dataesr/scientific-paragraphs-categorization huggingface dataset:
Although cooperation is prescribed in protocols, it is rather rare in practice. Experts in most locations reported negative experiences concerning the absence of crosssectorial cooperation, its ad hoc character and the absence of information flow. There is no cross-sectorial or national informational database that would document and preserve information about DV incidents. Each frontline responder (police department, social service, guardianship office, shelter, etc. ) from each sector has its own records that is typically not transparent to other frontline responders at different locations or different sectors. FLRs from various sectors cannot see the DV history of their clients. A national database and system of information that records relevant data about victims, offenders and DV incidents (e.
Following this report, a Governmental Order was approved (Council of Ministers Resolution no. 139/2019, 19 th August), defining the priority actions to be developed, as well as other measures to be taken in areas such as health, education and social security. Among measures determined, it should be underlined: a) the implementation of the database on violence against women and domestic violence (to improve official data in this domain); b) the elaboration of a joint training plan of professionals regarding Violence against Women and DV; and c) a guide of functional procedures to be followed within the 72 hours after the report by LEAs. In accordance, at the end of June 2020, the Portuguese Government presented four new instruments to prevent and fight DV: - -Handbook on the Minimum Requirements for Primary Prevention Programmes and Projects of Violence Against Women and Domestic Violence (Portuguese version available from https://bit.
g. ROBOCOP); only in a few cases do they exist to enhance intra-agency cooperation. Of course, the usual form of collaboration is the face-to-face gathering of professionals/frontline responders involved in the management of a particular case of domestic abuse. Examples of such meetings are MARACs and MATACs (e. g. Brooks-Hay et al. , in this book 5 ; Jaffré, 2019; Robinson, 2006). Depending on the way such a gathering is established, clients can also be present. In most of these meetings, all professionals present record the decisions and details of the meetings in their own respective databases, not a shared one. This is perceived as effective by most of the professionals because professional cultures are real hurdles, as one quote from a German interviewee shows: "We do not have something like an online platform for our network.
For the evaluation, the piloting team completed an online survey provided by IMPRODOVA researchers  (Juszczyk & Pfleiderer, 2021). The survey focussed on the usability and quality of stashcat® and led to a set of recommendations. Along with the stashcat® app, an accompanying webpage  16  providing training materials with regard to domestic abuse management in the Slovenian language was evaluated (for more details, see Jyszczyk & Pfleiderer, 2021). In general, stashcat® received a positive rating from the respondents. Respondents reported that stashcat® was on average straightforward to use, information was easy to locate and that its functions were working as expected and easy to understand. In this respect, however, police officers rated the survey items more positively than social workers.
Concept of the training platform on DV We decided to present our teaching material in the form of building blocks with various modules being made available to the different stakeholder groups -police, health sector, social sector -and materials tailored to the situation in different countries. The teaching concept involved two pillars: the training platform could either be used as an online self-learning tool to train oneself individually combined with knowledge assessment, case studies and training videos or as a source for training materials on DV for trainers/teachers who want to conduct a training on domestic violence on their own. For the building blocks, partners of the IMPRODOVA consortium were asked to provide country-specific information and case scenarios and knowledge assessment questions by completing a template with several feedback cycles to further optimise the provided information.
Training modules of the platform Based on the needs identified through the IMPRODOVA research and requests from frontline responders, the platform includes a section on DV data and statistics, as well as seven modules focused on the topics most relevant for frontline responders. (cf. Figure  2 ):  Module 1 aims at a better understanding of Domestic Violence, its forms, and consequences. Knowledge is transmitted about the specific context and the impact of domestic violence that can be a helpful step in understanding the individual needs of victims.
Using gender-sensitive language can make it easier to see important differences between male and female victims' needs, challenge unconscious assumptions about gender roles in society, and make victims more comfortable with a disclosure  (EIGE, 2019). Therefore, aspects of gender-sensitive communication should be included in all training on DV. Pfleiderer & Sondern (2020) recommend based on the IMPRODOVA research results that the following sex and gender aspects should be included in risk assessment tools, procedures, and trainings: -As victims could be financially dependent on their partners and therefore do not present all information about violent incidents, the victim's financial situation and current work situation should be assessed independent on the sex of the victim. -The past sexual history of the victim (e.
g. , frequent change of sexual partners, the way a victim is dressed) should not influence the risk assessment of the frontline responders and therefore not be part in risk assessment tools. -Frontline responders should be aware that victims have gendered perceptions and conceptions that may lead biased interpretations and might influence the information that are released by the victim. -As victims have their own assumptions about frontline responders' genderbased attitudes, frontline responders have to reassure victims to share all information without a fear of being judged or moralised. Based on these recommendations, gender norms and perceptions that were identified in the IMPRODOVA research were integrated in all IMPRODOVA instruments and guidelines. In particular, Module 1 on the IMPRODOVA training platform (www.
14. The EU and the Member States should promote and fund surveys that can be repeated every few years to measure developments over time. This recommendation concurs with the FRA (2014) recommendation on this issue, and its adoption would signify a concerted effort to uncover information on the extent and nature of domestic violence. 15. Alternative methods of gathering and utilising data about the "bigger picture" of domestic violence should be considered, in addition to the use of administrative and conventional survey data. (Burman, Brooks-Hay, & Bradley, 2020)  Part of the IMPRODOVA project included the development of a risk assessment tool -Domestic Violence Risk Assessment Integration Module -RAIMO, which is described in detail in the chapter by Marianne Mela and Jarmo Houtsonen in this book.
For this reason, we developed a training platform on domestic violence (https://training. improdova. eu/en/) and supported it by analysing the possibilities of using the digital communication platform for interagency collaboration to manage domestic abuse  (Pfleiderer & Juszczyk, 2021). In addition to the domestic-violence risk-assessment integration module (RAIMO), the training platform is probably one of the more relevant and applicable results of the IMPRODOVA project, as it includes all first responders in training, whom we found to have a significant impact on the final success of responding to domestic violence. The importance of training is thoroughly presented by Bettina Pfleiderer and Paulina Juszczyk in this book.
-2013 -Amendment in the Penal Code (explicitly inclusion of dating situations in DV). -2015 -Amendments in DV Act (DV database; homicide reviews, risk assessment, safety plans…). -2018 -Amendment in the Penal Code (Law no. 59/2007, 4 th September) -DV aggravating circumstances -the inclusion of dissemination (through social networks or other means) of data related to the privacy of the victims. Currently, the definition of the DV crime under Criminal Code is: -2021-Amendments in the Penal Code and in the DV Act (Law no. 57/2021, 16th August)-Penal Code: introducing the economic/patrimonial violence in legal typification of DV and defining that children dependent of the perpetrator or dependent of other victim of DV (provided for in the paragraphs a, b or c) are also considered victim of DV even if doesn't coabit with the perpetrator.
In these four setups, σ 2 BV mat remained constant, whereas σ 2 BV dir was either equal or twice as high as σ 2 BV mat. In addition, the genetic correlation between direct and maternal effects ( r BV dir ,BV mat ) was assumed to be either zero or negative (-0. 50) in the base population. All these values are in the range of estimates for various honeybee traits and datasets  [26, [31] [32] [33].
Although theoretically well-founded, Moritz'  [20]  approach should rely on robust estimates of inbreeding effects, that require both large datasets as well as estimates obtained for different honeybee populations with diverse origins and breeding histories and for diverse colony traits recorded across various environmental conditions  [18, 19]. To our knowledge, there is only one study that has quantified inbreeding depression for several colony traits recorded by German and Austrian honeybee breeders  [46]. Omholt and Adnoy  [22] , on their side, derived for 20 generations of selection the expected occurrence of homozygotes at the csd locus in the brood, corresponding to diploid males. They showed that mass selection increased the occurrence of diploid males by 25% compared to within-maternal line selection.
The paper develops a new conceptual basis, introducing People, together with Food, Energy and Water, as factors of the UA Nexus. A methodological approach for its assessment is presented, aimed at measuring not only resource efficiency and food production but also motivations and health benefits, as well as the ecological awareness of urban farmers. The methodology is based on a combination of methods such as diaries of everyday UA practices, a database of UA activities, life cycle assessment (LCA), and material flow analysis to connect investigations developed at a garden scale to the city scale.
They are useful in gathering data that are rarely available: the Harvest-ometer, for example, is a database with records of yields of some of the many growing spaces in the UK that can be used to understand the quantity of food produced within the city as well as the variety of plants grown. With methods pertaining to urban metabolism, these data could be used to estimate material and social flows at a city scale and elicit correlations between such flows. As an initial step of the FEW-meter project, a nested scale approach of analysis was identified as appropriate: from farm-to city-level. This entails working with farmers to gather data from each UA case, analysing data collected from a pool of case studies, and using this analysis to perform a material flow analysis at a city scale. The resulting assessment tool is structured around the four steps of the urban metabolism assessment as follows: (1) Process Analysis (identification of the methods and of the indicators representing the four elements of the UA nexus); (2) Accounting and assessment (data collection and analysis); (3) Modelling structure and function (material flow analysis and/or life cycle assessment); and (4) Optimisation and regulation (summarising findings and producing recommendations for urban policies) (see Figure  1 ).
2015). This paper aims to repeat and build upon these analyses. The large amount of information available from the spectroscopic follow up of X-ray selected galaxy clusters allows for novel methods of calculating Δ as a function of distance from the cluster centre. The outline of this paper is as follows; in section 2 the SPIDERS catalogue is introduced and the properties of its clusters are discussed, including three different definitions of the centre of a cluster, then the data reduction process is presented. Next in section 3 the various contributions to Δ are described, before its variation with distance from the centre of the cluster is predicted for two theories of gravity in section 4. In section 5 Δ is found for each of the three centroid cases from observations of galaxies and clusters in the SPIDERS catalogue, and compared to the predictions.
The most recent catalogue, Data Run 16, comprises a subset of clusters that were identified in the CODEX program  (Finoguenov et al. 2020) , which searched ROSAT All Sky Survey data for extended X-ray sources. Data Run 16 contains 2740 clusters with close to 42 000 galaxies with spectroscopic redshifts. There are a number of parameters measured for each cluster, including the virial mass  200 estimated from its velocity dispersion, and both  200 and the X-ray luminosity  X are iteratively calibrated using an  200 - X scaling relation  (Capasso et al. 2020). Also provided within SPIDERS are three potential definitions of the centre of a cluster, each of which has been used for an independent measurement of Δ.
Data reduction Table  1  demonstrates the different conditions imposed on the raw SPIDERS DR16 dataset in an attempt to obtain an uncontaminated measurement of Δ. The effect of each condition, isolated from all the others, is also shown. The choice of a redshift uncertainty limit of  z < 0. 0002 includes 98. 5% of the population, and removes the  upper tail of galaxies with large redshift uncertainties. There are also other, more subtle effects not included in the table. For the BCG and optical case 2512 galaxies are automatically removed so the central galaxy of a cluster is not compared with itself, furthermore in some cases the BCG or CG has a redshift uncertainty ≥ 0. 0002, and so that cluster is not used in the analysis, since all the velocity offsets could potentially be biased. For the X-ray case, 5 604 galaxies within R ≤ 0.
2 are removed to find the averaged central redshift of the cluster. Finally, the restriction of there being a spectroscopically observed galaxy within R ≤ 0. 033 of the optical centre also removes some clusters from the optical analysis, resulting in an extra 4 405 galaxies removed. The net effect of these conditions and those in Table  1  is the removal of 22 539, 20 563 or 17 052 galaxies in the X-ray, optical or BCG centroid case respectively, from the original dataset which contains 41 663 spectroscopic redshifts. The remaining number of galaxies is greater than 10 000 in all cases, and so should yield a small enough standard error on Δ to resolve it from zero effect. High resolution X-ray data are needed to determine whether the large centroid offsets in Fig. 2  are caused by clusters being in a disturbed state, something which could in principle bias the gravitational redshift signal.
If most other alternative theories have very similar predictions of Δ( R) to GR, then because it is such a small effect with often large uncertainties, the efficacy of the method may be limited. SDSS-V using eROSITA X-ray data  (Kollmeier et al. 2017 ) promises more galaxy clusters with lower masses up to larger redshifts. More clusters means a better constrained Δ, and better prospects for using this signal to distinguish between theories of gravity. eROSITA will also have much better X-ray resolution, giving more localised X-ray central positions. The 4MOST eROSITA Galaxy Cluster Redshift Survey  (Finoguenov et al. 2019 ) aims to provide spectroscopic redshifts for ∼ 40 000 eROSITA galaxy groups/clusters, including their BCG and >15 cluster members for  < 0. 7. By combining an X-ray central position found from eROSITA data and the nearest 4MOST BCG or redMaPPer identified CG to this X-ray position, there is the potential for accurate identification of cluster centres, even in dynamic systems where simply using a BCG causes miscentring.
ACKNOWLEDGEMENTS We thank the referee for helpful suggestions leading to tests that improved the robustness of our results. CTM and CAC acknowledge support from  Liverpool John Moores University. JAP was supported by the  European Research Council  under grant no. 670193. AS is supported by the  ERC-StG  ' ClustersXCosmo ' grant agreement  716762 , and by the  FARE-MIUR  grant 'ClustersXEuclid' R165SBKTMA. DATA AVAILABILITY SPIDERS data are available through membership of the SDSS-IV UK Participation Group funded by  LJMU. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah.
QUESTEVAL is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward as it requires multimodal Question Generation and Answering systems on the considered tasks, which are seldom available. To this purpose, we propose a method to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. The resulting metric is reference-less and multimodal; it obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. We make data-QUESTEVAL's code and models available for reproducibility purpose, as part of the QUESTEVAL project.
Introduction Data-to-Text Generation (DTG) aims at generating descriptions in natural language given a structured input, e. g. a table  (Gatt and Krahmer, 2018). Reliability and precision of generated texts is currently regarded as a major issue in DTG  (Narayan and Gardent, 2020) , with experimental surveys showing that real-life end users of DTG systems care more about accuracy than about readability  (Reiter and Belz, 2009). Neural NLG systems are known to be fluent, but prone to hallucinations  (Lee et al. , 2018) , i. e. they tend to include nonfactual information. However, their evaluation remains an open research problem  (Novikova et al. , 2017). A recent approach, QuestEval  (Scialom et al. , 2021)  has shown significant improvement over standard metrics on Summarization tasks. To measure semantic matching between an evaluated summary and its source document, QuestEval relies on Question Generation and Answering (QG/QA) systems.
* Equal contribution 1 https://github. com/ThomasScialom/QuestEval QuestEval + Recall Q G Q A Questions (Q x ) Scores ( eq. 2 ) QuestEval scores Table  Table  description Precision Q G Q A Questions (Q y ) Scores ( eq. 1 ) Table  Table  description   As illustrated in Figure  1 , a QG system generates a set of relevant questions conditioned on the source document, which are then asked on its generated summary. Conversely, questions generated from the summary are answered using only the source input. If the answers provided by the QA systems are correct, the summary is deemed consistent with its source document. Can QuestEval be adapted for evaluation on DTG tasks? So far, QuestEval's QG/QA systems have been trained on a purely textual dataset, SQuAD  (Rajpurkar et al. , 2016) , which restricts the evaluation to comparisons between two texts.
Unfortunately, DTG inputs are of different modalities than text (e. g. structured tables). In the absence of specific multimodal-QA datasets, how can one obtain these multimodal-QG/QA models required for a data-QuestEval? To fill this gap, we propose an effective method for creating synthetic multimodal-QG/QA datasets, by relying only on existing, purely textual, QG/QA datasets. Trained on such synthetic multimodal datasets, QA and QG models can now be used in QuestEval, enabling direct comparison between an evaluated text and its structured input, removing the need for costly gold references. Furthermore, this method does not rely on any task-specific annotated QA dataset, which makes the approach general and suitable for any DTG task.
, 2019). Concurrently, a family of reference-less metrics has emerged in Summarization  (Chen et al. , 2017; Scialom et al. , 2019). The amount of information shared between two texts is measured by generating questions on the source text, and asking them on the evaluated text. In its recent extension, QUESTEVAL  (Scialom et al. , 2021)  was shown to overperform standard metrics in Text-vs-Text tasks such as the evaluation of summaries. Unfortunately, text-QG/QA systems are not usable off the shelf for tasks in other modalities which require multimodal-QG/QA systems. Further, there is significant variability in structures (e. g. tables, knowledge graphs, etc. ) and domains (e. g. biographies, sports, etc. ) across DTG tasks  (Gatt and Krahmer, 2018). Therefore, generalizing QUESTEVAL to DTG tasks relying solely on existing multimodal-QG/QA datasets is not a promising direction: the effectiveness of data-QG/QA models would be limited to the specific structures of the few existing data-QA datasets -e.
Reference-less multimodal Evaluation To make QG/QA metrics usable for reference-less evaluation on DTG tasks, specific QG/QA datasets are needed to train data-aware systems. Relying on an existing corpus is not generalizable: it is unreasonable to expect a multimodal-QA dataset for every DTG dataset. The annotation necessary to build such corpora is costly and time consuming. For this reason, we propose a general approach applicable to any DTG dataset, and requiring no annotated multimodal-QA dataset. The overall process entails four steps (illustrated in Figure  2 ): Step 1) Textual QG First, following QUESTE-VAL, we train a textual QG model on SQuAD. Step 2) Synthetic Questions Given the training set of any DTG dataset, composed of (structuredinput, textual description) pairs, we generate synthetic questions for each textual description using the textual QG (from step 1).
Step 3) Synthetic multimodal-Question dataset Each example in the training set is constituted of i) the source (i. e. structured data), ii) the textual description, and iii) the synthetic (Question, Answer) pairs generated during step 2. We can therefore match each structured input to its corresponding set of synthetic Questions & Answers to build a data-QG/QA dataset. Step 4) Multimodal-QG/QA model training The newly built synthetic multimodal-Question corpus is used to train multimodal QG/QA models. For QA, a source corresponds to the structured data and a synthetic question; the target is the corresponding answer. QG can be seen as the dual task of QA: any QA dataset can be used as a QG dataset by considering the question as the target. To learn representations from structured data, several approaches have been proposed -e.
Answer Similarity In Question Answering, answer correctness (i. e. did the system find the correct answer?) is traditionally measured via F1-score, as popularized by the SQuAD evaluation script  (Rajpurkar et al. , 2016). However, F1-score is based on exact-matching of n-grams and is not accurate when evaluating a correct answer that is realized differently from the reference (e. g. a synonym). This is especially concerning in DTG, where input tables often contain data that are not found verbatim in texts (e. g. "Place of birth: France" can be realized as "She is a French [. ]"). To deal with this issue, we move away from the F1-score and decide to measure answer's correctness via BERTScore  (Zhang et al. , 2020) , which compares the two contextualized representation of the compared answers.