence and estimate the overall sensitivity and specificity of the test sequence, including efficiency to extract evidence of life from host materials. (c) Exercise all elements of the sample safety assessment, including blind testing, to optimize processes, equipment and infrastructure, train personnel and science teams, and build confidence. 17. Contamination Knowledge (CK) covering all flight (Mars 2020, MSR program) and ground (SRF) elements is critically important to reduce uncertainty in the interpretation of the data and, as a consequence, avoid unnecessary rigor in handling and treating the samples.
18. Use of machine learning to support the sample safety assessment, in particular for data reduction and pattern recognition in large and diverse datasets, has the potential to improve the quality of the results and accelerate the process. 19. Once the MSR science investigations are selected, the appropriate regulatory authorities are in place, and any open development needs with respect to the overall sample safety assessment are addressed, this SSAF must be critically reviewed by the relevant stakeholders. The latest applicable version of the SSAF would be the basis for developing a detailed Sample Safety Assessment Protocol (SSAP). 20. A transparent risk communication throughout the development and execution of the SSAF and subsequent SSAP is essential to preserve the sovereignty of information.
Acknowledgments The  COSPAR Sample Safety Assessment Framework (SSAF)  has been developed by the  COSPAR Sample Safety Assessment Protocol Working Group  during the COVID-19 pandemic. GK would like to thank all members of the Working Group for their commitment and dedication during this challenging time and in particular, those that were involved in public health response activities throughout the drafting of this document. We would like to thank  Hayley Jones ,  Hugo Pedder ,  Chin Yang Shapland  (all  University of Bristol, UK ), and  Linda Sharples  ( London School of Hygiene & Tropical Medicine ) for their comments on the statistical section of this paper. We would like to thank the  US Centers for Disease Control and Prevention (CDC) team-David Lowe ,  Melissa Brykailo Pearce ,  Samuel Edwin ,  Matthew J.
Study sites This study took place in three study sites in Northern Iraq between June and August 2017 during the peak of the offensive against the Islamic State of Iraq and the Levant (hereafter referred to as Da'ish). We selected research sites purposively to reflect different durations of displacement (e. g. a short-term displacement camp, a long-term displacement camp and returnee villages), different social and physical settings within a conflict (e. g. comparing 'closed' verses 'open' camp settings, and comparing tented shelters to damaged buildings), and differences in water, sanitation, and hygiene (WASH) coverage. The first site was the tented Nargizliya Camp located within Dohuk Governorate in the Kurdistan Region of Iraq. Founded 6 months prior to data collection, Nargizliya housed 9,905 people who were predominantly Arab and had fled from the city of Mosul and its surrounding villages.
figshare. Dataset. https://doi. org/10. 6084/m9. figshare. 17263829. Observational data, visual data and data from handwashing demonstrations is not available because it is not possible to make this data anonymous. Funding: Funding for this research was provided by United States Agency for international development's Bureau of Humanitarian Assistance via grant number AID-OFDA-G-16-00270. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This grant contributed to the salaries of Sian White, Thomas Heath, Waleed Khalid Ibrahum and Dilveen Ihsan, but did not fully fund these.
Data collection methods Observations. Unstructured observations took place in 20 households across the three sites and were designed to understand behaviour within a contextual setting. Observations were scheduled for 3 hours, typically beginning at 8am (depending on local security). Observers wrote down all actions done by household members and the time actions took place. Observers recorded the availability of soap and participant behaviour during 'critical moments' for handwashing which were defined as handwashing after using the toilet, or cleaning a child's bottom, and before preparing food, eating food or feeding a child. Households participating in the observation were informed that we were interested in learning about 'daily routines' and were therefore not aware that the study was specifically interested in handwashing, so to reduce reactivity.
Given that we were taking notes on all action that occurred, it was also unlikely that participants could deduce that we were interested in handwashing behaviour. To monitor the quality of the data recorded, two households were observed at the same time (by two of the authors-DI and WKI) while the lead researcher (SW) moved between households and took notes to allow cross-checking and consistency. Focus group discussions. Ten group discussions were completed across the study sites involving 93 people. Four participatory activities were included in the group discussions to explore current and past hygiene challenges, priorities, perceived risk and preferences related to infrastructure and soap. Participatory activities included free listing and categorisation of priorities, risk scaling and appraising the characteristics handwashing products and infrastructure.
Data collection, management, and analysis Interviews, group discussions and observations were conducted by the three authors (SW, DI and WKI). We were a team with mixed cultural backgrounds (SW is British and DI and WKI are Kurdish) and all had prior experience with qualitative data collection. SW provided two days of classroom-based training on the research methods and we spent two days piloting the methods in a similar setting to ensure all research team members understood the methods and were able to apply them in a consistent way. All interviews and group discussions were conducted in Kurdish or Arabic, audio recorded and transcribed and translated. Observation notes were taken by hand and entered into a digital spreadsheet the same day. The handwashing demonstrations were video recorded.
Preliminary data analysis was done concurrently with data collection. This allowed us make theoretical and methodological notes  [30]  and decide when we had reached a point of saturation. Audio recordings from interviews and group discussions were transcribed and translated. Methods with ranked or scaled data were summarised in spreadsheets. Drawings, photos and videos were descriptively summarised. All data was imported into NVivo 12 software. The data analysis was informed by the process outlined by Braun and Clarke  [31]. Data were classified according to study site, gender and method and comparisons were made between study sites and between current practices and reported behaviours prior to the conflict. A top-down coding framework was applied based on the BCD checklist and emergent themes were added.
To compensate for these limitations, we triangulated data across the methods used and employed visuals, props and scenarios as part of participatory activities in order to illicit different types of responses. We also complemented talk-based approaches with observations and demonstrations. Some of the activities we used within interviews and group discussions, were also developed for this research or have only been piloted in a small number of other studies. Therefore, the validity and reliability of these tools should be tested further. Given the methodological limitations we were working with, our research was only able to describe apparent associational relationships between determinants and handwashing behaviour, rather than quantify or definitively state the impact of determinants on behaviour.
Acknowledgments We would like to thank the following people for helping to facilitate the research and contributing to ongoing reflections about emergent insights:  Waleed Rasheed ,  Honar Hasan ,  Asmaa Farooq ,  Basima Ahmed ,  Aso Zangana ,  Mustafa Abdalla ,  Nazar Shabila ,  Tara Vernon ,  Geraldine Delestienne ,  Karine Le Roch  and  Jean Lapegue. We would particularly like to thank the individuals who gave of their time to participate in this research and who welcomed us into their homes and shared their personal experiences so openly. This research was undertaken as part of the Wash'Em Project which aims to improve handwashing promotion in humanitarian crises. The research was made possible by the generous support of the American people through the  United States Agency for international development's Bureau of Humanitarian Assistance.
Interface evaluation The interface was assessed online by 10 medical doctors with experience in emergency phone call triage or in the design of clinical decision support system. Some of them have been involved in the initial writing of the textual recommendations. They tested the interface individually, without supervision, with two clinical cases (fever and head injury), and then filled an online form. The form collected anonymous sociodemographic data (e. g. , level of experience in emergency phone call triage), and 6 questions related to the ergonomics and the utility of the interface compared to the textual recommendations. Each question was assessed through a 5-points Likert-Scale. Additional open-ended questions were also integrated for free comments.