dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS In conclusion, we have presented a multi-atlas segmentation scheme implemented in our comprehensive AutoSeg segmentation software pipeline. Graph based clustering is employed to select the closest atlas per cluster for a weighted label fusion procedure. We validated this method on a publicly available dataset. The results show that the proposed method achieved comparable segmentation results to other existing multi-atlas segmentation methods for subcortical structures. Overall, AutoSeg provides the field of brain MRI studies with an automated multiatlas segmentation software pipeline for brain MRI neuroimaging studies.
ACKNOWLEDGMENTS This work was supported by the  National Institutes of Health  Grant Nos. R42 NS059095 ,  U24 NS059696-01A1 ,  HD-055741 ,  U54 EB005149  ( NA-MIC ),  R01 NS060722 ,  HD-003110 ,  North Carolina Translational and Clinical Sciences Institute   50KR71104. In this study, the  MRI  scans were selected from OASIS database, which was supported by  P50 AG05681 ,  P01 AG03991 ,  R01 AG021910 ,  P50 MH071616 ,  U24 RR021382 ,  R01 MH56584.
g. recovery of exploited stocks, surplus production)  [16]. These impacts may manifest themselves at an order of magnitude comparable to that exerted by fisheries pressure. Ecological/ecosystem modelling is a rich, well established research field; nonetheless, it is not always included in fisheries modelling and management  [16]. Whole ecosystem models try to account for all trophic levels in the ecosystems studied. Some of the most notable examples are ECOPATH  [17]  and ECOSIM  [18]. Currently, the most commonly used whole ecosystem modelling approach is probably Ecopath with Ecosim (EwE), a combination of ECOPATH, ECOSIM and a constantly increasing number of add-ons  [19]. A software implementation of EwE is freely available for evaluating ecosystem impacts of fisheries  [20, 21]. EwE modelling is dataintensive, especially regarding biomasses and diets, and its outputs require interpretation to be used for policy-making support, among other limitations  [21].
The framework intends to assess overall sustainability, yet emphasises its environmental dimension, mainly due to data availability. We consider the proposed coupled model as an example of ''ecosystem-based supply-chain modelling''. Moreover, the goals of both approaches differ as well: the value chain analysis in  [30]  accounts for socio-economic benefits of fisheries and subsequent links in the value chain, while our analysis compares the relative sustainability performance of competing fisheriesbased supply chains. In our framework, monetary flows are analysed at the industrial-segment level rather than at the value chain level; that is to say, no individual firms are modelled, but rather whole production sectors (e. g. fisheries, reduction industry, speciesspecific aquaculture sector) by aggregating and generalising individual firm results.
Supply chain characterisation and modelling The biophysical accounting framework used to model supply chains was Life Cycle Assessment (LCA). LCA is a mature approach, and current Life Cycle Impact Assessment (LCIA) methods encompass a great diversity of environmental impact categories. Socio-economic aspects would ideally be assessed by combining life cycle methods and economic analysis frameworks, such as Life Cycle Costing (LCC), Social LCA and cost-benefit analysis. Nonetheless Social LCA is not yet mature, and it is usually difficult to obtain all the data required from fishery and fishfood industries to apply it  [101, 102]. Not enough data were available for LCC or cost-benefit analyses. Several LCA studies were required to characterise environmental impacts and resource consumption (including energy use) of components of fish supply chains: fisheries, processing for DHC, reduction into fishmeal and fish oil, aquaculture and distribution.
LCAs were performed using the software SimaPro  [103] , which features integration with the widely used database ecoinvent  [104]  and various LCIA methods, including CML baseline 2000  [105] , ReCiPe  [106] , Cumulative Energy Demand  [107]  and USEtox  [108]. LCA methodology and results associated with anchoveta supply chains are presented in  [39, 40, 42, 56, 63]. LCA results (including additional and fishfood-specific impact categories and other Life Cycle Inventory-based indicators), EwE outputs and socio-economic performance indicators become inputs for the Umberto modelling environment. Umberto outputs include mass and energy balances and flow diagrams (e. g. Sankey diagrams).
Definition and calculation of the indicator set Once the target supply chains are modelled based upon detailed operational and socio-economic data, a set of sustainability indicators is calculated to assess sustainability and compare alternative supply chains (e. g. DHC vs. IHC chains based on the same fishery). Several sustainability indicators were selected from the large indicator pool available in the literature so that all aspects of sustainability -especially the environmental dimension, but also energy efficiency, human nutrition and socio-economic factorswere addressed. Main criteria for this selection were historical use in the fishfood research field; purpose (mainly environmental plus key socio-economic aspects); practicability, given data availability; and comparability with other food systems.
Data sources and methods Establishing inventory data for LCAs was the most dataintensive endeavour in this study. Most background processes had been previously modelled in ecoinvent and reference publications. Data were collected in Peru from 2008-2013 in the ANCHO-VETA-SC project, in cooperation with PRODUCE, IMARPE, the Research Institute of the Peruvian Amazonia [114], a trout development project from the Puno regional government  [115] , Peruvian universities, various large fishing and reduction enterprises -organised into the National Fisheries Society  [51] -, as well as many confidential and anonymous sources. Detailed statistics and operational data about all key links in the complex anchovetabased supply chains were gathered. Moreover, experts and analysts of the anchoveta industries were also approached, and historical datasets obtained from them, some including data from a large enterprise no longer in operation but whose vessels were operated by other companies.
Surveys were extensively used to obtain data, particularly from industrial and SMS fisheries. Field visits included fishing ports, fishmeal plants, fish processing plants, aquaculture farms and shipyards. Details about all data sources used are presented in  [39, 40, 42, 56, 63, 81]. A screening-level LCA (Life Cycle Screening, LCS) of the industrial hake fleet was performed using literature data and landings statistics from PRODUCE and IMARPE (R. Castillo, pers. comm. , 2013; R. Adrien, pers. comm. , 2013). This screening relied heavily on assumptions, since detailed data on Peruvian hake fisheries was not available. Based on these uncertain data, sustainability indicators were calculated so as to compare the fishery of this carnivorous fish with those of anchoveta and another carnivorous fish (farmed trout), as well as their respective products.
The one-way coupling between the EwE model and the MFM, built with Umberto  [100] , is monodirectional, since dynamic linking was not feasible. EwE outputs are inputs to the MFM model, but changes in the MFM model cannot influence the EwE model directly. Therefore, the one-way coupled model was used to model the current situation and alternative fish-exploitation scenarios. Nonetheless, the MFM model can be used alone, as a supply chain modelling tool to explore variations within a defined scenario (e. g. changes in relative production volumes of aquaculture products or anchoveta DHC products). The Umberto project file containing the MFM model is available upon request to the corresponding author. For the alternative exploitation scenarios, changes in the percentage of anchoveta landings destined to DHC and in aquaculture production were modelled for future years by extrapolating historical landing and production data  [116, 117]  using statistically representative trend lines.
Methodological choices The use of trophic level (TL)-based ecological indicators is suitable for Peru because its fisheries are fully-or over-exploited. TL-based indicators, under the fishing-down-the-food-web concept  [118] , represent a measure of ecosystem structure and functioning and thus can be used to measure state and trends. These indicators would be less useful in situations in which exploitation is still developing. Both 1) the proportion of anchoveta landings destined for DHC and 2) aquaculture production are expected to grow. Complete historical annual data was available until 2011, and both DHC and aquaculture production datasets indicated a growing trend until 2010. Nonetheless, in 2011 total anchoveta landings for DHC were lower than those in 2010, but it was not possible to predict a decreasing trend based on a single ''low-catch'' year.
Acknowledgments This work, carried out by members of the  Anchoveta Supply Chain (ANCHOVETA-SC ) project (?http://anchoveta-sc. wikispaces. com), is a contribution to the  International Joint Laboratory ''Dynamics of the Humboldt Current System'' (LMI-DISCOH)  coordinated by the  Institut de Recherche pour le Developpement (IRD)  and the  Instituto del Mar del Peru(IMARPE) , which groups together several other institutions. The authors are extremely grateful to  Dr. Michael Corson  ( INRA, France ) for his contribution to the language improvement of this manuscript.
This paper introduces Click'n'Cut, a novel web tool for interactive object segmentation designed for crowdsourcing tasks. Click'n'Cut combines bounding boxes and clicks generated by workers to obtain accurate object segmentations. These segmentations are created by combining precomputed object candidates in a light computational fashion that allows an immediate response from the interface. Click'n'Cut has been tested with a crowdsourcing campaign to annotate images from publicly available datasets. Results are competitive with state-of-the-art approaches, especially in terms of time needed to converge to a high quality segmentation.
The goal of an object segmentation process is to label all pixels of an image depending on whether they belong to a certain object or not. Typically, the output of such process is a binary mask whose white pixels represent the object and the black pixels correspond to the background. Our work explores how crowdsourcing can be used as a valid strategy for obtaining a large amount of high quality object segmentation results (masks), as long as the appropriate tools and data collection strategies are adopted. The main contribution of this paper is the design and test of an online web interface (Click'n'Cut) that is responsive to the users' interactions and guides them into collecting highly informative data. Our results indicate that this tool is competitive with other state-of-the-art interactive segmentation techniques, especially in terms of responsiveness.
Incentives for the workers may vary from an abstract call to help science, to a very accurate pricing policy. A popular initiative in this direction is LabelMe  [17] , an online platform that has collected a large amount of local annotations by asking volunteers to draw a polygon around the object. One of the most ambitious projects up to date is related to the Microsoft COCO (Common Objects in COntext) dataset  [11] ; this segmentation effort uses the OpenSurfaces interface  [4] , an open source tool based on polygonal segmentation. The crowd was also used in  [10]  to assess an interface aimed at choosing the best input modality among a bounding box, a sloppy contour or a tight polygon. The authors highlight that in crowdsourced campaigns the annotation time is the basic budget constraint, and that by automatically adapting the annotation mode to the image it is possible to optimize the quantity and quality of the segmentations.
A worker can also correct a wrong click by just clicking on it again to make it disappear. The Clear points button The interface is implemented using HTML5 and JavaScript on the client side, and Java on the server side. The server side handles the computation of the current best mask as well as the persistence of workers' interactions in the database. To compute the best mask with respect to a set of f foreground points and b background points, we adopt the following algorithm. For each mask m ∈ M CG, where M CG is the set of masks computed using the Multiscale Combinatioral Grouping algorithm  [3]  , we start by computing two scores f gm and bgm. f gm (resp. bgm) is the number of foreground (resp. background) points that are correct with respect to m. If there exists a mask m * for which f gm * = f and bgm * = b then m * is the best possible mask and this is the mask that will be shown to the worker.
EXPERIMENTS AND RESULTS The experimental setup used for evaluation has been proposed by  [14] : it consists of 100 objects to segment from 96 different images from the Berkeley Segmentation DataSet (BSDS)  [12]. Each object is described by a sentence in English and a binary mask is provided as ground truth. We augmented the dataset with 5 more images (from the PAS-CAL VOC dataset  [8] ) in order to introduce gold standard tasks that would allow quality assessment of the workers' performance while working on those tasks.
Quality of the traces Our first study focused on the quality of the collected traces. Crowdsourced traces are usually noisy due to several reasons, chief among them the misunderstanding of the instructions for the task, e. g. , selecting a larger/different region than the requested one. The quality of the users was estimated using five gold images from the Pascal VOC dataset  [8]. The comparison between the error percentage for the gold or test datasets (Figure  2 ) indicates that seven users (labeled 3, 5, 8, 10, 11, 18 and 19 in Figure  2 ) were successfully identified as unreliable because they performed worse than a 20% error threshold on both the gold and test images. It also shows that workers 12 and 15 performed significantly worse on the gold images than on the test images. The high error rates from users 3 and 19 were due to their opposite interpretation of the foreground/background clicks and masks.
Budget The budget in our crowdsourced task can be approximated from two perspectives: user time and money. Table  1  compares the necessary user time on Click'n'Cut with the data collected from other related publications. The first observation is the diversity of datasets used to solve interactive segmentation tasks prevents a direct comparison of the resulting values, with the exception of Click'n'Cut and  [14] , as detailed in Section 4. 2. 2. Our experiments also show that, given the same interface, experts tend to spend almost 50% more time than the crowd in generating the annotation, but that this higher dedication only produces a small increase in the quality of the segmentation. According to these data, the faster responsiveness of our system (already pointed in Section 4. 2. 2) seems to be confirmed with comparison with other solutions.
CONCLUSION This paper introduced Click'n'Cut, a web tool for interactive object segmentation designed for crowdsourcing tasks. The tool was tested with a crowdsourcing campaign to annotate images from publicly available datasets. Experimental results are competitive with state-of-the-art interactive segmentation approaches in the literature. Despite some noisy traces, due to low-quality input from a few workers, the presented online interface has proven to be effective in collecting object segmentation results for crowdsourcing tasks, especially in terms of fast convergence to high quality results. Moreover, when comparing the performance of experts and crowd workers on the same task using the same images, we have seen that using expert volunteers -at a cost of three times as much as crowdsourced workers -increased the average duration of the task by about 50%, while providing only a modest (less than 10%) increase in accuracy.
The provision of public information contributes to the enrichment and enhancement of the data produced by the government as part of its activities, and the transformation of heterogeneous data into information and knowledge. This process of opening changes the operational mode of public administrations, leveraging the data management, encouraging savings and especially in promoting the development of services in subsidiary and collaborative form between public and private entities. The demand for new services also promotes renewed entrepreneurship centred on responding to new social and territorial needs through new technologies. In this sense we speak of Open Data as an enabling infrastructure for the development of innovation and as an instrument to the development and diffusion of Innovation and Communications Technology (ICT) in the public system as well as creating space for innovation for businesses, particularly SMEs, based on the exploitation of information assets of the territory.
The Open Data Trentino Project has initiated and fosters the process of opening of public information and develops as a natural consequence of this process of openness, the creation of innovative services for and with the citizens. In this paper we present how our project acts on long-chain, from raw data till reusable meaningful and scalable knowledge base that leads to the production of data reuse through the implementation of services that will enhance and transform the data into information capable of responding to the specific questions of efficiency and innovation.
Introduction The process of opening the data in Public Administrations (PA) as a process of enhancement of public information implies a radical change to the data approach and work inside the PA. This takes time, an improvement of the data management methodology, creation of operational tools and providing a reliable space for sharing. Governments of various countries and administrative divisions thereof worldwide have nowadays been starting to release a huge quantity of datasets in the context of the Open Government Data (OGD) movement  (Ubaldi, 2013). The movement became a shared policy after the European Directive no. 2003/98/CE (Public Sector Information Directive), improved by the Directive no. 2013/37/UE. The first Directive has been transposed in the Italian regulatory system with the Legislative Decree no.
36/2006 and the second one still needs to be implemented. The main purpose of the PSI Directive is to enable the so called "data re-use", that means "the use by persons or legal entities of documents held by public sector bodies, for commercial or non-commercial purposes other than the initial purpose within the public task for which the documents were produced" (Directive 2003/98/EC). In the context of the Autonomous Province of Trento (PAT), a large, diverse and interesting collection of datasets are already published as OGD. New datasets are slowly becoming available and the existing ones are updated whenever needed for the purposes such as correcting mistakes and adding new data horizontally (as instances) or vertically (as properties). The data catalogue is also linked with the website of the Department of Innovation of the PA 2.
This department governs the process of opening new data and the dissemination of the so called data culture. This is an important result to reach both inside and outside of the PAT. People started understanding the value of publishing high-quality data and the power in the reuse of them. Linking data will highly foster the value of sharing data. It will also lead to a new kind of data-centric public bodies that will empower citizens and generate innovative services. Immense numbers of government datasets could open up new opportunities for application developers and trigger disruptive business models to come  (Ferro & Osella, 2013; Manyika e al. , 2013; Vickery, 2008). While quantity of such datasets is considered as satisfactory enough, quality (e. g. , correctness and vertical completeness) is yet to be improved  (Bohm et al.
I. The description of the adopted opening procedure as an integral part of the change management in a public administration. II. The implementation of a methodology for generating entity types  (Maltese, 2012)  out of published datasets to model data as entities  (Giunchiglia, 2012)  for facilitating an integrated, combined and extensible representation. III. The implementation of a procedure and the corresponding tool for dealing with unforeseen data (along with known ones) about an entity, taking into account the semantics. IV. Description of our experience in handling Open Big Data for building life style changing unprecedented (in the region) applications. The paper is structured as follows: in Section 2 we present the process that has been put in place within the PAT and the choices made.
Open Data Trentino Overview The Open Data Trentino 3 project was created under the push of the local government of opening their public information as expressed in the guidelines for the reuse of public data official document  (DGP, 2012). From the legal point of view, the process started with the adoption of the Provincial Law no. 16/2012 (LP 16/2012), on the promotion of the information society and the digital administration and for the distribution of free software and open data formats. The process continued by adapting and improving, for the local administration context, the state of the art of existing European good practices in matter of Public Sector Information (PSI) 4. The result was the drafting of the Open Data Guidelines (Resolution 2858/2012). The data hunting and publication process followed is a step by step, day by day, federated approach by involving the local authorities since the beginning by asking to every provincial department to open at least one dataset.
Although the direct engagement of any department is time consuming it has proved of being successful and brought the desired side effect of creating enough awareness to the whole public administration and to spread the change paradigm. Many aspects are involved in this process: from the data cleaning to the data modelling, from the privacy issues to the intellectual property rights, from the dissemination aspects to the process design. At the same time, we have focused at the creation of the Data as a Culture, by acting in several dissemination and educative actions internally to the institution and with a broader scope at the national and international level. The Dati Trentino portal is built over CKAN.
5 , an open source data management system started by the Open Knowledge Foundation and maintained by the CKAN community itself. CKAN is specifically designed to allow programmatic access, finding and retrieval of dataset metadata through web APIs to which we are currently contributing. For instance we developed dedicated clients to access datasets in two different programming languages. The first one, Ckan Api client  6 , is an open source Python based library used to add automatically harvested datasets into a standard CKAN platform version 2. 2. The second one, called Jackan 7 , is an open source Java based lightweight library with built-in support for provenance tracking to easily access the catalogue data from Java. As of October 2014, the government of the PAT published about 860 datasets in a catalogue made available under the link http://dati.