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3,480 results for “a value”
Application of GIS and Spatial Statistics in identifying the rental value of shop lot in a shopping mall based on location factor
<p>GIS Property Data</p>
SUVfdg: a standard-uptake-value (SUV) body habitus normalizer specific to fluorodeoxyglucose (FDG) in humans
<p>In PET, several different Standard Uptake Value (SUV) metrics have been proposed utilizing different normalizers in an attempt to take into consideration the patient to patient differences in radionuclide uptake due to differences in body habitus (body weight, surface area, lean body mass). These normalizers are to some extent aribitrary in that they are selected from the list of proposed body habitus metrics, none of which necessarily describes well the distribution volume into which a given radionuclide labeled compound distributes with the body. </p> <p>In this work we propose a new body habitus normalizer, SUV<sub>fdg</sub>, that is specific to the tracer <sup>18</sup>F-FDG and which like previously proposed SUV normalizers is a simple function of a patient's height and weight. Derivation of this metric assumed that absolute normal-liver FDG uptake rate is not itself a function of body habitus (i.e. is not correlated with height, weight, etc). The SUV<sub>fdg</sub> metric was tested in an independent cohort and shown to have little to no correlation with body habitus measures in normal liver, spleen and blood. When applied to normal brain uptake, it was shown to vary as a function of patient age.</p>
Data for a scoping study on the plastic value chain in India
<p>This is a dataset with the bibliographic information on the literature used for a scoping study on the plastic value chain in India.</p>
Sagittal Balance in Children: Reference Values of the Sacral Slope for the Roussouly Classification and of the Pelvic Incidence for a New, Age-Specific Classification
<p>Raw data of the paper entitled "Sagittal Balance in Children: Reference Values of the Sacral Slope for the Roussouly Classification and of the Pelvic Incidence for a New, Age-Specific Classification", published on Applied Sciences</p>
Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h
<p>This repository contains the raw data and processing Python scripts corresponding to the paper "Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h"</p>
Literature review on innovative, sustainable and competitive agrifood value chains
<p>This Literature review Data-set has been produced in the frame of CO-FRESH - D1.1. State of the art summarized in an overview of the key success factors (and their theoretical explanations) of innovative, sustainable and competitive agrifood value chains.</p> <p>Grand societal challenges, such as climate change, environmental degradation, food security, immigration, and digital transformation, urge agrifood value chains to embrace sustainability objectives and measures. This objective requires value chains actors from farm to fork to interact for environmental, social and economic improvement of the system. In this context, innovation, that has long been considered as a factor of growth and competitiveness, becomes an increasingly important factor to create value while addressing sustainability challenges. Therefore, the emergence of concepts such as sustainability-oriented innovation and sustainable innovation are undeniable results of this trend. However, our scholarly understanding of these emerging concepts can be improved. In addition, it has been acknowledged that sustainability outcomes cannot be gained individually and that collaboration among value chain partners is needed. Thus, collaboration is seen as an effective way to turn ambition and commitment into action in the journey toward sustainability.</p> <p><strong>The CO-FRESH project</strong> aims to enhance collaboration for sustainability-oriented innovation, by proposing tools for re-designing fruit and vegetables (F&V) agrifood value chains across Europe. To reach these objectives, Work Package 1 takes the lead in <em>Identification, Analysis and Design of Innovative and Sustainable Agrifood Value Chains</em>. The current report is the outcome of Task 1.1, a review of the state of the art.</p> <p><strong>Deliverable 1.1.</strong> presents the state of the art of the academic and grey literature on sustainability-oriented innovation (SOI) in agrifood chains, more specifically presenting key success factors and their theoretical explanations for innovative, sustainable and competitive agrifood value chains. The review, first, gives a clear definition of ”sustainable and innovative agrifood chains’’ in the CO-FRESH project, drawing on the literature about SOI. Second, the review explores different modes of collaboration that contribute to sustainability and innovation in agrifood chains. Finally, the review elaborates on the role of public policies in promoting SOIs in agrifood chains.</p> <p>The outcome of this review will be used in offering a list of search criteria to create an inventory of innovative and sustainable F&V value chains in Europe. In addition, the insights of this deliverable will be used for characterizing and assessing the F&V value chains and their success factors (Task 1.2). On the basis of this review and the assessment in Task 1.2, recommendations will be given for data collection on and redesigning of the CO-FRESH pilot cases.</p> <p><strong>Chapter 2 presents the main methodology applied in the literature review.</strong> A framework for guiding the review was designed around the key concepts of sustainability, innovation, value-chain and collaboration. The main questions were: How is SOI defined in the literature and how does it apply to the agrifood sector? What are the specificities of SOI in fruit and vegetable value-chains? How does inter-organizational collaboration relate to SOI in agrifood chains? How do public policies relate to SOI in agrifood chains? The agrifood sector was the primary focus, but perspectives from other sectors were brought in to complement our understanding. No limitations were put on the type of agricultural products considered. The disciplines of Business, Management and Economics were studied, but also Agriculture, Regional and Urban Planning, Environmental Studies. This process result in more than 350 references reviewed.</p> <p><strong>Chapter 3 addresses how sustainability-oriented innovation (SOI) is defined in the economics, business and strategic management literature.</strong> The chapter starts by reminding that sustainability in itself is subject to debate. In the agrifood sector, sustainability has long been addressed through the environmental lens, first at the farm level and then at the level of the value chain with the development of LCA methods. The concept has then evolved to encompass a more holistic view of sustainability taking into account social dimensions of food systems (like healthiness and relational proximity) and economic dimensions in value chains (like a fair distribution of value, and competition within and across value chains). Even when consensus exists on the sustainable development goals, the indicators developed by international organizations often differ from the ones that are of interest for corporates or developed by academics. From our review of the literature, we conclude that SOI can be defined according to the <em>direction</em>, <em>distribution</em>, <em>diversity</em>, and <em>degree of openness</em>.</p> <ul> <li>The direction of SOI refers to the three pillars of sustainability.</li> </ul> <p>While eco-innovation or social innovation put the emphasis on one dimension, SOI takes into account more than one dimension. As a result, SOI involves a higher level of complexity and more uncertainty on the outcomes of the innovation compared to conventional innovation.</p> <ul> <li>The distribution of SOI refers to the repartition of the benefits.</li> </ul> <p>Many authors argue that SOI should enable a fair benefit-sharing among the value chain actors. In addition to that, the outcomes of SOI do benefit not only the value chain actors but also external actors. For instance, a reduction of the use of chemical inputs on the farm results in cost reduction, thus an economic benefit for the farmer, while it also results in a reduction of greenhouse gas emission, thus an environmental benefit for society as a whole.</p> <ul> <li>The diversity of SOI refers to the type of innovation. Based on economics of innovation and innovation management literature, we rely on the following types of innovation in the CO-FRESH project: <ul> <li><em>Technical innovation:</em> The implementation of new or significantly improved goods and services, or new or improved methods of producing goods and services.</li> <li><em>Organizational innovation:</em> The implementation of a new routines, management structures and methods of coordination within or between organizations.</li> <li><em>Managerial innovation</em><em>:</em> A form of organizational innovation focused on the specific roles and functions of the manager, including the interpersonal roles, the informational roles and the decision-making roles.</li> <li><em>Institutional innovation</em>: A legitimate change in the cognitive, normative, or regulative rules of a social system</li> </ul> </li> </ul> <p> </p> <ul> <li>The degree of openness of SOI refers to the participation of different actors in the innovation process.</li> </ul> <p>SOI is a process that can rely on various degree of value-chain collaboration. In other words, a process that brings together value chain actors (and sometimes also external actors) toward a common goal and a common understanding of developing and implementing an innovation. These value chain actors share decision rights on a part of their individual activities. While SOI begins at the firm level in response to external stimuli such as legislation, it evolves in a pro-active change at the wider system level.</p> <p>In sum, we provide the following general definition of <strong>sustainability-oriented innovation: </strong>“A collaborative process of change directed at improving one or more of the three pillars of sustainability (i.e., environmental, economical and social), relying on a diversity of bundled innovations (notably technological, organizational and institutional), and whose benefits are fairly distributed along the value chain actors”.</p> <p>Acknowledging that collaboration is of paramount importance for SOI,<strong> Chapter 4 analyses how inter-organizational collaboration relates to SOI</strong>. In this report, the focus is on inter-organizational relationship and not on intra-organizational collaboration. The modes of inter-organizational collaboration in the agrifood sector include collaborations among value chain actors (such as joint ventures, cooperatives, licensing, contracting) and collaborations between value chain actors and actors in the business ecosystem, the technological innovation system or the socio-ecological system (including public-private partnerships and multistakeholder partnerships). In the agrifood sector, collaboration traditionally concerns knowledge exchange; production and distribution activities; new product development; and commercialization of products. As collaboration entails risk and loss of autonomy, organizations engage in collaborations only if there are clear benefits. Current motives for horizontal and vertical collaboration include strengthening of bargaining power; gaining economies of scale; accessing tangible and intangible resources; reducing transaction costs; sharing of risks; improving lobbying power and legitimization; and joint learning and innovation. Success factors of collaboration include trust, reciprocity, communication, partner compatibility, and the complementarity of resources.</p> <p>The remainder of chapter 4 explores<strong> how particular collaboration modes relate to SOI in agrifood chains</strong> and derives with six main themes.</p> <p><em>One,</em> <strong>cooperatives</strong> could enable collective investments necessary for SOI. Moreover, cooperatives could facilitate a reduction of the individual risk of farmers in SOI and provide the technical assistance and financial room for experimenting with sustainable agricultural practices.</p> <p><em>Two,</em> the dual role of <strong>contracts</strong> appears to be crucial for SOI in value chains. On the one hand, contracts facilitate adoption of new technologies and farming practices, that could lead to improved environmental sustainability. On the other hand, contractual arrangements are an organizational innovation that enable value creation and value sharing, that could lead to better socio-economical sustainability. Contracts in general facilitate incentive alignment, risk mitigation and benefit sharing; co-investment, joint-funding and protection of specific investment; socio-technical interactions and inter-organizational learning; and provision of credit, inputs and technical services.</p> <p><em>Three,</em> <strong>involving consumers and citizens </strong>is a strong driver of innovation and a move toward more responsible patterns of consumption. However, the match between citizen preference and consumer purchase behaviour is yet to be better understood. Consumer-driven initiatives such as community-supported agriculture, buying groups, and participatory guarantee systems are growing and have been described in the literature on short food supply chains.</p> <p><em>Four</em>, the advantages of <strong>multistakeholder partnerships </strong>appear to be evident for promoting SOI along the value chain. Actors and stakeholders in the periphery of a firm's stakeholder network (e.g., universities, civil society and public authorities) may be even more relevant for SOI than primary stakeholders.</p> <p><em>Five,</em> the relevance of <strong>open innovation</strong> and the collaboration among value chain actors for innovation is undeniable. Although open SOI capabilities comply with the traditional open innovation capability framework, the capabilities should be accustomed to the sustainability context and adapted to the diversity of sustainability as perceived by actors.</p> <p><em>Six,</em> <strong>digitalization</strong> has substantial influence on inter-organizational collaboration for SOI in agrifood. Digitalization influences collaboration modes in several ways: facilitation of information and knowledge sharing by overcoming material constraints; improvement of transparency and reliability; and intensification of the volume and rapidity of data collection and data analysis. However, digitalization has downsides such as increasing the power of dominant actors and the risk of exclusion of small-scale and computer-illiterate producers.</p> <p><strong>Chapter 5 analyses how public policies relate to SOI in agrifood chains</strong>. Among the most debated public policies addressed in the literature, the EU Common Agricultural Policy (CAP) is the most explored, especially with reference to the instruments provided within the framework of the two pillars of the CAP. With regard to policy instruments aimed at supporting SOI, most of the literature focused on financial instruments. However, some authors explored normative instruments, mainly with reference to environmental and phytosanitary regulations. There is little debate on informative policy instruments, studied mostly with reference to advisory services and educational campaigns. Another theme that found relatively modest debate was the impact of innovation on the social dimension of sustainability. Most authors focused on the environmental and economic impact of sustainability. The role of public policies to promote social sustainability along the value chain remains a key area of improvement, especially with regard to rural employment and livelihood. The review highlights that, in general, the investigation approach to explore the role of public policies in promoting SOI along agrifood value chains is often rather narrow and case study-oriented.</p> <p><strong>Finally, the report derives at two main conclusions. </strong>First, we draw <strong>lessons learnt</strong> on how collaboration and public policies relate to SOI in agrifood chains, and on the factors that strengthen SOI. Independently of the types of collaboration (within the value chain or between the value chain and the broader innovation system), this review reveals common factors that lead to successful collaboration for SOI. A prerequisite is for actors to share a common vision on the sustainability goals. In addition, the importance of relational factors such social capital, trust and communication was highlighted in all literature. The complementarity of resources between the actors is also a major factor for successful collaboration. Finally, the importance of combining different collaborations modes was revealed in our review, as well as the importance of enabling institutions and public policies. We also found that inclusive and evolutionary governance structures at different levels of the value chain promote collaboration for SOI. For instance, bilateral collaboration in a value chain for access to specific resources may be complemented with open innovation strategies that enable generating knowledge. Finally, this review principally focused on meso-level factors that can influences collaboration modes for SOI. Further explorations should focus on micro-determinants of innovation and the evolution of success factors of collaboration along the process of SOI.</p> <p>Second, our literature review has allowed us to present <strong>16 </strong><strong>recommendations for interventions</strong> for improvements in the pilot cases of the CO-FRESH project. These recommendations are clustered in three categories: relational, structural and operational, and strategic. Relational factors are about the (social) interactions between actors involved in the value chain and in the innovation process. Structural and operational factors are about structuring the agrifood chain, structuring the innovation process, and the operations in the value chain. Strategic factors include the goals that the actors in the value chain pursue, individually and jointly, and the translation of these goals into management decisions.</p>
Luftdaten.Info data set 2016 - 5min, 30min values
<p>The data set contains the normalized averaged 5 and 30 minutes value of the <a href="https://archive.sensor.community/">Sensor.Community - archives</a>, formerly Luftdaten.info, of the year 2016.</p> <p>The tar-archive includes the CSV-files, XZ-compressed. For each sensor and day, the averaged values are available and each CSV-file contains a header.</p> <p>For particulate matter data (sensors: PPD42NS,SDS011):<br> <em>timestamp,sensor_id,location,lat,lon,P1,durP1,ratioP1,P2,durP2,ratioP2</em></p> <p>Description:</p> <ul> <li>timestamp: Date and Time of measurement</li> <li>sensor_id: ID of Sensor</li> <li>lat: Latitude</li> <li>lon: Longitude</li> <li>P1: value of Particle Matter 10µg/m³ (PM10)</li> <li>durP1:</li> <li>ratioP1:</li> <li>P2: value of Particle Matter 2.5µg/m³ (PM2.5)</li> <li>durP2:</li> <li>ratioP2:</li> </ul> <p> </p>
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrLSU data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold. in Four new species of Entoloma (Entolomataceae, Agaricomycetes) subgenera Cyanula and Claudopus from Vietnam and their phylogenetic position
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrLSU data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold.
Assessment of compound-specific fatty acid d13C and d2H values to track fish residency and mobility in a small subalpine catchment
<p><span>Methods for identifying origin, movement, and foraging areas of animals are essential for understanding ecosystem connectivity, nutrient flows, and other ecological processes. Telemetric methods can provide detailed spatial coverage but are limited to minimum body size of specimen for tagging. In recent years, stable isotopes have been increasingly used to track animal migration by linking geophysical isotope landscapes called "isoscapes". However, compared to telemetric methods, the spatial resolution of bulk stable isotopes is low.</span></p> <p><span>Here we examined a novel approach by evaluating the use of compound-specific hydrogen and carbon stable isotopes of fatty acids (δ</span><sup><span>2</span></sup><span>H<sub>FA</sub> and </span><span>δ<sup>13</sup></span><span>C<sub>FA</sub>) from liver, muscle, brain, and eye tissues for identifying site-specificity of fish from a sub-alpine river catchment. We analysed 208 fish (European bullhead, rainbow trout, and brown trout) collected in 2016 and 2018 at 15 different sites.</span></p> <p><span>δ<sup>13</sup></span><span>C<sub>FA</sub> values of these fish tissues correlated more amongst each other than those of </span><span><span>δ</span><sup><span>2</span></sup><span>H<sub>FA</sub></span> values. Both </span><span><span>δ</span><sup><span>2</span></sup><span>H<sub>FA</sub></span> and </span><span><span>δ<sup>13</sup></span><span>C<sub>FA</sub></span> values showed tissue-dependent isotopic fractionation, while fish taxa had only small effects. The highest site-specificity was for </span><span>δ<sup>13</sup></span><span>C<sub>DHA</sub> values, while the </span><span>δ<sup>2</sup></span><span>H isotopic difference between LIN and ALA resulted in the highest site-specificity. Using linear discrimination analysis of FA isotope values, over 90 % of fish could be assigned to their location of origin, however, the accuracy dropped to about 56 % when isotope data from 2016 were used to predict the sites for samples collected in 2018, suggesting temporal shifts in site specificity of </span><span><span>δ</span><sup><span>2</span></sup><span>H<sub>FA</sub></span> and </span><span>d</span><span>13</span><span>CFA. However, predictive results were still higher than site specificity compared to bulk tissue isotopes for a single time point. In summary, compound-specific isotope analysis of fatty acids may become a highly effective tool for assessing fine and large-scale movement and foraging areas of animals.</span></p>
Prognostic Value of High-Sensitive C-Reactive Protein in patients with In-Stent Restenosis: A Meta-Analysis of Clinical Trials
<p>Supplement figures</p>
Figure 19. Phylogenetic relationships within Pentanchidae with different k-values. A in Combined phylogeny and new classification of catsharks (Chondrichthyes: Elasmobranchii: Carcharhiniformes)
Figure 19. Phylogenetic relationships within Pentanchidae with different k-values. A, extended implied weighting (k = 1). B, extended implied weighting (k = 7). E, extended implied weighting (k> 10).
Figure 18. Phylogenetic relationships within Scyliorhinidae with different k-values. A in Combined phylogeny and new classification of catsharks (Chondrichthyes: Elasmobranchii: Carcharhiniformes)
Figure 18. Phylogenetic relationships within Scyliorhinidae with different k-values. A, extended implied weighting (k = 1). B, extended implied weighting (k = 7). C, extended implied weighting (k> 10).
Laboratory value changes under dabrafenib and trametinib treatement of malignant melanoma with respect to pyrexia
<p class="MsoNormal">Pyrexia is a frequent adverse event of BRAF/MEK-inhibitor combination therapy in patients with metastasized malignant melanoma (MM). The study's objective was to identify laboratory changes which might correlate with the appearance of pyrexia. Graphical visualization of time series of laboratory values suggested that a rise in C-reactive-protein, in parallel with a fall of leukocytes and thrombocytes, were indicative of pyrexia. Additionally, statistical analysis showed a significant correlation between lactate dehydrogenase (LDH) and pyrexia. An algorithm based on these observations was designed using a deductive and heuristic approach in order to calculate a pyrexia score (PS) for each laboratory assessment in treated patients. The supplied data provides anonymized and normalized values for CRP, LDH, leukocyte and thrombocyte numbers and the respective pyrexia score of 44 patients without pyrexia and with pyrexia under dabrafenib plus trametiniob treatment. Pyrexia scores based on the four parameters were statistically significantly higher in pyrexia patients, differentiated between groups and showed a significant predictive value for the diagnosis of pyrexia.<span><br></span></p>
Quality-controlled geomagnetic hourly mean values datasets of Sheshan observatory from 1933 to 2019
<p>The Sheshan geomagnetic observatory (IAGA code SSH) is one of the earliest geomagnetic observatories in China and one of the geomagnetic observatories with the longest history in the world. The datasets include the absolute hourly mean values files of declination components D, horizontal components H and vertical components Z at SSH for 1933-2019, and the metadata files about the datasets. The datasets can be applied to the study of geomagnetic daily variation, construction of geomagnetic field model and secular variation of geomagnetic field.</p>
Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"
<p>Supplementary materials for "The values of public libraries: a systematic review of empirical studies of stakeholder perceptions"</p> <p>https://www.emerald.com/insight/0022-0418.htm</p>
Reward salience but not spatial attention dominates the value representation in the orbitofrontal cortex
<p>We have uploaded the behavioral and electrophysiological data collected in the study of 'Reward salience but not spatial attention dominates the value representation in the orbitofrontal cortex'. The description of the data the code used to analyze it could be found at https://github.com/tmyang-lab/reward_salience_in_OFC.</p>
Dataset with values of morphological parameters and phenotypes of cells and colonies from three human pluripotent stem cell lines
<p>The dataset is a part of the following manuscript submitted for publication in International Journal of Molecular Sciences (MDPI):</p> <p>"Prognostic Analysis of Human Pluripotent Stem Cells Based on their Morphological Portrait and Expression of Pluripotent Markers" by Olga A. Krasnova, Vitaly V. Gursky, Alina S. Chabina, K. A. Kulakova, L. L. Alekseenko, Alexandra V. Panova, Sergey L. Kiselev and Irina E. Neganova</p> <p>The files contain values of several morphological parameters and phenotypes obtained for cells and colonies from hESC line H9, hiPSC line AD3, and hiPSC line CaSR. The phenotypic information is presented in three forms: four possible classes of colonies according to the visually assessed phenotype ('bad', 'average', 'good', and 'excellent'), three possible classes (previous 'good' and 'excellent' combined in one 'good' class), and two possible classes (previous 'bad' and 'average' combined in one 'bad' class, and previous 'good' and 'excellent' combined in one 'good' class).</p>
The full results (including p-values, tests statistics, and degrees of freedom where appropriate) are available as supplementary material for inspection.
<p>The full results (including <em>p</em>-values, tests statistics, and degrees of freedom where appropriate) are available as supplementary material for inspection.</p>
FIGURE 9 in Larval morphology, life cycle and nutritional values of Lepidostoma abruptum Banks 1931 (Trichoptera: Lepidostomatidae) from Lower-Hill Evergreen Forests of Southern Thailand
FIGURE 9. The frequency distributions of head capsule widths (mm) by larval instar stage of Lepidostoma abruptum Banks 1931.
FIGURE 4. Lepidostoma abruptum Banks 1931, final instar larva. 4A in Larval morphology, life cycle and nutritional values of Lepidostoma abruptum Banks 1931 (Trichoptera: Lepidostomatidae) from Lower-Hill Evergreen Forests of Southern Thailand
FIGURE 4. Lepidostoma abruptum Banks 1931, final instar larva. 4A, thorax (pronotum, mesonotum, and metanotum), dorsal; 4B, left front leg, anterior; left middle leg, anterior; left hind leg, anterior.
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.