dataesr/scientific-paragraphs-categorization huggingface dataset:
TwR6 -Resilience: Trustee has to guarantee the process execution in case of failure of one of components. Trustor must be able to recover the data and to transmit it into other system. TwR7 -Availability: All resources (human, physical, hardware/software, information) needed for process/activity execution has to be available. This requirement has to be fulfilled by a trustee (an organization, an individual, or a mediating infrastructure). According to the literature, resilience and availability mostly determine social trust and trust in technology. Benevolence is the extent to which a trustee is believed to want to do good to the trustor, aside from an egocentric profit motive  [2]. We identified 7 trustworthiness requirements that refer to trustee's ability to guarantee (or to trustor's capacity to control) that trustee's actions will cause trustor no harm (Table  6 ).
For digital trust: A blockchain-based system (= trustee) has to ensure that no personal data is collected and/or used by the network from the connected individuals or devices (= trustor) without their explicit consent. Our analysis shows that, in spite of growing importance of digital trust, social trust and trust to technology remain important drivers in organizational decision making. The TwR identified in this study not only drive the design and development of trust-enabling technological solutions but also determine relationships between individuals and organizations. They reflect the needs for all three types of trust in organizations.
Following the ISO guidelines, we formulated a set of 21 TwR consistent with the related works (C2). While originated from blockchain literature, the TwR are formulated in a technology-neutral language and do not advocate blockchain or any other specific technological solution. To bridge the gap between social and technological domains, we associated the identified TwR with the three trustworthiness factors defined in the social science: ability, benevolence and integrity (C3). Our literature analysis shown that trust issues can be expressed by stakeholders at different organizational levels, varying in scope and technical details. We discussed how TwR can be mapped on three abstraction levels consistent with enterprise architecture (strategic, operational and IT) (C4). While being strongly presented in the literature, digital trust is not the only form of trust that the organizations seek to reenforce: trust in technology and social trust remain powerful drivers in decision making.
Threats to Validity and Directions for the Future Work This study follows a systematic literature review approach  [20]  to ensure accuracy and eliminate bias, nevertheless the following limitations can be listed: This study examines primary research focused on design of blockchain-based solutions. This threatens completeness of our presented requirements taxonomy. More general analysis of trust issues can bring new insights and extend this taxonomy. This SLR reveals very little agreement on trust definition in blockchain community. Whereas both social and technology-centric definitions of trust are in use, the majority of studies do not provide an explicit definition of trust. This discrepancy in trust definition represented a challenge during data extraction, analysis and coding. The authors often had to rely on their experience and interpretation, what presents a threat to the internal validity of this study.
METHODS We used Google Health Trends API (application programing interface) to download data on relative search volume (search volume of a specific term divided by the overall search volume in a region and period  [Zepecki et al. , 2020] ) for biodiversity topics for 191 of the 193 countries recognized by the United Nations (China and North Korea were excluded due to minimal use of Google search engine) from 2013 to 2020. As opposed to the online Google Trends API, Google Health Trends API does not scale relative search volumes inside each time series; thus, it is preferable for comparing topics and regions  (Zepecki et al. , 2020). We used unique identifiers assigned to each search topic by the Google Knowledge Graph to obtain our search volumes (obtained through gkgraphR package  [Correia, 2021] ).
This feature allows inclusion of synonyms and different languages in the search volume data for a particular topic and exclusion of irrelevant homonyms (Google News Initiative, 2022). For example, when searching for the words puma, cougar, mountain lion, suçuarana, or onça-parda (the last 2 are names used in Brazil for the same animal), the search engine will autocomplete and show the option "puma (animal). " All these synonyms will share the same unique identifier in Google Knowledge Graph, which is distinct from the identifier for the homonym PUMA (a clothing brand). Our analyses spanned the years 2013-2020 due to the merger between Google Insights for Search and Google Trends in 2012 and because the topics feature mentioned above was not available previous to this merger  (Google Inside Search, 2012).
Although Google is the dominant search engine worldwide, its use is less widespread in some countries, either due to state restrictions, as in China, or due to user preferences, as in Russia  (Statcounter, 2022). Therefore, results regarding these countries should be interpreted with care. We highlighted countries in which Google is not the only major dominant search engine (i. e. , <75% of the search engine share) in our maps and appendices. We excluded China from all analyses because of the very low percentage of use of Google and North Korea because of state restrictions on internet use  (Warf, 2015). Other limitations to and nuances of the use of Google Trends as a proxy for public interest are discussed extensively in  Troumbis and Iosifidis (2020). We selected biodiversity topics to fit 4 broad categories: the 500 most viewed taxonomic entities in Wikipedia from 2013 to 2020 plus the 10 most viewed in all of the 320 different languages identified by Wikipedia (  989  To identify the top most viewed taxonomic entities in Wikipedia, we used wikitaxa  (Chamberlain & Welty, 2020)  to search for all taxa in each Regna in the Wikispecies taxonomy ("Archaea," "Bacteria," "Protista," "Virus," "Fungi," "Plantae," "Animalia") and their associated Wikidata identifiers.
As for the choice of topics in the conservation actions category, we recognize that different groups of scientists will likely have different ideas as to which steps are necessary to conserve biodiversity, so we relied on reputable sources, such as the International Union for the Conservation of Nature (IUCN), which often informs policy making. Moreover, the terms used by these reputable sources have unique identifiers associated with them in Google Search, which allowed us to evaluate searches for them across languages. Put together we aimed to minimize the potential of personal biases in our selection of keywords by including lists from reputable sources and species lists gathered similarly across many languages and regions in the hope that these keywords would reflect a wide consensus. Our methods may give greater emphasis to countries with many local languages (e.
We further used a Bayesian structural time series (BSTS) model to estimate temporal trends in biodiversity and conservation interest (i. e. , median slope coefficient of a linear trend component) with the bsts R package  (Scott, 2022). The algorithm BSTS is efficient and flexible and allows decomposition of time series to seasonality and trends  (Scott & Varian, 2014). We used bivariate choropleth maps to visualize trends and overall interest across countries. Bivariate choropleth maps are a plotting technique in which 2 color scales are used to represent trends across 2 variables on a map  (Carstensen, 1986). We also regressed trends against median interest in either biodiversity or conservation across countries to explore associations between overall interest and its temporal trend. We further investigated correlates of trend and median interest in biodiversity and conservation.
Second, even if there is a lack of consensus in the community over each bundle's anatomical definition  (Rheault et al. , 2020b) , some state-of-the-art methods do not allow an easy modification of anatomical definitions (TractSeg, XTRACT ). In fact, if a change in bundle definitions is needed with TractSeg  (Wasserthal et al. , 2018b) , a full reannotation of the training dataset is required alongside with a complete retraining of its three neural networks. Contrastingly, XTRACT  (Warrington et al. , 2020)  heavily relies on regions of interest (ROI) drawn by expert neuroanatomists to work properly. Those ROIs are usually hard to get and prone to inter-expert variability. Third, methods such as WMA are impractical in a large scale context because they rely on an affinity matrix computed using the pairwise distance of each streamline in a whole-brain tractogram.
To avoid confusion and to be sure to distinguish the slight method variations between previous works and the current implemented methods, we renamed the implemented 2in-1 filtering and bundle segmentation method as FINTA-multibundle, and the implemented generative method as GESTA-gmm (Gaussian mixture model) (c. f. Section 2). This work makes the following five contributions: 1. FIESTA leverages the power of previous fiber autoencoder methods  (Legarreta et al. , 2021 (Legarreta et al. , , 2022 (Legarreta et al. , , 2023) )  and overcomes their individual limitations, yielding a pipeline able to work in an end-to-end in vivo dMRI data analysis scenario; 2. FIESTA is more reliable than current state-of-the-art automatic bundle segmentation methods; 3. We show that the usage of generative sampling improves the bundle-wise volumetric reliability; 4.
Data 2022) bundles were used for the threshold calibration steps. HCP subjects were also used for adjusting GESTA-gmm's parameters. TractoInferno  (Poulin et al. , 2021 ) is a publicly available, widely heterogeneous database designed for machine learning purposes in dMRI tractography. It is composed of 354 subjects from 6 different datasets acquired with 5 different MRI scanners with various image resolutions, acquisition parameters, and subject ages. MRI acquisitions underwent a manual quality control (QC) process before using an ensemble of four tracking types -namely local deterministic, local probabilistic, PFT  (Girard et al. , 2014), and SET (St-Onge et al. , 2018)  before being processed into RecoBundlesX, yielding its silver standard. TractoInferno whole-brain tractograms were used for the autoencoder training (c.
f. Section 2. 2. 1), whilst TractoInferno silver standard bundles were only used for the latent space distance threshold calibration (c. f. Section 2. 3. 1). MyeloInferno  (Edde et al. , 2023 ) is a non-public dMRI dataset. We have divided the MyeloInferno dataset into 2 single time-point subsets and 2 five-time-point subsets. One single time-point subset, MyeloInferno-HC, was composed of 24 young and healthy control (HC) subjects (mean age 36 years ± 4. 7 [standard deviation]; 17 women) and the other, MyeloInferno-MS, was composed of 21 young subjects with multiple sclerosis (MS) (mean age 38 years ± 6. 8 [standard deviation]; 16 women). Written informed consent was obtained from participants and were recruited following the ethics protocol of the Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (Sherbrooke, Canada).
Most HC and MS subjects in MyeloInferno had 5 time-points or more. Thus, for the creation of our 2 test-retest subsets, we only kept data with 5 time-points or more and excluded time-points above 5 for our analysis. We named those 2 new subsets MyeloInferno-HC-TR and MyeloInferno-MS-TR. MyeloInferno-HC-TR contains 𝑁 = 18 × 5 = 90 acquisitions, whilst MyeloInferno-MS-TR contains 𝑁 = 19 × 5 = 95 acquisitions. Thus, the data are ideal to evaluate the reproducibility of our pipeline. All images were acquired on a 3T Ingenia MR scanner with a 32-channel head coil (Philips Healthcare, Best, Netherlands) with T1w images acquired with a resolution of 1 mm isotropic voxels and dMRI images acquired with a resolution of 2 mm isotropic voxels, 100 gradient directions uniformly distributed over three shells (𝑏 = {300  (8), 2000(32) , 3000(60)} s∕mm 2 ) (the number of directions per shell is in parentheses), and 7 unweighted images.
Each tractogram was an ensemble of tractograms generated using the TractoFlow pipeline  (Theaud et al. , 2020) , which provided a PFT probabilistic tracking and a local probabilistic tracking that were concatenated. PFT tractograms were generated using interface seeding with 30 seeds per voxel, while local tractograms were generated using white matter seeding with 10 seeds per voxel. Both tracking methods were constrained with a streamline length range between and 200 mm. More details can be found in  Edde et al. (2023)  and on the corresponding website at https://high-frequency-mri-databasesupplementary. readthedocs. io/en/latest/index. html. The Human Connectome Project (HCP) Young Adult dataset  (Glasser et al. , 2013 (Glasser et al. , , 2016 ) is a dataset of approximately 1200 subjects (age range 22-35 y/o) composed of different MRI modalities acquired on Siemens scanners.
The autoencoder training scheme We trained the autoencoder in a train-validation-test scheme. Our whole dataset is composed of 120 subjects randomly shuffled with an 80/20 split between TractoInferno and HCP datasets (c. f. Section 2. 1). The train/validation/test split was therefore 100/10/10. 200,000 streamlines per subject were used, with 100,000 streamlines per HCP whole-brain tractogram (𝑁 = 2 -namely PFT and local probabilistic tracking) and 50,000 streamlines per TractoInferno whole-brain tractogram were randomly sampled from each raw tractograms (𝑁 = 4namely PFT, SET, local deterministic tracking and local probabilistic tracking). In total, our dataset contained 24,000,000 streamlines. Also, each streamline is resampled to a fixed dimensionality of 𝐷 = 256 3D vertices and the latent space dimensionality is fixed to 𝑑 = 32.
Reliability evaluation metrics FIESTA's test-retest reliability was assessed using the MyeloInferno dataset according to five overlap metrics -namely (i) the voxelwise Dice coefficient, (ii) the weighted voxel-wise Dice coefficient, (iii) Fig. 5. Saturation curves of various bundles generated by GESTA-gmm showing that between 5000 and 15,000 is a reasonable amount of generated streamlines to cover the entirety of the bundle. After 15,000 streamlines, the volume of each bundle does not increase significantly. the voxel-wise bundle adjacency, (iv) the streamline-wise bundle adjacency, and (v) the streamline density correlation  (Rheault et al. , 2020a; Cousineau et al. , 2017; Dice, 1945). The Dice and the weighted Dice coefficients are overlap measures between 2 bundles varying from 0 to 1 where 1 means a perfect overlap.
FIESTA qualitative evaluation To gain a better intuition into the different tested methods, we conducted a qualitative analysis on a random subject from the MyeloInferno-HC-TR subset. We qualitatively compared various bundles from the different benchmarked methods in order to understand the pitfalls and the quality of each one of them. As one subject is not sufficient to conclude on the bundles' anatomical accuracy, we conducted a more extensive qualitative assessment using the dMRIQCpy toolbox  (Theaud, 2022)  on the 95 individual images of the MyeloInferno-MS-TR dataset. The qualitative evaluation was conducted using a 3 score classification following the user guide of dMRIQCpy on the 27 similar bundles shared amongst methods. The 3 possible scores were either pass, warning, or fail. For each bundle, or a pair of bundles if applicable, a pass score was given when the bundle clearly respected the anatomical definition of the atlas, overlapped the correct brain region, and was located in the expected hemispheric and anatomical region.
Performance on disease cohorts In order to grasp the performance of FIESTA on disease subjects, we extended the test-retest analysis to subjects with neurodegenerative diseases. Therefore, the ADNI-TR, PPMI, and MyeloInferno-MS-TR datasets were used with their multiple time-points per subject. ADNI-TR subjects used for this analysis included a mixture of normal aging, MCI, and AD subjects. PPMI subjects were only composed of subjects with Parkinson's disease (PD). MyeloInferno-MS-TR contained subjects with multiple sclerosis disease. All three datasets contain multiple time-points, with 5 for ADNI-TR and MyeloInferno-MS-TR, and 3 for PPMI. Thus, we repeated the reliability evaluation on those 3 datasets following the same procedure presented in Section 2. 5.
Qualitative analysis To gain insight into the different methods, we conducted a qualitative analysis on a random subject from the MyeloInferno-HC-TR dataset. Fig. 6  lays out the same bundles across each bundle segmentation method for a given subject. First, it is qualitatively seen that FIESTA's results (which contains generated streamlines), seems to have the best hemispheric homogeneity. This is particularly true for the PYT and the CC_Pr_Po bundles. TractSeg misses some WM streamlines on the left PYT and the CC_Pr_Po bundles. Otherwise, TractSeg and WMA seem to output cleaned bundles with few spurious streamlines. The major drawback of WMA comes from its inability to process the original tractograms due to memory issues (∼4M streamlines in the MyeloInferno dataset for each whole-brain tractogram).
Concerning the qualitative analysis made on 95 acquisitions of the MyeloInferno-MS-TR dataset using the dMRIQCpy  (Theaud, 2022)  toolbox, all the 27 evaluated bundles over the whole dataset were given a score of pass. Such results confirm that, qualitatively, FIESTA performs well on a disease cohort where all bundles are fully populated, located in the right hemisphere and in the correct anatomical region. During this exercise, the shape of all bundles were compared to the shape of the bundle in the PAWM atlas. All bundles presented a qualitative similar shape to the atlas of bundles used for the development of FIESTA.
Reliability analysis Table  1  presents the results on the MyeloInferno dataset of the average similarity metrics for each method benchmarked over 17 similar bundles, all shared among the different methods. Quantitative average results show that FIESTA yielded the best Dice, voxel-wise Dice, voxel-wise bundle adjacency, and streamline density correlation with 0. 74 ± 0. 08, 0. 96 ± 0. 04, 0. 43 ± 0. 21 mm, and 0. 85 ± 0. 13 respectively, whereas FINTA-multibundle obtained the best streamlinewise bundle adjacency with a value of 5. 02 ± 0. 54 mm. The voxel-wise bundle adjacency shows that, on average, two binary masks of the same bundle from the same subject are ∼0. 5 mm apart using FIESTA. For each metric where FIESTA did not come up first, it was in the margin of error of the first one. TractSeg yielded the best ICC length and ICC volume with values of with 0.
More specifically, the streamline density correlation, the Dice score and the weighted Dice score (Fig. 7a, 7b,  and 7c ), show that FIESTA yields higher within-bundle similarity. Fig. 7a  shows that the streamline density between different acquisitions is systematically more correlated when using FIESTA than with the other methods. The voxel-wise bundle adjacency of FIESTA shows that the average distance between 2 bundle masks of the same subject is ∼0. 5 mm. Knowing that the voxel size of the MyeloInferno dataset is 2 mm isotropic, this underlines how reproducible FIESTA is. Finally, Fig. 7e  shows that, depending on the studied bundle, the TractSeg streamline-wise bundle adjacency is close to FIESTA and FINTA-multibundle, with many bundles surpassing the proposed method, whilst never surpassing FINTA-multibundle.
75, thus producing good reliability measurement (c. f. Table  1 ). Table  2  presents the results of FIESTA on three disease cohortsnamely ADNI-TR, PPMI, and MyeloInferno-MS-TR. Results show that performances on those cohorts are similar to what were obtained on healthy subjects, presented in Table  1. Thus, it shows the potential of using FIESTA on a wide range of population. Also, results presented in Table  2  shows the usefulness of using GESTA-gmm to improve most reliability metrics. Finally, a subset of bundles (CC_Pr_Po and IFOF) alongside the segmented lesions are presented in Fig. 10     subject of the MyeloInferno-MS-TR dataset showing how the bundle are able to cross MS lesions as long as the diffusion signal is not altered in those regions.
Quantitative average results showed that FIESTA yielded the best Dice, voxel-wise bundle adjacency, and streamline density correlation or is in the margin of error of the best values (streamline-wise bundle adjacency, ICC length, and ICC volume). The voxel-wise bundle adjacency shows that, on average, two binary masks of the same bundle from the same subject are ∼0. 5 mm apart using FIESTA. This value is minimal considering that the acquisition resolution of the dataset used is 2 mm isotropic. Also, variability induced by registration, necessary for similarity metric computations, did not overly affect the voxel-wise bundle adjacency average distance. Higher density correlations obtained by FIESTA might be explained, in part, by the use of generative sampling. Interestingly, a closer analysis of the source of variability in FIESTA reveals that it is mainly due to the MRI field of view positioning (c.
Data collection and ethics The DCE was embedded within the third wave of data collection of a longitudinal cohort study of 600 FSWs in Dakar, Senegal, noted earlier, and was carried out from 29 June to 28 July 2020. Data was collected face-to-face by trained enumerators in private rooms located near four of out five of the STI health centres in Dakar, Senegal (Pikine, Rufisque, Mbao, and Sebikotane). These STI health centres are now referred to as the study sites. The cohort study follows FSWs in Dakar, Senegal and it was conceived to document and understand the socio-economic and behavioural determinants of risky sexual behaviours among FSWs. The cohort study began in 2015 (wave 1) and wave 2 was conducted in 2017. The aim of the third wave of data collection in 2020 was to define a policy change to address the main barriers of registration, increase the benefits of registration, provide evidence on the feasibility and to evaluate the effectiveness and cost-effectiveness of this policy change.
Experimental design The five chosen attributes and their corresponding levels (shown in Fig  1 ) have a possible 4 2 2 3 = 48 different combinations of outcome scenarios (two attributes with four levels and three attributes with two levels). All potential scenarios were not presented to each respondent due to likely respondent fatigue and low response rates. Using Ngene 1. 2 software  [13] , a fractional factorial experimental design was used to reduce the number of scenarios whilst maximising the variation in the data. An efficient design was used, allowing for attributes to be independently varied over scenarios while minimising the standard errors of the parameter estimates. Specifically, we used a D-efficient design in which the D-error is minimised  [13]. The final optimal design included 20 choice pairs.
This included the testing of the pictures that illustrated each attribute and their corresponding levels as shown in column (3) of Fig 1. The DCE was piloted in two phases, initially to 10 participants. The first version was well understood by participants, so no changes were made. However, we piloted the DCE on an additional 7 participants in order to collect enough data to use as priors to generate the final efficient experimental design using Ngene 1. 2 software  [13]. Unexpected priors were highlighted during this stage and qualitative research was undertaken to shed light on them. For example, some participants who were not registered had a registration booklet. Participants were found to be sharing registration booklets and using out-of-date registration booklets to reduce the risk of facing imprisonment when working as a non-registered FSW.