dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, the trend remains as IAM << ELMAM2 $ MP. The magnitudes of the values of (r cp ) and r 2 (r cp ) for the C-C bonds from the experimental static MP model and the XWR method are consistently and significantly larger compared to the dynamic models and the MEM electron densities. For the polar C-O and C O bonds, the r 2 (r cp ) values approximately agree between the dynamic MEM results based on ELMAM2/MP and the static XWR results, but they are significantly too large for the static MP model. It was shown on a set of high-resolution X-ray diffraction datasets of amino acids and tripeptides  [Fig. 5 in Woin ´ska et al. (2017) ] that the XWR model is superior over the multipole model especially for the accurate description of polar bonds such as C-O and C O since for the amino acids and tripeptides the r 2 (r cp ) values were also too large sometimes by a factor of 2 or 3.
3. 4. 4. The p-hole carbon bonding interaction. The 3D static deformation density from the experimental multipole model, the ELMAM2 database transferred model and XWR are shown in Fig. 10 along with the 3D dynamic deformation density maps from the dynamic model density with the research papers Acta Cryst. (2022). B78, 231-246 Rumpa Pal et al. Co-crystal hydrate of gallic acid and pyrazine 241 Table 7.
INTRODUCTION Network analysis is becoming an established methodology across many disciplines such as biology, the social sciences, and the humanities. While the specific datasets are diverse and heterogeneous with respect to how they have been captured and what research questions they aim to address, they have in common that the analyst thought of their data as a network-i. e. , as nodes and links. By applying methods from network analysis, such as calculating metrics or producing a visualization, the analyst aims to learn something about these nodes and their relationships. Hence, network analysis is a tool and a lens to model and interrogate data. It comes with assumptions, such as that a network is a meaningful representation for the data. And, it involves multiple steps in defining nodes and links, formatting data, applying visualization, and interpreting these visualizations correctly.
Especially for novice analysts, performing these steps and making the respective decisions can pose problems. Over several years, we ran workshops, tutorials, talks, and interdisciplinary collaborations on network visualization and built our own network visualization tool, the Vistorian  [3, 7]. During this time, we experienced first-hand some of the problems, challenges, and misconceptions that can hamper engagement with network analysis and visual network exploration. Many of the issues we encountered were very common, such as identifying the meaningful entities (nodes, links, attributes, time, geographical locations) from the initial dataset-e. g. , a corpus of documents-that should represent nodes and links to create a network that meaningful to answering a specific research question. Other issues we observed were about using visualizations other than node-link diagrams to understand multivariate, dynamic, and geographic networks, or properly formatting a dataset for import into a tool.
For simplicity, we relied on a single tool, the Vistorian, that we consider a state-of-the-art network visualization tool comparable to Gephi  [9] , NodeXL  [71, 72] , or Palladio  [73]. The Vistorian has been specifically developed to provide an easy entry point to network visualizations (node-link, adjacency matrix, timeline, map) and interactive exploration for data analysts 1 without a technical background (Sect. 3). In a first log study (Study 1, Sect. 4) we tracked users of the Vistorian over several months with the aim of understanding how they use visualizations and interactions in their day-to-day work. To complement these anonymous data with qualitative data about potential barriers, we designed a 6-week network exploration course and delivered this to 36 analysts whilst closely monitoring their progress, giving personal advice, and engaging in individual interviews (Study 2, Sect.
That said, data-wrangling tasks such as re-formatting data and correcting errors in 'dirty' datasets can be a significant portion of data-analysis work  [41]. Dedicated tools can be used for data wrangling, for example, Ploceus  [46] , Orion  [36]  or Origraph  [14]. Our own network visualization tool, the Vistorian, is representative of these tools in that it creates interactive node-link diagrams visualizing link weight, direction, and type as well as node types, and can represent temporal and geographical data. It had been developed and iterated over with many domain collaborators and allows us to log interaction data during in-the-wild and day-to-day use. We were inspired by NodeXL's table format, which is easy to create and edit in common spreadsheet applications. At the same time, the Vistorian extends existing tools by providing alternative visualization techniques such as adjacency matrices (e.
However high-level these concepts seem, studies on teaching network science in high schools and public exhibitions  [18, 25, 79]  revealed how little is generally known about the basic concepts of networks, their analysis and visualization in the wider population. For example, when shown images of node-link diagrams, visitors to science museums often struggled to explain how these images would be interpreted  [18]. Courses and explicit training for network analysis  [25]  have been used in the context of MILCs to evaluate NodeXL and learn about teaching network exploration concepts  [17, 35]. These studies introduced graduate students (21 and 15 respectively) to Social Network Analysis and NodeXL and tracked them over 5 weeks. Students used demo datasets and smaller self-chosen datasets, described as discovery learning and exploratory learning in the paper.
Each row of a node table defines a node, and columns can specify the display name and node type. Locations in the link table can be expressed as place names (e. g. , Paris) and are geocoded using the MapTiler API  [48]. Upon upload, users can specify the network schema by defining a column's interpretation from a drop-down list for each column (Source Node, Target Node, Link Weight etc. ) Again, this feature was inspired by NodeXL. As soon as all required schema fields (only source and target node) for a network are specified, a status message above the table informs the user that the network is ready for visualization. Our website vistorian. net contains demo videos showing data upload and exploration, a detailed explanation on formatting data in node and link tables, links to fully-functional visualizations with a demo-dataset pre-loaded, and a FAQ.
Studying Real-World Usage of the Vistorian-While designed for analysts in the social sciences and humanities, it addresses a potentially wide audience. It has been designed and optimized with collaborators in these areas since 2014. It supports exploration processes that can easily take weeks, months, or years in which an analyst iteratively obtains new data, refines their research questions and explores their dataset from different perspectives. In such scenarios, an analyst's understanding of the dataset is constantly evolving alongside their analysis goals, questions, and methods. Data exploration is almost always a process that requires learning and mastering new methodological tools, including visual exploration. Understanding these processes and how to facilitate them is the main aim of our research.
STUDY 1: INTERACTION LOGGING & WORKSHOPS The first phase of our research focused on anonymously logging interactions and conducting workshops to gain a general understanding of how real-world analysts make use of the Vistorian and how to introduce analysts of various backgrounds to network visualization. Interaction Loggingwe anonymously tracked all interactions with the Vistorian. Over 10 months, we collected data on upload and schema specification, creating and interacting with a visualization, and requesting help. A pop-up informed people about the logging taking place, linked to information for study participants, and obtained their consent. This procedure was approved by our University Ethics Board. Logging started from the moment a user landed on the Vistorian website and continued until they closed all related browser pages.
Logs were collected using the Intertrace JS library  [29]. To facilitate the analysis of our interaction logs, we classified all log events, based on categories we defined in advance. The main categories (the list of all categories is included in the supplemental material) included Data-Management events (e. g. , interactions such as creating a new network, uploading data tables, updating table entries, specifying table schema), Visualization & Interaction events (e. g. , any interactions with visualizations), Support & Communication events (e. g. , engagement with the Vistorian help resources such as manuals, demo data and videos), and Bookmark events (e. g. , interactions related to the creation, update, and usage of bookmarks and annotations). Workshops-While still developing the Vistorian platform, we ran a number of public workshops that varied in length from half a day to three days.
5). Findings from Interaction Logs & Workshops-Over the course of 10 months we logged 534 individual users sessions which we analyzed using the interaction categories described above, looking for interaction patterns (i. e. , interaction times, what parts of the Vistorian platform people engaged with and in what order, and the number of returning users). Based on our analysis, we identified four types of usage patterns: Demo-users (298/534 ≈ 55%) solely played with the demo dataset or looked into help resources, without exploring their own data using the Vistorian platform. Data-strugglers (71/534 ≈ 13%) did try to explore their own datasets using the Vistorian platform, but struggled with shaping their data and creating their networks. These users sometimes returned to the platform several times, but without managing to successfully create a network visualization.
We encouraged participants to bring their own data and related questions/issues/problems to the course, so they could directly apply lessons-learned in a real-world context relevant to them. However, we also provided demo datasets to those who did not (yet) have their own data. Each week consisted of a two-hour session that combined online live lectures (recorded and shared online) with hands-on activities Some sessions set a homework for the following week, to encourage participants to practice and identify the areas that they needed help and support with. We offered weekly one-hour drop-in sessions during which course participants could ask questions about the lecture material or raise issues they had encountered. We also moderated a Slack channel that allowed participants to discuss course-related questions and problems with each other and with the course instructors.
We provided respective Python scripts and assisted participants with applying them to their data. Activities included loading the finalized data tables into the Vistorian using the import wizard. Week 4: Network Exploration Using Node-Link Diagrams introduced node-link diagrams and related interactive features. We presented different exploration tasks  [44]  in the form of questions, e. g. , What to observe about network structure?, What does that mean for the data?, or to interactively explore a network's change through time. Week 5: Network Exploration Using Adjacency Matrices introduced adjacency matrices. We explained the visual encodings and how the node ordering is aiming to optimize for visual patterns in the matrix. Week 6: Timeline, Maps, and Coordinated Views introduced timeline visualization and the geographic network visualization techniques, as well as the coordinated view feature in the Vistorian.
Data Collection & Analysis Our findings are based on the following data collected across the course. Questionnaires. We asked all participants to fill out questionnaires prior to and after the course to capture their starting points and expectations when entering and their insights and reflections after the course. We received completed pre-questionnaires from 36 participants, and exit questionnaires from 8 participants. Interviews. We invited all 36 course participants to a semi-structured interview where we asked them about their initial motivation to take the course, their lessons learned, to what extent they managed to create visualizations with the Vistorian, what strategies they applied, and what they found the most challenging about the course and the visualization process. To learn more about the Vistorian, we also included questions about which features participants most appreciated, which ones they found most difficult to use, what additional features they would like to see added, and how they would overall describe their workflow with the Vistorian (See the supplementary material for the list of interview questions).
13 participants followed our invitation to be interviewed. All interviews were automatically transcribed prior to their analysis. Course Recordings & Online Discussions. All course sessions were recorded, and we took notes of comments, questions, and discussions that drove the tutorial and drop-in sessions. We also captured inquiries and discussions on the course's Slack channel, where participants would ask questions about the course content and ask for help on their network visualization processes and challenges. Course recordings were not fully transcribed, but reviewed to complement our written notes. Our analysis across this variety of data largely follows a thematic analysis approach  [21, 34]  where one author qualitatively coded participants' statements and the emerging coding was then discussed and iteratively refined between a team of three co-authors.
Initial Participant Goals Statements from the pre-course questionnaires show that participants' motivations for engaging in network visualization varied in terms of the specificity of goals, exploration questions and the data at hand. G1: Learning About Network Visualization To Inform Data Collection. Approximately 22. 2% (8/36) of our course participants were still in the process of collecting data when entering the course or planned to start with the collection. Their main goal was to learn about network visualization and analysis in order to inform their data collection processes and questions of potential interest: "I am preparing for network data collection but would like to see what can be analyzed before deciding on types of network data to collect. "[P-SupplyChain]. G2: Exploring Data to Help Define Questions.
Participant Course Journeys While all active participants (24/36) were able to create at least one network, they followed a range of course journeys, influenced by their data and initial goals. In order to anchor our findings on network exploration strategies and potentially hampering barriers, we illustrate the different types of journeys based on selected participant profiles. J1: Vistorian Exploration using Demo Data. Participants who did not bring their own data to the course explored the features of the Vistorian based on the demo datasets provided, to learn about network visualization (G1). For example, P-UrbanStudies an engineer with a social sciences background and experience in network visualization was "interested in learning about timeline and geographic map networks, plotting adjacency matrices, and learning what Vistorian is about.
"before I explored my network, I supposed that the institution [X] was the most influential actor in my network, but it was  [Y]. "[P-Letters]. J3: Independent Network Exploration. At least 5/24 participants actively and without assistance explored their data from the different perspectives offered by the Vistorian. For example, P-Business(G3) (computer science, social sciences, humanities; experience in network vis but not with the Vistorian) wanted to explore how funding rounds of commercial businesses assist in the growth of industries and business networks, and how the people behind businesses are linked. To explore these questions, they created and iteratively refined a number of temporal and geographic network visualizations and adjacency matrices. P-RocksStudies (G2) (Natural Sciences; some experience in network visualization) had a dataset describes studies and methodologies about a specific phenomenon related to rocks.
It can blind participants to alternatives, and hindered them from exploring other mappings of nodes and links that could express different information about their data or support more specific goals. This reflects findings about latent abstractions  [15] , i. e. , participants not having clear abstractions of their data, such as networks, tables or timelines. For example, P-Carers collected data about social relations between 80 patients and their carers from interviews. Initially, P-Carers wanted to create an ego-network centered around each patient. However, discussion and initial network visualizations revealed that the patients were not actually that relevant to the network, as P-Carers was interested in the types of carers and their relations through the act of caring. In other words, the collection of individual ego-networks had to be re-imagined as a network of relationships between roles of carers.
Defining and Formatting Networks 40% of our participants (in particular those with less specific questions or goals) were initially unsure about the aspects in their data that should form the nodes and links in their network(s). When asked about particular challenges they faced during the course, one participant highlighted the difficulty of defining the structure of their network data: "deciding which and how information should be included prior to exploration [before having seen the network visualized]. " During the course, we facilitated this abstraction process using sketching exercises that asked participants to envision possible node and link (Sect. 5. 2, Weeks 1&2). To a certain extent, this approach was successful enabling active participants to create at least one visualization of their dataset.
For example, P-Carers had multiple instances of the same family doctors in their data as these doctors were connected to several patients. Having different node instances representing each doctors made it hard to identify roles of high importance. They, finally decided to use a single node to represent each carer role, e. g. , 'family doctor'. Similarly, P-Laureates, working with a dataset of laureates across multiple years, aimed to find those who shared the same types of awards. They first decided to represent awards in each year as individual nodes. However, it turned out that this offered a limited understanding of laureates across years. In a subsequent iteration, P-Laureates therefore represented each award title (regardless of the year) as a single node. Creating one node per year and linking to the laureates in that years would help P-Laureates answer their question about when the same person had received the award twice.
For example, P-Festivals found that the same venue recruited certain ensembles multiple times for different events. P-Documents's data included labeled documents that included multiple co-occurrence links with other documents. During the course, we provided technical support (in the form of dedicated Python scripts) to help participants explore duplicate links and, based on their goals and questions, transform these meaningfully (e. g. , aggregating data points to be represented in the form of link weight) (Sect. 5. 2, Week 3). This helped several participants reduce the size of their networks and highlight important patterns in their data without affecting the nature of the network.
g. , identifying potential nodes and links on paper, sketch potential outcome visualizations based on this, and formalize outcomes of these processes as concept maps; see our course activities in Sect. 5. 2). However, there are still open questions about how such activities can be integrated into network visualization tools. B4: Choosing The Right Level of Abstraction (6)-The initial network visualizations of participants not always yield insights, because an intuitive mapping of domain concepts to nodes and links led to visually cluttered networks due to too many nodes or edges (or both). Participants struggled with introducing the right level of abstraction to create more meaningful networks. We define abstraction as the process of deducing a new network from the initial network that describes the entire dataset.
This notion of abstraction is different from Bigelow's et al. [15]  which refers to data abstractions as tables, networks, timelines or alike. Mitigation Strategies-In the course, we discussed options and gave participants access to dedicated Python scripts to facilitate network size reduction, as well as transformation and aggregation strategies. Tool support in this area exists (e. g. ,  [14] ), and, again, sketching can help identify goals and approaches to abstraction. B5: Choosing the Appropriate Data Formats (2)-Choosing the appropriate (machine readable) data format and transforming data accordingly (Step 3) can become a barrier through a range of issues (O6), e. g. ,  (1)  not understanding the data format required, (2) confusing different formatting options, and (3) inconsistent formatting.
• Support creative approaches to creating networks. For example, show possible networks derived from a given a "seed" network through aggregation, projection, and filtering. • Explain visual patterns and encoding as part the tool, e. g. , using automatic annotations suggesting explanations for specific patterns or miniature galleries of visual patterns found inside a dataset. • Automatically recommend or highlight potentially insightful views and data pattern. Methodology, Limitations, and Open Questions-In-the-wild research is inherently challenging  [17, 70]. Our findings are situated in the specifics of our course and course participants, as well as the tool we used. Since analysts may have enrolled because they had particular conceptual challenges in network visualization, we cannot say how prominent our observed barriers are in other settings (e.
Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions.
For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes.