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352 results for “research projects”
Research in Svalbard international projects edgelist
<p>This dataset lists all the country to country ties derived from the <a href="https://www.researchinsvalbard.no/">Research in Svalbard</a> (RIS) database using the country of origin of the organisations with joint research projects in Svalbard and the projects year. This edgelist is broken down into two time periods: 1972-2004 ; 2005-2022. Per each pair of countries, it gives the number of joint research projects registered in the RIS database per period of time. It can be used for network analysis purposes. It has been created and analysed using a core-periphery approach within the publication: Strouk, M. & Maisonobe, M. (2024). "Field science and scientific collaboration in the Svalbard Archipelago: beyond science diplomacy<em>". Science and Public Policy.</em> DOI: <a href="https://doi.org/10.1093/scipol/scae012">https://doi.org/10.1093/scipol/scae012/</a></p>
Descriptions of SNSF-funded research projects
<p>This repository contains the data to replicate the analyses performed in Meier, D. S., Mata, R., & Wulff, D. U. (2021). text2sdg: An R package to Monitor Sustainable Development Goals from Text. <em>arXiv preprint arXiv:2110.05856</em>.</p> <p>The <a href="../api/records/11060662/draft/files/GrantWithAbstracts.csv/content" target="_blank" rel="noopener noreferrer">GrantWithAbstracts.csv</a> data is originally provided by the Swiss National Science Foundation and can also be downloaded from their <a href="https://data.snf.ch/datasets">website</a>. This data contains information on research projects funded by the Swiss National Science Foundation between 1975 and 2022. Among other things, the data provides information on what the funded projects were about and how much funding was provided.</p> <p>The <a href="../api/records/11060662/draft/files/backtrans_table.RDS/content" target="_blank" rel="noopener noreferrer">backtrans_table.RDS</a> data contains 1,500 randomly selected projects from the <a href="../api/records/11060662/draft/files/GrantWithAbstracts.csv/content" target="_blank" rel="noopener noreferrer">GrantWithAbstracts.csv</a> data that were translated from English to German and then from German back to English.</p> <p>The <a href="../api/records/11060662/draft/files/benchmark_table_revision.rds/content" target="_blank" rel="noopener noreferrer">benchmark_table_revision.rds</a> data contains runtimes from benchmarking the text2sdg R package. </p>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)
<p>This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it.</p> <p>The report associated with this dataset is available at:</p> <p>https://doi.org/10.5281/zenodo.7805987</p>
IRISTECH Research Project
<p>irisTECH aspires to develop a novel, easy-to-use and low-cost, variable rate fertilization system for application in linear crops. The project is based on hyperspectral sensors and actuators combined with the functions of a system utilizing artificial intelligence technology in order to perform real time identification of crop/soil system in fertilizer and then apply the necessary quantity of granular fertilizers if needed. The proposed approach is opposed to existing solutions that require information collection, offline map editing and mapping, and then re-visit to the field for application. irisTECH utilizes state-of-the-art technologies and in particular emerging developments in the field of internet of things (IoT), artificial intelligence, embedded systems, cloud computing and image processing. In this rapidly expanding market, from a commercial perspective, the proposed project aspires to be an important research and innovation action with a targeted impact at national and wider international level.</p>
Research data supporting for "The embedded research librarian: a project partner"
<p>This dataset contains the data that supports the following paper: Féret, R. and Cros, M., 2019. The embedded research librarian: a project partner. <em>LIBER Quarterly</em>, 29(1), pp.1–20. DOI: <a href="https://dx.doi.org/10.18352/lq.10304">10.18352/lq.10304</a></p> <p>The dataset contains 3 files related to the bibliographic metadata of the publications of the 7 H2020 projects supported by the University Library of Lille and a general file providing the data for the table, figure 2 and 3 and for the data on H2020 projects coordinators:</p> <ul> <li>figures: this file contains the information related to the projects supported by the Library, including the data presented in the figure 2 (tab 1), the figure 3 (tab 2), the table 1 (tab 3) and the data on 2020 project coordinators (tab 4).</li> <li>wos_publications : the data extracted from the Web of Science for 106 publications (.txt, UTF-8, Windows), searched on the base of the 7 H2020 projects Cordis number.</li> <li>refined_wos_publications : the same data after having been transformed into a .xlsx format in the tool OpenRefine.</li> <li>processed_publications : contains the main bibliographic data (authors, article title, source title, DOI, date of publication) and their open status.</li> </ul> <p><strong>Abstract of the paper</strong><br> This paper presents new services developed by the Lille University Library for European and National research project coordinators. This is a specific audience that libraries are not used to target, with a widely recognised institutional status and academic background. Supporting them in their coordination activities is an opportunity to gain a new role for libraries, which starts from the design of research at the submission stage and lasts several years after, during the project lifetime. These services help coordinators to meet their funders’ expectations on open access and research data management. It is also a way to develop new collaborations with research units and some university services, such as the Grant Office. The Lille University Library has already supported the writing of forty grant proposals since 2017, including about thirty since early 2019. The Library currently follows twelve projects on open access, research data management or both. This second figure is likely to increase in 2020 due to the number of projects supported at submission stage since the beginning of 2019. The paper describes our set of services and the lessons we learned from our approach.</p>
From A to Z: Projective coordinates leakage in the wild: research data and tooling
<p>Description</p> <p>This dataset and software tool are for reproducing the research results related to CVE-2020-10932 and CVE-2020-11735, resulting from the article "From A to Z: Projective coordinates leakage in the wild" (to appear at CHES 2020). The data was used to carry out the attack in Section 6 of the article.</p> <p>Data format</p> <p>txt files</p> <p>The <code>[int].txt</code> files contain an encoded page-fault trace prefixed by <code>trace:</code>.</p> <p>A trace represents the sequence of tracked memory pages that were executed during the generation of an ECDSA signature. The trace is encoded using ASCII characters for better visualization.</p> <p>The encoding follows this table:</p> <pre><code class="language-markdown">| Functions | Symbol | Page offset | | ---------------------- |:------:|:-------:| | _gcry_ecc_ecdsa_sign | T | 0xa1000 | | _gcry_mpi_invm | . | 0xcf000 | | _gcry_mpi_set | S | 0xd5000 | | _gcry_mpi_add | A | 0xcd000 | | _gcry_mpih_sub_n | - | 0xd8000 | | _gcry_mpih_rshift | - | 0xd8000 |</code></pre> <p><code>_gcry_ecc_ecdsa_sign</code> is the highest level function tracked in the attack. This allows to differentiate different calls to the <code>_gcry_mpi_invm</code> function which contains an insecure version of a Binary Extended Euclidean Algorithm (BEEA).</p> <p>Using these pages it is possible to locate the execution of <code>_gcry_mpi_invm</code> corresponding to the computation of <code>Z mod p</code> during projective to affine coordinates conversion (see <code>preprocess_trace</code> function).</p> <p>It can be seen, that <code>_gcry_mpih_sub_n</code> and <code>_gcry_mpih_rshift</code> shares a page. However, they can be differentiated using mainly the caller memory page. This sharing, instead of being a drawback, allows a straightforward recovery of BEEA execution flow (see <code>extract_Zi</code> and <code>extract_Xi</code> functions in <code>recover_z.py</code>).</p> <p>dat files</p> <p>The format of the <code>[int].dat</code> files is as follows.</p> <ul> <li><code># X [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># Y [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># Z [hex]</code>: Ground truth projective output of scalar multiplication, before affine conversion</li> <li><code># curve_name [str]</code>: The curve (P256)</li> <li><code># h [hex]</code>: Hash of the message to be signed</li> <li><code># k [hex]</code>: Ground truth ECDSA nonce</li> <li><code># q [hex]</code>: Curve order</li> <li><code># r [hex]</code>: First component of the ECDSA signature</li> <li><code># s [hex]</code>: Second component of the ECDSA signature</li> <li><code># x [hex]</code>: Ground truth ECDSA private key</li> <li><code># y [hex] [hex]</code>: Public key coordinates</li> <li><code># leak_pad [int],[int],[int]</code>: Leakage recovered during backtracking. Example: <code>0,4,15 => 0 = k % 2**4 = k & 15</code></li> </ul> <p>Tooling</p> <p>The <code>recover_z.py</code> script</p> <ul> <li>Loads a trace.</li> <li>Recovers the corresponding Z coordinate from the trace data.</li> <li>verifies the recovered Z matches the ground truth Z.</li> </ul> <p>Example</p> <p>Unpack the data:</p> <pre><code>tar xf traces.tar.gz</code></pre> <p>Run the tooling on trace index 123:</p> <pre><code>$ python2 recover_z.py 123 INFO:recovered Z:65b9b7006bc7b030218bef1b6e569f9f7acaee059b53d669388c6b860f67e213 INFO: real Z:65b9b7006bc7b030218bef1b6e569f9f7acaee059b53d669388c6b860f67e213</code></pre> <p>The output demonstrates the recovered Z coordinate is correct, i.e. matches the ground truth.</p> <p>Credits</p> <p>Authors</p> <ul> <li>Alejandro Cabrera Aldaya (Tampere University, Tampere, Finland)</li> <li>Cesar Pereida García (Tampere University, Tampere, Finland)</li> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> </ul> <p>Funding</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 804476).</p> <p>License</p> <p>This project is distributed under MIT license.</p> <p> </p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)
<p>The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area. The main results of the project can be found at</p> <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. ⟨10.3390/rs11111269⟩ hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> <p>This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images.</p> <ul> <li><strong>Fields_samples.7z: </strong> ESRI Shape Format. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li><strong>HySPex-2m.7z: </strong>full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li><strong>HYPXIM-4m.7z, HYPXIM-8m.7z, Sentinel2-10m.7z</strong>: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023) - PI
<p>Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Dorota Sosnowska used as a basis for two papers: <a href="https://open.icm.edu.pl/items/9117fe29-528d-43ff-99c7-2e1fe0a8a93a">Blasted 1999. Sarah Kane’s Body Against the Archive (icm.edu.pl)</a> and <a href="https://open.icm.edu.pl/items/0aa7964c-b690-42af-8f7c-3d4fc62084b5">Brzydkie uczucia. O nudzie w sztuce i teatrze lat’ 90 (icm.edu.pl)</a></p>
Data gathered during the first and second stage of carrying out the NCN research project "Odmieńcy. Performances of otherness in the Polish transition culture" (year 2022 and 2023)
<p>Data gathered during the first and secon phase of the project "Odmieńcy. Performances of otherness in Polish transition culture" by Łukasz Kiełpiński used as a basis for two papers: <a href="../records/10625768">Zarządzanie ambiwalencją. Polski dyskurs ekspercki wokół HIV/AIDS na przełomie lat osiemdziesiątych i dziewięćdziesiątych XX wieku (zenodo.org)</a> and <a href="../records/10625805">Gra o sumie zerowej. Ekonomia wstydu w filmie "Pora na czarownice" (zenodo.org)</a></p>
Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"
<p>This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see:</p> <p>Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101</p> <p>These hearths were identified using this data:</p> <p>Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977</p> <p>The above is derived from:</p> <p>Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441</p> <p> </p>
Blue Mountain Charcoal Project Research Area
<p>This GEOJSON polygon identifies the area in which Dr. Benjamin Carter (Muhlenberg College) and students have focused their efforts in an attempt to use remote sensing and field work to identify charcoal hearths from the 19th century. The main destination for the charcoal was the furnaces and forge of the Balliet family (Lehigh and East Penn Furances and East Penn Forge) in East Penn, Carbon County and Washington, Lehigh County (Pennsylvania, USA). This polygon defines an area that includes much of Pennsylvania State Gamelands #217 but also surrounding privately owned areas that show limited recent impacts by people (especially avoiding homes and roads). It extends from the Lehigh Gap to Pennsylvania Route 309. While there is limited evidence of hearths to the east of the Lehigh Gap, there are definitely more hearths to the southwest of route 309.</p>
Toolkit on Open Access for Research Project Coordinators
<p>The materials in this toolkit were created by Romain Féret as a resource for training on how to help project coordinators to comply with their open access requirements. The slides of the training are available on Zenodo at 10.5281/zenodo.3381783. This training day took place on Wednesday the 5th of June 2019, at the University of Lille. It was organized with the support of Couperin as a part of its activities in the project OpenAIRE-Advanced.</p> <p>The tutorials are divided into two folders. The ‘Coordinator’ folder contains documents that can be sent directly to the researchers, while the ‘Support staff’ folder contains tutorials for support staff (librarians, project managers) who help the coordinators to manage their project. Each tutorial is in .pdf and .docx format for easy reuse and modification. Each document is available in French and in English.</p>
Research data from the survey on Smart Cities professional profiles for the Article "Modelling and analyzing the availability of technical professional profiles for the success of Smart Cities projects in Europe"
<p>The file includes the complete version of data collected through the surrvey on recommended profile for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician. It complements the previous version focused on IoT implementation stired at <a href="../doi/10.5281/zenodo.7492254">https://zenodo.org/doi/10.5281/zenodo.7492254</a></p>
Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions
<p><strong>Looking into the Futures: Delphi Survey about the MENA region</strong></p> <p>In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled.</p> <p>The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).</p>
Survey for coordinators of agroecology research projects
<p>This dataset (Project_coordinators_survey.csv) contains data related to the survey launched within the Task 1.3 of AE4EU project for the coordinators of agroecology research projects funded by European, transnational, and national programmes identified in the mapping activities. Together with the dataset, the structure of the questionnaire related to this survey is also provided (Project_coordinators_questionnaire_structure.pdf).</p> <p>Answers from respondents were anonymised before the publication. Informed consent was obtained from participants to the survey to use their answers and quotations for research and publication</p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences X - Research Project Site Information 2014
This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include research site location information. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.
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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.