dataesr/scientific-paragraphs-categorization huggingface dataset:
Please explain in a sentence or two why you answered the previous question the way you did The respondents gave a range of reasons as to why the teaching of computer ethics is important. The. most common was the ever-growing impact that computers have on society which was mentioned by almost 50% of the respondents. Some of the respondents highlighted specific areas within Computer Science where they believe computer ethics is important-Data Science, Artificial Intelligence, Computer Security and Ubiquitous Computing were mentioned multiple times. In terms of content delivery, some respondents felt that computer ethics should be taught by incorporating it into existing modules, whereas others felt it should be delivered as an optional module. Specific content that respondents suggested included Codes of Ethics, Intellectual Property rights, privacy, as well as the broader areas of software design and development and where computer ethics fits into those processes.
Are there plans to teach ethics on your Computer Science and/or Computer Science related programmes? The responses were evenly split between academic institutions that planning to begin teaching computer ethics on their Computer Science (and related) programmes and those who aren't (41% each). 18% of respondents did not know (Table  8 ). Respondents were asked to comment on their answer to this question. For those respondents whose institutions are planning to teach computer ethics, the main subject areas mentioned were Data Science, Artificial Intelligence, Computer Security, Health Informatics & Bioinformatics, Requirements Engineering, and CSCW (Computer-Supported Cooperative Work). One respondent mentioned that their institute are launching an Ethical-Legal stream on their MSc in Data Science programme.
How is ethics taught on your Computer Science and/or Computer Science related programmes? (choose only one answer) NOTE: in this case, a "module" refers to a single topic that a student studies over one or two semesters, e. g. Databases, Computer Networks, etc. This question explores if computer ethics is being taught as a stand-alone module, or distributed throughout several modules, or a combination of both (Table  12 ). In the majority of institutions (38%), computer ethics is taught as a standalone module.
• Computer ethics is often taught as a standalone subject. • When computer ethics is not taught as part of Computer Science (or related) programmes the most common reasons cited are a lack of staff availability and expertise. • Computer ethics is considered more important for certain Computer Science topics. For example Data Science, Artificial Intelligence and Computer Security. • Most institutions devote a relatively small number of hours to teaching computer ethics on their Computer Science or related programmes, 67% of institutions surveyed teach 10 h or less per semester. As noted from the survey computer ethics is considered more important by many respondents, for certain Computer Science topics. For example, respondents mention the importance of computer ethics with regard to topics such as Data Science, Artificial Intelligence and Computer Security and respondents replied that computer ethics is most commonly taught as part of Artificial Intelligence, Computer Security and Human Computer Interaction courses.
your institution teach ethics as part of any of the computing topics outlined below? (These classifications are based on criteria by ACM -Association for Computing Machinery) Table 16 Ethical topics Infrequently to Never Sometimes Occasionally to Regularly Ethical Issues 8% 16% 76% Code of Ethics Professional-bodies IEEE/ 23% 26% 51% ACM Ethics Theory 38% 12% 50% Argumentation 38% 25% 38% Code of Ethics National bodies 55% 26% 19% Code of Ethics IT companies 61% 23% 16% Does The survey used the European Research Council's Peer Evaluation (PE) panel clas- sifications of Computer Science (PE6) disciplines. The categories are as follows: • PE6_1: Computer architecture, pervasive computing, ubiquitous computing • PE6_2: Computer systems, parallel/distributed systems, sensor networks, embedded systems, cyber-physical systems • PE6_3: Software engineering, operating systems, computer languages • PE6_4: Theoretical computer science, formal methods, and quantum computing • PE6_5: Cryptology, security, privacy, quantum crypto • PE6_6: Algorithms, distributed, parallel and network algorithms, algorithmic game theory • PE6_7: Artificial intelligence, intelligent systems, multi agent systems • PE6_8: Computer graphics, computer vision, multi-media, computer games • PE6_9: Human computer interaction and interface, visualization and natural lan-guage processing • PE6_10: Web and information systems, database systems, information retrieval and digital libraries, data fusion • PE6_11: Machine learning, statistical data processing and applications using signal processing (e.
, 1985)  to demonstrate the reorganization of the Baltic Sea ecosystem. By integrating hydroclimatic, nutrient, plankton and fisheries data into the ITA approach they identified two stable states between 1974 and 2005, separated by a transition regime in  1988 -1993. Heymans and Tomczak (2016)  and  Tomczak et al. (2013)  combined ITA with outputs of Ecopath with Ecosim modelling approach 1 and used ecological network analysis (ENA) indicators as described by  Ulanowicz (1986) , to test for the changes in food web organisation and to describe changes that occurred in the Northern Benguela and the Baltic Sea. The main objective of this study is to investigate the hypothesis that a regime shift occurred in the PCSE during the study period. Two types of data describing ecosystem drivers (fishing mortality and environment/climatic indices) and ecosystem state (biomass and ecosystem indicators), and ITA methodology (PCA, STARS, and CC) were used to: (1) assess the changes in the PCSE over the last three decades; (2) extract the common ecosystem trends (PCA); (3) detect significant shifts in datasets (STARS and CC); (4) compare times when the shifts occurred in different datasets; (5) explore the relationship between extracted trends, and (6) discuss the role of ecosystem drivers in the shifts describing ecosystem state.
Data and Model In order to test the hypothesis of the occurrence of regime shifts in the PCSE during the last 30 years, we used data describing ecosystem drivers that include: (1) fishing mortality, (2) environmental variables and climatic indices, and data describing ecosystem state that includes: (1) observed biomass, (2) modelled biomass, and (3) ecosystem indicators (Table  1  and Figure 2 ). Fishing mortality and observed biomass data used in this study were also used as inputs of the EwE model, describing the PCSE between 1986 and 2017. The static version of this model was developed by Veiga-  Malta et al. (2019)  and further updated and fitted to 32 years of time-series data by  Szalaj et al. (2021). Moreover, modelled biomass and ecosystem indicators data used in the study come as an output of this EwE model.
The model was developed using the EwE modelling software that uses a set of differential equations to describe the ecosystem's food web structure, its functioning, and dynamics. It has been described in detail by  Christensen and Pauly (1992) ;  Pauly et al. (2000) , and  Christensen and Walters (2004)  (see text footnote 1). Using EwE data to assess the PCSE, allows for the integration of time-series data into the analysis that are not available from other sources, such as biomass of lower TL species and marine mammals. The EwE model estimated these groups and using them in the ITA provides a more complete picture of the studied ecosystem. In addition, the EwE model provided an extensive suite of indicators describing the ecosystem's properties, its structure and functioning. The use of these data allows for the assessment of the changes that occurred in the PCSE at the whole ecosystem level.
Ecosystem Drivers Data Data describing ecosystem drivers consisted of two data groups: (1) fishing mortality and (2) environmental variables and climatic indices (Table  1 ). Fishing mortality (F) data for the majority of functional groups was calculated using Stock reduction analysis (SRA;  Kimura et al. , 1984)  and implemented as a stochastic SRA  (Walters et al. , 2006)  while F for sardine and hake was obtained from stock assessment outputs  (ICES, 2018a,b). A detailed description of the SRA methodology and obtained results are provided in the Supplementary Material S2. Environmental variables consisted of annual, summer and winter means of SSTs and UI while climatic indices included annual, summer and winter means of AMO, NAO, and East Atlantic pattern (EA).
Observed biomass data Observed biomass data consisted of the biomass data aggregated into 20 functional groups (Table  1 ). These data were used to calibrate the PCSE EwE model between 1986 and 2017  (Szalaj et al. , 2021). When possible we used the outputs of stock assessment models as biomass estimates (i. e. , sardine, hake and horse mackerel). For other species, when enough data were available, we estimated plausible biomass (B SRA ) trends using SRA. For the majority species (demersal piscivorous fish, rays, sparids, bogue, benthopelagic invertivorous, and piscivorous fish, mackerel, blue jack mackerel) we used BT survey data as abundance estimates for SRA input, with the exception of anchovy for which we used abundance estimates obtained from acoustic surveys. For species that lacked sufficient data to perform a SRA, the abundance index trends were taken directly from BT surveys (sharks, squids, benthic cephalopods, flatfish, and demersal invertivorous fish), with exception of the biomass for chub mackerel which was taken from an acoustic survey.
Portuguese BT surveys are carried out in autumn (September-October) at predefined stations in the Portuguese Exclusive Economic Zone at depths varying from 20-to 500m depth  (ICES, 2017b). In this study only data that referred to the continental shelf (depth < 200 m) was considered. BT survey biomass estimates were calculated using the swept area method. Portuguese acoustic surveys (PELAGO survey series) are performed annually in spring (April-May) in an area that covers the Portuguese continental shelf. Acoustic fish data were obtained following the methodology described in ICES (  2016 ) by performing echo-integration along 1 nm Elemental sampling distance unit (ESDU, distance measure used in acoustic surveys) and supported by fish sample collection. Both acoustic and BT surveys were performed by the Portuguese Institute of Sea and Atmosphere (IPMA).
Ecosystem indicators data Various indicators that describe ecosystem properties were extracted from the EwE model fitted to the time series for the PCSE between 1986 and 2017. Among the available indices, 16 indicators that represent distinctive processes, were used in the analysis. They are (1) demersal per pelagic biomass (Dem/Pel B), (2) predatory biomass (Pred B), (3) Kempton's index (Kempton Q), (4) Shannon diversity index (Shannon), (5) total catch (Tot C), (6) demersal per pelagic catch (Dem/Pel C), (7) predatory catch (Pred C), (8) pelagic catch (Pel C), (9) demersal catch (Dem C), (10) mean trophic level of the catch (mTLc), (  11 ) mean trophic level of the community (mTLco), (12) relative ascendency (A/C), (13) Finn's cycling index (FCI), (  14 ) turnover rate (TotP/TotB), (  15 ) entropy (H), and (  16 ) redundancy (R).
Integrated Analysis Data described in subsection "Data and Model" was subjected to an ITA that was performed using the method described by Diekmann and Möllmann (2010) (Figure  2 ). It integrates the following statistical methods: (1) PCA, (2) STARSs  (Rodionov, 2004) , and (3) CC  (Legendre et al. , 1985). The PCA and CC analyses were performed using R (R Core Team, 2020), packages vegan  (Oksanen et al. , 2019)  and rioja  (Juggins, 2020). The script was obtained from  Diekmann et al. (2012). The STARS analysis was performed using STARS software written in visual basic for application (VBA) Excel 2002, available at: sites. google. com/climateologic/download. •year -1 ) (Pred B) Sum of the biomass of all species with a trophic level ≥ 4 Includes biomass of all the groups with TL ≥ 4 and tends to decrease with increasing fishing pressure in marine ecosystems  (Rochet and Trenkel, 2003 ) Kempton's index (Kempton Q) Kempton Q = Q90 = 0.
, 2007)  and it has been linked with ecosystem stability  (Christensen, 1995). It describes the distribution of energy flow pathways in the system. If the R is high, it indicates that they are many alternative pathways for energy flows to get from one compartment/functional group to another that allows compensation for the environmental stress, while lower R indicate that flows are constrained and energy is channelled through more specific pathways The PCA, based on the correlation matrix, was performed separately on fishing mortality data (PCA_FM), environmental/climatic variables (PCA_ENV_CLIM), observed biomass (PCA_B_obs), modelled biomass (PCA_B_model) and ecosystem indicators (PCA_indices). Before analysis, each dataset was checked for cross-correlation (Pearson) and variables that are highly correlated (>0.
8, Supplementary Material S3) were excluded from the PCA. The list of the variables included in the PCA analysis is shown in Table  3. The number of meaningful principal components (PC) that were considered further in the analysis was assessed by the scree plot that shows how much variation each PC captures from the data and the broken stick plot that demonstrates how many PCs should be retained. Also, variable loadings for each meaningful PC were checked and the variables whose loadings values were < 0. 3 were excluded from further analysis. Year scores for the meaningful principal components (mainly PC1 and PC2) were plotted against time to display temporal relationships (PC trends). Then PC trends were subjected to STARS analysis to detect the occurrence of regime shifts in the ecosystem.
Moreover, the relationship between PC trends extracted from the data describing ecosystem state and ecosystem drivers were explored with cross-correlation. To identify significant shifts in mean values of a given time series, STARS was performed separately on each time series of the whole dataset and on the time series of PC scores (PC trends). The STARS method is based on a sequential analysis of change points in the time series using the Student t-test. At each observation, the procedure calculates a regime shift index (RSI) to determine whether the following observation values are significantly different from the mean of the previous regime. The method and its use in the detection of shifts in marine systems were discussed in  Strinimann et al. (2019). In order to account for serial correlations (red noise component), the prewhitening procedure that adjusts the significance level of the shifts by calculating effective degrees of freedom, described in  Rodionov (2006) , was used.
It is used for multivariate datasets and clustering is based on the calculation of a similarity matrix of the time-series based on Euclidian distances. The CC analysis was performed separately on the same data groups as the PCA (fishing mortality, environmental/climatic variables, observed biomass, modelled biomass, and ecosystem indicators). According to  Diekmann and Möllmann (2010) , the significance level α (considered as a clustering-intensity parameter) and connectedness level were set to 0. 01 and 50%, respectively. To make results interpretable in relation to PCA, data were standardised prior to the calculation of the similarity matrix.
Changes in Ecosystem State (Indicators) Changes in the ecosystem were also reflected in the ecosystem indicators. The major changes in ecosystem indicators data were observed in the mid-90s and later in the mid-2010s. In the mid-90s a major change was described by a decrease in Dem/Pel B, and catch related indices (Tot C, Dem C, Dem/Pel C, and Pred C). A decrease in Dem/Pel B can be explained by an increase in biomass of pelagic species, discussed above. On the other hand, a decrease in catch related indices is probably related to the reduction of fishing effort observed in the PCSE during the last three decades  (Bueno-Pardo et al. , 2020). Later in the mid-2010s, a further reduction in Dem/Pel B was observed. At this time, an increase in TL indices indicating a rise of higher TL fish has also been documented (mTLc, mTLco, Pred B, and Tot P/Tot B).
An increase in indices, mTLc and mTLco, that are linked with the decreasing fishing pressure  (Pauly et al. , 1998; Rochet and Trenkel, 2003) , were observed in this study. This is in line with the study by  Leitão (2015a)  that assessed the profiles of Portuguese fisheries landings since 1950. In this study,  Leitão (2015a)  documented an increase in the mean TL of the catch, by approximately 0. 2 units per decade. However, in this study, we used fishing mortality data for only 10 functional groups, and this considerably limits the assessment of the impact of fishing on this system. Fishing mortality data for eight commercial groups, that were included in the biomass data (chub mackerel, sparids, flatfish, demersal and benthopelagic invertivorous fish, cephalopods, and sharks) were not considered as fishing drivers.
Additionally, in the observed biomass dataset, PC1 was represented by sparids, benthopelagic invertivorous fish, and tunas (for which data was not introduced in PCA_B_model) while for modelled biomass data set, PC1 was also represented by cephalopods. PC1_B_obs and PC1_B_model showed a statistically significant correlation with PC1_ENV_CLIM and PC2_ENV_CLIM, respectively. PC1_ENV_CLIM represented SST, AMO and EA variables, while PC2_ENV_CLIM represented NAO and UI trends. Therefore, based on this analysis it seemed that all these environmental/climatic variables might have played a role in the overall biomass changes observed in the study. It is possible that the changes in environmental drivers that were apparent in the mid-90s (SST, AMO, EA) affected the ecosystem and manifested with different time lag depends on the functional groups.
In the context of our study these findings are worrying and this limitation should be taken into consideration when interpreting the results. We tried to minimalize this effect by excluding highly correlated variables prior to PCA. Also, the ITA methodology used in this study combines different methods and not all of them rely on PCA. For example, STARS performed on separate time series accounts for autocorrelation (prewhitening method described in the "Material and Methods" section) and detected shifts in similar years (time ranges) that STARS performed on PCA output. Moreover, the CC method confirmed dissimilarity in the datasets in similar years as STARS on PCA results. However, to confirm and consolidate the conclusions of this study, as a future recommendation we would suggest to replicate the analysis using other methods that are not so sensitive to cross and autocorrelation and compare its outputs with the results obtained in this study.
Regime Shifts in the PCSE and North Atlantic The performed analyses allowed to detect three shifts in data, in the mid-90s, mid-2000s, and mid-2010s. However, based on the analysis of the results and the review of the literature, only two of them, in the mid-90s and mid-2010s, could be considered as shifts to a new regime. The shifts in the mid-90s and mid-2010s were detected by STARS on all PC trends regardless of the data group used, while the shift in the mid-2000s was mainly detected in the biomass data (when considering main PCs). Moreover, the timing of the shifts detected in the mid-90s and mid-2010s in this study coincided with the time of ecosystem regime shifts documented in the other regions of the North Atlantic. The mid-90s shift is well documented in the northern hemisphere and a large set of previous studies described it and linked it with large scale atmospheric variations (e.
In the study by  Beaugrand et al. (2019) , a model based on the numerically generated thermal niche of pseudo-communities was used and the shifts predicted by the model were validated with observed community shifts in 14 oceanic regions. This study is of special interest to us, as the shift in the mid-2010s, that was detected in our analysis, is not well documented in the scientific literature. A few substantial climate events occurred around this time: exceptionally cold North Atlantic Ocean temperatures, 2015 severe European heatwave  (Duchez et al. , 2016)  and the change in AMO phase  (Frajka-Williams et al. , 2017). However, these events are quite recent, and their ecological consequences are not yet well studied and understood. The mid-2000 regime shift that was detected in this study did not relate to any relevant environmental pressure observed in the North Atlantic in our dataset or the literature.
FUNDING This publication was financed by Portuguese national funds through  FCT -Fundação IP  under project reference  UIDB/ 04292/2020 , and by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement  N810139 :  Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. Work of DS was funded by  FCT (Fundação para a Ciência e a Tecnologia) Ph. D. fellowship (ref  PD/BD/114540/2016 ).
In order to investigate the actual influence of these molecular diffusion coefficients more precisely, we also evaluated the diffusion coefficients of N 2 and C 2 H 6 in CH 4 by molecular dynamics (MD) simulations. A similar technique has already been employed, in the context of Titan, by  Kumar & Chevrier (2020). Here, we have employed the well-known open-source package GROMACS 2 ,3 2018 version  (Abraham et al. 2015). Nitrogen molecules have been described by the TraPPE 4  force field  (Potoff & Siepmann 2001). Alkanes (i. e. , methane and ethane) were modeled by adopting the TraPPE-UA 5  coarsegrained representation where pseudoatoms are introduced for methane and ethane methyl groups. (Martin & Siepmann 1998; Mansi et al. 2017). For all computations, the intermolecular interaction cutoff has been fixed to 1.
2018 ). On board this probe, a scientific package called DraGMet 7  should contain instruments dedicated to geophysical measurements. However, to the best of our knowledge, the precise capabilities of these instruments are not publicly available. It would be surprising if they could probe the Titan subsurface layers down to a depth of several kilometers. Nonetheless, some particular circumstances could enable the search for clues concerning the behavior and the fate of liquids within underground layers. For example, Dragonfly will explore a region covered by large dunes and we can imagine a comparison of liquid composition analysis between samples collected at the base of a dune and at its top.
Data availability We make a part of the data related to the present work publicly available on the Open Science platform Zenodo 8  , which belongs to the OpenAIRE project 9. The dataset provided allows the reproduction of molecular dynamics simulations used in this work. Results from GROMACS molecular dynamics simulations performed on two binary mixtures, CH 4 (80%) + C 2 H 6 (20%) and CH 4 (80%) + N 2 (20%), and the ternary mixture CH 4 (60%) + C 2 H 6 (20%) + N 2 (20%), are available online 10  and it is also referenced with the DOI: https://doi. org/ 10. 5281/zenodo. 4975047. Each mixture is composed of 5000 molecules, and two values of temperature and pressure are considered, namely (T, p) = (90 K, 1. 5 bar) and (95 K, 120 bar). This dataset collects information on trajectories (i. e. , atomic positions and velocities, energies, and statistical data) and transport properties (diffusion and viscosity) for the six aforementioned cases (three mixtures under two (T, p) conditions).