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254 results for “Grants”
Dataset for SNSF Project 'Green Piezo' (Grant no. 179064)
<p>This data set contains the data collected during the FNS project Green Piezo (Green biodegradable piezoelectric electronic and microsystems, Grant no. 179064).</p> <p>With the continuous increase of electronic waste, the main research topic of this project is the development of green electronic and microsystems with a focus on piezoelectric transducers. The latter are currently made using materials can be harmful to the environment, even toxic such as lead zirconate titanate, and structured using mainly cleanroom processes. The realization of more sustainable piezoelectric microsystems is based here on an approach involving the use of degradable materials and their patterning using additive manufacturing, i.e. printing. <br> <br>The objectives of the project were:<br>• The development of a piezoelectric ink processed by printing at temperatures compatible with sensitive biodegradable cellulosic and biopolymer substrates; <br>• Integration of the piezoelectric material with degradable electrodes fully by printing;<br>• Demonstration of the applicability and valorisation of the developed technologies through the realization of different types of ecoresorbable and bioresorbable microsystems, such as physical and chemical sensors, and actuators. </p> <p>The data present in this data set contains the following elements: (i) electrical and piezoelectric properties and process optimization for conductive and piezoelectric eco-friendly and biodegradable inks, (ii) optical and topology images and data of the resulting printed layers, (iii) data pertaining to the behavior and functionality of sensors and actuators based on these inks and finally (iv) the design files used to print these devices. A more in-depth description is given in README.txt file attached to the data set.</p>
Fig. 2 in Identification of novel Theileria genotypes from Grant's gazelle
Fig. 2. Phylogenetic analysis of Theileria genotypes isolated from Grant's gazelles (Nanger granti) in Kenya. Bayesian analysis of a 400 nucleotide fragment of the 18S ribosomal RNA gene from 1 sequence of Toxoplasma gondii, 33 Theileria sequences from GenBank, and 3 representative Theileria sequences from Grant's gazelles (GG1, GG2, GG3) in this study (bold font). The tree is rooted on the lineage of T.gondii. Numbers above the branches indicate bootstrap support based on 1000 replicates. Host species, geographic location of isolation, and GenBank accession numbers of the sequences are provided where known. Numbered sequences are listed in Table 1.
Fig. 1 in Identification of novel Theileria genotypes from Grant's gazelle
Fig. 1. Light microscopy of a blood smear stained with Giemsa showing single and paired hemoparasites (highlighted by arrows).
OB00004 Gaya Grant of Samudragupta
<p>OB00004 Gaya Grant of Samudragupta engraved on the front face with inscription IN00004 and carrying an oval seal of the Gupta period (OB00004 b).</p>
OB00004 [part b] Seal on the Gaya Grant of Samudragupta
<p>OB00004 [part b] Seal on the Gaya Grant of Samudragupta.</p>
OB00004 Gaya Grant of Samudragupta
<p>OB00004 Gaya Grant of Samudragupta rear face with an oval seal of the Gupta period (OB00004 b).</p>
Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: 773785
<p>Questionnaires: Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: <strong>773785</strong></p>
FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.) in Review of the Frog Genus Silverstoneia, with Descriptions of Five New Species from the Colombian Chocó (Dendrobatidae: Colostethinae)
FIG, 1. John William Daly (1933–2008) on the upper Río San Juan. This paper is dedicated to John Daly, our late friend and colleague, who helped collect three of the new species here described. In addition to his globally acclaimed discoveries in chemistry and pharmacology, John was an accomplished field herpetologist who contributed importantly to the systematics and natural history of dendrobatoid frogs (see Grant et al., 2006; Myers, 2009). This photograph shows John at age 37, with the upper Río San Juan behind him and branches overhead of a madroño tree (probably Garcinia magnifolia, syn. Rheedia chocoensis, Clusiaceae). When in South America, John was never far from a dendrobatid frog—this time, in the tree above his head, a tiny, undescribed semiarboreal species (also collected and later named "Dendrobates fuguritus" by our colleague Philip Silverstone). Other dendrobatids found nearby included Phyllobates aurotaenia (Boulenger, 1913), which was then being used for poisoning blowgun darts, and also the nontoxic species that we name Silverstoneia dalyi herein. (Photograph by C. W. Myers, 2 km above Playa de Oro, Chocó, February 16, 1971.)
Lending Club loan dataset for granting models
<p>Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on <a href="https://www.kaggle.com/datasets/wordsforthewise/lending-club">Kaggle</a> and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).</p> <p><strong>TARGET VARIABLE</strong></p> <p>The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.</p> <p><strong>EXPLANATORY VARIABLES</strong></p> <p>The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.</p> <h1><strong>FULL LIST OF VARIABLES</strong></h1> <p><strong>Loan identification variables:</strong></p> <ul> <li> <p>id: Loan id (unique identifier). </p> </li> <li> <p>issue_d: Month and year in which the loan was approved.</p> </li> </ul> <p><strong>Quantitative variables:</strong></p> <ul> <li> <p>revenue: Borrower's self-declared annual income during registration. </p> </li> <li> <p>dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.</p> </li> <li> <p>loan_amnt: Amount of credit requested by the borrower. </p> </li> <li> <p>fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.</p> </li> <li> <p>experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.</p> </li> </ul> <p><strong>Categorical variables:</strong></p> <ul> <li> <p>emp_length: Categorical variable with the employment length of the borrower (includes the no information category) </p> </li> <li> <p>purpose: Credit purpose category for the loan request. </p> </li> <li> <p>home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.</p> </li> <li> <p>addr_state: Borrower's residence state from the USA. </p> </li> <li> <p>zip_code: Zip code of the borrower's residence.</p> </li> </ul> <p><strong>Textual variables</strong></p> <ul> <li> <p>title: Title of the credit request description provided by the borrower.</p> </li> <li> <p>desc: Description of the credit request provided by the borrower.</p> </li> </ul> <p>We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&amp;” was substituted by “&”, “&lt;” was substituted by “<”, etc.).</p> <h1><strong>RELATED WORKS</strong></h1> <p>This dataset has been used in the following academic articles:</p> <ul> <li>Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. <a href="https://doi.org/10.48550/arXiv.2401.16458">https://doi.org/10.48550/arXiv.2401.16458</a></li> <li>Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. <a href="https://doi.org/10.1109/ACCESS.2020.2984412">https://doi.org/10.1109/ACCESS.2020.2984412</a></li> </ul>
A Statue from Ulysses S. Grant Memorial
A Statue from Ulysses S. Grant Memorial in Washington DC Created in RealityCapture from 42 images Source: Objaverse 1.0 / Sketchfab
Friends of Princeton University Library Research Grant Application
<p>This pdf is the project narrative of the successfully funded application to Princeton University Library entitled “Between City and Cosmos: Mapping Alexandria and the Oikoumene. A Study in Cartographic Heritage,” Rare Book Division, Historic Maps Collection.</p> <p>It was submitted during a two-year MSCA-IF Fellowship at the University of Copenhagen. Project Acronym : Chlamys. Grant Agreement no. 657898.</p> <p>The MSCA Fellow would like to acknowledge the generous funding of the European Commission and thank the Stanley J. Seeger '52 Center for Hellenic Studies Hellenic Studies.</p>
Analysis of [Mim][OTf]-TiO2 catalyst - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents NMR, TGA, Raman, IR spectra, TEM-EDX analysis of synthesized [Mim][OTf]-TiO2 catalyst.</p>
Analysis of immobilized triflogallate (III) IL catalyst - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents TGA and SEM-EDX analysis of immobilized triflogallate (III) IL catalyst on silica.</p>
Catalytic activity of [Mim][OTf]-TiO2 catalyst - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents the catalytic activity of [Mim][OTf]-TiO2, [Mim][OTf] and TiO2 catalysts in the esterification of oleic acid and 2-ethylhexanol.</p>
Dataset of research grants from selected funding organisations
<p>This repository contains datasets on research grants from selected funding agencies . </p> <p>N.B: The data is still undergoing development / quality assurance. </p> <p>This release (version 1) is mainly a prototype of a more comprehensive datasets on research grants, their results and linkages to other data. </p> <p>This release contains data about <strong>500k grants</strong> from four organisations: </p> <ul> <li><strong>Agence Nationale de la Recherche (ANR) </strong>: 27,970 grants (from 2005)</li> <li><strong>European Union, Framework Programme (EU FP)</strong> : 124,233 grants (from 1984, all Framework Programmes)</li> <li><strong>US National Science Foundation (NSF)</strong>: 201,388 grants (from 2007)</li> <li><strong>UK Research and Innovation (UKRI)</strong> : 143,600 grants (from 2006)</li> </ul> <p>Where available data following data are also included: </p> <ul> <li>hosting organisations</li> <li>researchers involved </li> <li>resulting publications . </li> </ul> <p>The datasets are provided as in "tab-separated-value" format. </p> <p>A more detailed documentation, development roadmap, limitations and efforts to link it to existing initiatives is forthcoming. </p> <p>The scripts used to create the datasets can be found in this github repository:</p> <p><a href="https://github.com/almugabo/grants_dataset">https://github.com/almugabo/grants_dataset</a></p> <p>Some of the items on the (rough) development roadmap: </p> <ul> <li>Documentation of the datasets and its creation</li> <li>Seek collaborations with other interested parties (such as OpenAire or individuals working on this) to discuss the main differences between the datasets and how interests could be aligned to minimize duplication of efforts going into data collection and curation</li> <li>De-duplicate entities</li> <li>Expand the coverage <ul> <li>Datasets from other funding agencies (we already have dataset of grants from the <strong>US National Institutes of Health</strong> 1,3 Millions grants which will be included in the next release)</li> <li>Other data such as result -patents</li> </ul> </li> <li>Linking to other datasets which may help study the impact of research funding</li> </ul> <p> </p> <p>This version is an incremental upload to the previous version. </p> <p>It adds:</p> <ul> <li>an improved version of the dataset researchers from EUFP grants with identifiers etc ....</li> <li>publications / projects pairs from OpenAire</li> <li>an experimental dataset of simplified project abstracts . This was done using AI (mistral-7b) for experimentation purposes (use with caution !). </li> </ul> <p> </p>
The second research task in the project entitled "Research on the electrodialytic recovery of selected hydrophilic ionic liquids from post-reaction solutions" - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.
<p><span>The second research task carried out under the project concerns study <span>on the fouling and stability of the ion-exchange membranes</span>. The information relates to research performing for the NCN project SONATA-17, grant no. </span><span>2021/43/D/ST8/02776</span><span>.</span></p>
The first research task in the project entitled "Research on the electrodialytic recovery of selected hydrophilic ionic liquids from post-reaction solutions" - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.
<p><span>The first research task carried out under the project concerns study on the effectiveness of the selected hydrophilic ILs transport across ion-exchange membranes. The information relates to research performing for the NCN project SONATA-17, grant no. </span><span>2021/43/D/ST8/02776</span><span>.</span></p>
The third research task in the project entitled "Research on the electrodialytic recovery of selected hydrophilic ionic liquids from post-reaction solutions" - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.
<p><span>The third research task carried out under the project concerns <span>development of mathematical model to describe the transport of ionic liquid in the electrodialysis process. </span>The information relates to research performing for the NCN project SONATA-17, grant no. </span><span>2021/43/D/ST8/02776</span><span>.</span></p>
[Bmim]Cl limiting current density - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.
<p><span>Dataset contains results obtained during the NCN project SONATA-17, grant no. </span><span>2021/43/D/ST8/02776</span><span>. The file presents results of the influence of [Bmim]<sup>+</sup> ion concentration and linear flow velocity on the limiting current density during electrodialysis process.</span></p>
[Omim]Cl recovery by ED - NCN project SONATA-17, grant no. 2021/43/D/ST8/02776.
<p><span>Dataset contains results obtained during the NCN project SONATA-17, grant no. </span><span>2021/43/D/ST8/02776</span><span>. The file presents results of the effect of ED parameters on IL recovery.</span></p>
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