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317 results for “resource use”
FAO and SAGARPA. (2012). Baseline of the Natural Resources Sustainability Program. Sustainable Land Use Subindex - Calculation Methodology. Mexico City (53 pp.) (FAO y SAGARPA. (2012). Línea de Base del Programa de Sustentabilidad de los Recursos Naturales. Subíndice de Uso Sustentable del Suelo - Metodología de Cálculo (53 pág.). Ciudad de México)
The lack of soil data is a complication that most soil scientists will encounter throughout their career; this critical aspect is exacerbated due to the excessive cost of soil surveying. Consequently, it is essential to develop strategies that guarantee the permanent accessibility of past soil sampling efforts. The main objective of this contribution is to release an entire dataset of soil samples surveyed by the Secretary of Agriculture, Livestock, Rural Development, Fisheries, and Food (SAGARPA) in collaboration with the Food and Agriculture Organization of the United Nations (FAO) in the year 2012, the dataset consists of more that 4000 compound samples surveyed on managed cropland. SAGARPAS's main objective was to generate a new index to assess and monitor the current and future state of soil when sustainable soil practices take place. A complete set of physicochemical properties were determined via laboratory analysis for all record in the dataset, namely: pH, EC, OM, BD, P, Sand, Silt, Clay, Texture, N, K, Ca, Mg, Na, CEC, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), including the calculation of the Sustainable Soil Land Use Subindex (SSLUS). We presented a reviewed dataset with the potential to contribute to a large variety of studies, ranging from: agricultural pinpointing of the best conditions for crop production to digital soil mapping and modeling and agronomical studies. We also provide the original report on the developement of the dataset, indicating the names of creators and colaborators, as well as the methods of analysis and interpretation of the results. The new information is appealing for a wide diversity of users interested in soil traits across agricultural systems of Mexico.
SBC LTER: Beach: Sandy beach prey resource use by surfperch across tidal phase
These data describe trophic links between sandy beach and an associated surf zone fish species. The datasets are the result of a short-term study investigating the effect of tidal phase on a local sandy beach macroinvertebrate community and the diet of barred surfperch (Amphistichus argenteus) during the summer and fall of 2020. The beach invertebrate population dataset details the abundance and biomass of taxon within each beach intertidal zone across three paired neap and spring tidal phases. The diet datasets report the counts and sizes of prey taxon observed in barred surfperch stomach samples taken at the time of each beach sampling event. Data are contained in three tables: 1) the beach macroinvertebrate population data, 2) prey counts from barred surfperch stomach content samples, and 3) the sizes of prey in stomach samples.
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </p>
DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature
<p><strong>Datasets</strong></p> <ol> <li>DISTANT-CTO is a weakly-labelled dataset of 'Intervention' and 'Comparator' entity annotated sentences. The dataset was obtained using candidate generation the approach described in "DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low Resource Entity Extraction Using Clinical Trials Literature". <ol> <li>distantcto_high_conf.txt - ds conf 1.0 (full dataset)</li> <li>extraction1_pos_posnegtrail_conf09.txt - ds conf 0.9 (partial dataset)</li> </ol> </li> <li>The physio test set is a dataset comprising 153 PICO annotated randomized controlled trial abstracts from Physiotherapy and Rehabilitation. This dataset was used as an additional benchmark to evaluate the generalization power of the weakly annotated dataset and NER model for this sub-domain.</li> </ol> <p> </p> <p><strong>Utility</strong></p> <p>The dataset could be used as an input for training 'Intervention' named-entity recognition (NER) models.</p> <p> </p> <p><strong>Availability</strong></p> <p>This directory includes extraction1_pos_posnegtrail_conf09.txt - This text data file contains all the weak annotations (source intervention terms mapped onto target sentences) from clinicaltrials.org (CTO) with a confidence score of 0.9 and above.</p> <p>The directory also includes ‘physio_sent_annot2POS_posnegtrail.txt’ – This data file contains manually annotated (Intervention entity) data from the physiotherapy and rehabilitation domain. It follows a roughly similar structure as described in the ‘Description for long targets’ section. (‘Participant’ and ‘Outcome’ annotations are removed from this file)</p>
Hourly LC impacts - Resource use - minerals and metals - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Resource use - minerals and metals, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants
<p>Dataset of the PhD Thesis from Lucas D. Gorné:<br> - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.</p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes
Data associated with "Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes" (DOI: 10.3389/fmicb.2024.1452534). These data include soil resource measurements and various phenotypic measurements of associated Streptomyces isolates/populations. Specifically, these data note population level inhibition phenotypes according to Herr's Assays, isolate level antibiotic resistance phenotypes against 9 standard antibiotics, and isolate level resource use phenotypes quantified with Biolog SF-P2 96 well plates.
Data for: The interaction between metabolic rate, habitat choice, and resource use in a polymorphic freshwater species
<p>Raw respirometry data and respirometry code</p> <p>Data.xlsx is the data about each fish that was used for all analyses including Stable Isotope values, length, weight, sex, and habitat. This is the data that is used in the R code. </p> <p>Example code of the models used in our analyses</p> <p>TEF_metabolism.xlsx is data on the fish that were kept in the lab for almost a year. </p> <p> </p>
Medical Students use Online Study Materials more than School-Provided Resources when preparing for USMLE Step 1
<p>In this excel file contains the raw data collected from a survery sent out to students attend ULSOM and UNRSOM. The raw data was processed and analyzed in the sheet titled "graphs". </p>
Fig. 3 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil
Fig. 3. Diet composition of Plagioscion ternetzi by size class in Sinhá Mariana lagoon (a) and in Chacororé lagoon (b) (EIG: Eigenmannia spp.; PIM: Pimelodella spp.; ROE: Roeboides spp.; PCU: Psectrogaster curviventris; CDO: Curimatella dorsalis; BRA: Brachyhypopomus spp.; SMA: Synbranchus marmoratus; TAR: Tetragonopterus argenteus; HOR: Hemiodus orthonops; LOR: Loricariichthys spp.; SBR: Schizodon borellii; AST: Astyanax spp.; SMG: Serrasalmus marginatus; LEP: Leporinus spp.; OF: other fish; SH: shrimp; INS: insect; FR: fish remains).
Fig. 2 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil
Fig. 2. Diet composition of Plagioscion ternetzi in the lagoons Sinhá Mariana (a) and Chacororé (b), during the flood and dry periods.
Fig. 4 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil
Fig. 4. Species abundance curve of fish species at Sinhá Mariana lagoon (a = flood period and b = dry period) and Chacororé lagoon (c = flood period and d = dry period). Species within the rectangle correspond to the ten most abundant in the lagoon, in order of importance; the species out of the rectangle correspond to the most consumed by Plagioscion ternetzi.
Fig. 1 in Spatial, seasonal and ontogenetic changes in food resource use by a piscivore fish in two Pantanal lagoons, Brazil
Fig. 1. Location of the study area in the system of Chacororé-Sinhá and Mariana lagoons, Pantanal Matogrossense, Mato Grosso State, Brazil.
Fig. 2 in Relationships between morphology, diet and spatial distribution: testing the effects of intra and interspecific morphological variations on the patterns of resource use in two Neotropical Cichlids
Fig. 2. Head of Satanoperca pappaterra (a) and Crenicichla britskii (b), showing differences in the mouth protrusion.
Fig. 7 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 7. Spearman correlations between the distribution (percentage frequency) of guilds and scores of the first axis of the principal components analysis (PCA1) applied to the correlation matrix of abiotic variables. White circle = headwaters; gray circle = middle stretch; and black circle = mouth of the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil.
Fig. 5 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 5. Longitudinal niche breadth (B values) of each fish trophic guild in the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Aq = aquatic; ter = terrestrial.
Fig. 4 in Longitudinal use of feeding resources and distribution of fish trophic guilds in a coastal Atlantic stream, southern Brazil
Fig. 4. Mean ± standard error of diet breadth (B) of fish trophic guilds in the Vermelho River, East Atlantic basin, Antonina, Paraná State, Brazil. Aq = aquatic; ter = terrestrial. Piscivores and omnivores are not included in the ANOVA.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.