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13,499 results for “researcher”
Insights into European research funders policies and practices - public dataset
<p>This is the dataset arising from a survey of European research funders on Open Access (OA) and Research Data (RD) policies, commissioned by SPARC Europe, in consultation with representatives from the following organisations: <a href="https://www.allea.org/">ALLEA</a>, the <a href="https://www.efc.be/">European Foundation Centre</a> and <a href="http://scienceeurope.org/">Science Europe</a> and a wider advisory group.</p> <p>Launched in the spring of 2019, the survey, which targeted about 400 funders, garnered just over 60 responses from 29 countries. The cohort includes important national funding agencies (almost 50%), pan-European funders, national and regional academies, foundations and philanthropic organisations and research charities. </p>
Examining ray and skate diversity in the Irish Sea using DNA barcodes: Research project data
<p>All of the supplementary material to accompany the 4th year research project 'Examining ray and skate diversity in the Irish Sea using DNA barcodes'. Data includes <em>Cytochrome c oxidase I</em> (COI) sequences generated in this project from <em>Raja </em>specimens, agarose gel electrophoresis images of DNA samples, DNA concentrations of DNA extractions from <em>Raja </em>specimens as well the accession numbers of sequences sourced from GenBank that were used to construct a maximum likelihood tree. </p>
Inventory of the sustainability methodologies, indicators and criteria of research projects funded by the European Union
<p>Based on a screening in the CORDIS database and the experience of project partners, 16 projects were selected for analyses of their contributions regarding sustainability criteria and indicators.</p>
Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.)</p> <p>In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to<strong> capture expert knowledge from senior researchers in their research domain related to the SDG</strong>. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. <strong>The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.</strong></p> <p>17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey.<strong> The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs</strong>.</p> <p><strong>The survey contains the following questions (see inside dataset for exact wording):</strong></p> <ul> <li><strong>Are you familiar with this SDG?</strong> <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li><strong>Suggest research papers that are relevant for this SDG (upload list)</strong> <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li><strong>Select research papers that are relevant for this SDG (radio buttons: accept, reject)</strong> <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li><strong>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest SDG related glossaries with relevant keywords (text fields: url)</strong> <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest improvements for the current queries (text field: suggestions per target)</strong> <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> <p><strong>In the dataset root you'll find the following folders and files:</strong></p> <ul> <li><strong>/00-survey-input/</strong> <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li><strong>/01-raw-data/</strong> <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li><strong>/02-aggregated-data/</strong> <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li><strong>/03-scripts/</strong> <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li><strong>/04-processed-data/</strong> <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p><strong>In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:</strong></p> <ul> <li><strong>SDG-survey-questions.pdf</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-questions.doc</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-respondents-per-sdg.csv</strong> <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li><strong>SDG-survey-city-heatmap.csv</strong> <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li><strong>SDG-survey-suggested-publications.txt</strong> <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong> <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li><strong>SDG-survey-selected-publications-accepted.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li><strong>SDG-survey-selected-publications-rejected.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li><strong>SDG-survey-selected-keywords.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-keywords.csv</strong> <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-keywords.csv</strong> <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-glossaries.csv</strong> <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-selected-journals.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-journals.csv</strong> <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-journals.csv</strong> <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-suggested-query.csv</strong> <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> <p><strong>Cite as:</strong></p> <blockquote> <p><em>Survey data of "Mapping Research output to the SDGs"</em> by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a></p> </blockquote> <p><strong>Attribute as:</strong></p> <blockquote> <p><em><strong>Survey data of "Mapping Research output to the SDGs</strong>"</em> by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA);<strong> is licensed under a Creative Commons Attribution 4.0 International License.</strong> <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a></p> </blockquote>
Research4Life Landscape and Situation Analysis - Trends in the funding of research in and for LMICs PEST Analysis
<p>A PEST infographic summarising the key trends in the funding of research in and for low and middle-income countries, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Research data supporting for Stress-induced amorphization triggers deformation in the lithospheric mantle
<p>Original TEM micrographs used to prepare the figures of the article</p>
Dataset used in "Marine Sediment Characterized by Ocean-Bottom Fiber-Optic Seismology" by Spica et al., 2020 in Geophysical Research Letters
<p>3000fullhisy: raw data to reproduce Fig. 2<br> ppsdspec.npz: all spectrogram as shown in Fig. 3a<br> AllVelMods: All velocity model shown in Fig. 3b<br> ac.out.final.npz: auto-correlation image in Fig. 3c<br> DAS11_lpf5.stack51.grd: Earthquake wavefield as shown in Fig. 3d<br> </p> <p> </p>
Dataset to "Applied Research of the Hygrothermal Behaviour of an Internally Insulated Historic Wall without Vapour Barrier: In Situ Measurements and Dynamic Simulations"
<p>This record contains raw data of a 3-month monitoring period of the HeLLo project.</p> <p>The datafiles titled MeasLog_YYYY-MM-DD.dat correspond to the raw data used for the data analysis presented in “Hygrothermal analysis at critical points of an internally insulated historic wall without vapour barrier: in situ measurements and dynamic simulation”, submitted for publication in journal <em>energies</em>.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, corresponds to daily registered data monitored every minute.</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat, contains temperature (T) and relative humidity (RH) values, monitored through T-RH sensors (Telaire T9602; Amphenol). The general architecture of the acquisition system is based on a Master Slave configuratio, as described in “Development of a Compatible, Low Cost and High Accurate Conservation Remote Sensing Technology for the Hygrothermal Assessment of Historic Walls” (doi:10.3390/electronics8060643).</p> <p>Each file, format MeasLog_YYYY-MM-DD.dat is a text-based DAT file and can be opened with a standard text editor.</p>
Bibliographic data and analysis of COVID-19 research outputs from Imperial College London 16.01.2020-02.04.2020
<p>Bibliographic data and analysis of 41 research outputs, including reports/preprints/published articles/code, identified as having Imperial authorship and being relevant to COVID-19, published between 16.01.2020 - 02.04.2020. </p> <p>Related report can be found at: Price RC and Ozkan YA. 13 weeks in a pandemic: a descriptive study of Imperial College London’s COVID-19 publications. Imperial College London (April 2020), https://doi.org/10.25561/77970</p>
Neurobiology Research Unit – Serotonin Atlas
<p>A high-resolution positron emission tomography (PET)- and magnetic resonance imaging-based human brain atlas of four important serotonin receptors (5-HT1A, 5-HT1B, 5-HT2A, 5-HT4) and the serotonin transporter (5-HTT) is presented. The actual dataset includes five NIfTI images (one NIfTI for each serotonin tracer) and 10 FreeSurfer surfaces with average non-displaceable binding potential (BP<sub>ND</sub>) values (two Freesurfer surfaces for each serotonin tracer). The molecular imaging maps were related to autoradiography data and an unprecedented agreement was found, supporting the validity of the methodology and results presented, and allowing translating PET binding estimates into densities. This conversion facilitates the interpretability of the maps and allows for a direct comparison across the five 5-HT targets, in vivo in the human brain. All participants included in this study were healthy male and female controls from the Cimbi database; the data analysis was restricted to include individuals aged between 18 and 45 years. A total of 232 PET scans and corresponding structural MRI scans were acquired for 210 individual participants; 189 subjects had only one scan, 20 subjects had two scans, and a single had three scans. Scans were combined and aggregated atlases for each serotonin tracer were generated.</p> <p>For more information on the original data, please go to: <a href="https://xtra.nru.dk/FS5ht-atlas/">https://xtra.nru.dk/FS5ht-atlas/</a></p>
CVE-2020-12399: research data and tooling
<p>This dataset and software tools are for reproducing the research results related to <a href="https://nvd.nist.gov/vuln/detail/CVE-2020-12399">CVE-2020-12399</a>, resulting from the manuscript "Déjà vu: Side-channel analysis of Mozilla's NSS", to appear at <a href="https://www.sigsac.org/ccs/CCS2020/">ACM CCS 2020</a>.</p> <ul> <li>The data is from a remote timing attack against the NSS v3.51 implementation of DSA signing.</li> <li>The client machine was a 3.1 GHz 64-bit Intel i5-2400 CPU (Sandy Bridge).</li> <li>The server machine was a Raspberry Pi 3 Model B plus board containing a 1.4 GHz 64-bit quad-core Cortex-A53 processor.</li> <li>The client and server were connected by a Cisco 9300 series enterprise switch over Gbit Ethernet.</li> <li>The data contains pow(2,18) samples.</li> <li>The data was used to produce Figure 1 in the paper and contains all the remote timing attack data from Section 4.</li> </ul> <p>Data description</p> <p>The file <code>remote_timings.json</code> contains a single JSON array. Each entry is a dictionary representation of one digital signature. A description of the dictionary fields follows.</p> <ul> <li><code>p</code>: prime (DSA parameter).</li> <li><code>q</code>: generator order (DSA parameter).</li> <li><code>g</code>: generator (DSA parameter).</li> <li><code>x</code>: the DSA private key.</li> <li><code>y</code>: the corresponding public key.</li> <li><code>r</code>: first component of the DSA signature.</li> <li><code>s</code>: second component of the DSA signature.</li> <li><code>k</code>: the ground truth nonce generated during DSA signing.</li> <li><code>k_len</code>: the ground truth number of bits in said nonce.</li> <li><code>msg</code>: message digitally signed.</li> <li><code>h</code>: SHA-256 hash of said message. (Truncated to the same bitlen as q.)</li> <li><code>id</code>: ignored.</li> <li><code>latency</code>: the measured wall clock time (CPU clock cycles) to produce the digital signature.</li> </ul> <p>Prerequisites</p> <pre><code>sudo apt install openssl python3-ijson xxd jq</code></pre> <p>Data setup</p> <p>Extract the JSON:</p> <pre><code>tar xf remote_timings_rpi.tar.gz</code></pre> <p>Key setup</p> <p>Generate the public key (<code>public.pem</code> here) from the provided private key (<code>private.pem</code> here):</p> <pre><code>$ openssl pkey -in private.pem -pubout -out public.pem</code></pre> <p>Examine the keys if you want.</p> <pre><code>$ openssl pkey -in private.pem -text -noout $ openssl pkey -in public.pem -text -noout -pubin</code></pre> <p>Example: Verify key material</p> <pre><code>$ openssl pkey -in private.pem -text -noout Private-Key: (2048 bit) priv: 1f:87:68:eb:57:e1:f4:f1:29:a6:c8:ca:03:c8:db: 49:1d:8e:2b:81:bd:72:92:64:0c:1c:d6:6d pub: 00:9d:fa:bc:47:00:cb:11:fa:51:45:c1:bd:b1:88: 2d:dd:a2:79:5b:c3:43:0a:af:bb:83:e2:d5:84:d1: 07:01:ab:f9:ae:76:2d:dd:f2:a5:75:f5:3e:94:4d: 3b:c6:f6:ce:17:c6:60:09:5b:49:3d:cb:a0:db:ec: 29:91:85:8b:c3:f5:6c:6a:3c:01:87:12:85:ae:fc: 9e:bf:67:81:1b:1d:b1:9d:12:bd:79:8c:54:08:48: 11:13:6d:ab:b0:16:ef:11:4a:27:a7:0a:80:b3:db: 72:c1:cc:1e:e8:4a:39:b7:00:ca:97:b7:3a:6e:e9: 25:22:2e:5c:57:ee:62:be:23:d0:5e:53:a3:9f:05: d4:7d:7f:b5:b6:cb:4b:27:90:14:79:72:a5:43:97: c6:6a:7d:f7:32:b3:67:58:90:fc:c3:65:34:57:89: 1b:43:28:68:43:24:12:5e:f1:43:76:3c:e9:bc:9c: 5d:7d:ae:d6:3a:31:32:ca:df:a4:07:88:a2:55:6e: a4:8c:da:13:c8:30:b7:2a:1c:23:0f:32:da:9e:7f: e1:f7:3d:2d:1c:58:f5:1d:f2:7d:fb:67:45:8d:dd: 84:eb:83:c4:b0:00:a6:c2:09:b0:48:48:f9:4e:a8: d7:ab:e1:c6:e8:bf:5c:fa:e3:f2:cd:c6:f1:e7:f2: 2c:90 P: 00:e5:4e:f4:32:f8:4a:ec:28:3c:dd:32:a8:05:e3: 5a:fa:a5:81:47:98:d9:a7:94:ba:34:b0:f9:7b:20: c5:fb:52:12:3e:82:d7:6e:6f:f5:50:be:5e:9f:df: 82:9b:4e:0c:9d:a2:9f:3f:0a:f3:72:c2:55:7c:46: 6e:fe:48:00:88:b6:4e:4f:9b:19:8c:98:3b:71:42: 56:d2:b4:1c:47:69:6e:fc:f0:e6:26:04:0e:e2:63: ed:06:0f:fb:a8:a9:94:73:e1:41:e0:6b:5a:b4:d9: 86:cd:7b:46:d3:39:ba:18:13:da:f2:3a:7b:dc:41: 21:83:e8:0d:25:13:31:90:5d:bd:82:41:9b:ea:6b: 8a:ba:8a:48:b1:1d:d2:3d:5e:c4:1b:29:5e:7f:b6: 56:1b:e6:91:65:ec:84:82:c2:f6:a1:b0:14:1b:0b: 08:d8:2b:2a:06:17:d7:2a:9b:c3:aa:fb:28:26:14: 3f:5d:0a:48:1a:48:45:c0:fd:ea:ec:90:6c:ec:93: c8:af:a3:31:4b:3a:d8:cd:20:ae:8f:14:58:26:49: 18:1f:7a:99:c9:da:c3:f0:76:b8:52:8d:eb:b2:e2: 98:6b:a5:47:15:c3:ff:c8:e7:6c:d3:db:c7:fb:4c: 36:3e:15:eb:45:e1:4a:5d:01:ed:3b:87:f7:69:c1: 31:59 Q: 00:ca:6d:df:fc:7b:96:2e:35:30:27:4f:1f:cf:57: 2f:e9:4c:40:97:53:a1:fa:d0:89:56:8d:2c:25 G: 43:26:04:66:b3:80:c3:3f:8d:f5:5a:29:79:58:7a: 0b:8c:72:b9:cb:23:61:5d:c1:45:c5:38:7f:33:4e: 93:63:75:8a:b0:44:61:8f:59:df:fd:2f:3f:1f:22: 73:66:ba:53:65:53:2a:57:5b:d9:40:34:be:4c:78: 22:4a:bf:94:5d:23:15:65:66:e1:1f:6b:93:12:00: f0:ac:f5:64:0d:6d:6c:a3:eb:26:83:6d:68:95:e0: 2c:bf:75:62:fa:5f:95:0f:b0:40:68:ce:66:3b:58: ed:c1:63:e3:d8:35:5c:cc:db:b8:12:e6:62:e4:63: b6:29:e0:86:75:79:bc:95:27:74:d1:fd:94:b9:7f: 6e:57:b4:e5:39:a2:15:41:94:3f:47:90:43:a5:da: dd:08:a4:92:c5:bf:ef:34:4e:2e:7e:82:5c:07:0e: dc:5d:6b:79:10:04:53:cc:b2:8e:bd:65:61:80:49: ad:c7:dd:5f:5a:9b:74:ae:bc:e0:49:f1:ad:4c:1e: 8f:4e:9d:39:e9:fe:57:4d:39:b7:ba:69:03:e3:7e: 4d:0d:9b:65:c3:55:77:ff:2c:86:27:21:c7:3e:60: a3:23:a5:e8:7e:0d:29:15:1c:5e:04:91:91:25:03: f3:97:77:6c:11:24:34:58:c9:ec:b7:ca:ce:74:cd: a7</code></pre> <p>This shows the keys indeed match (JSON <code>x,y</code>, above <code>priv,pub</code>):</p> <pre><code>$ grep --max-count=1 '"x"' remote_timings.json "x": "0x1F8768EB57E1F4F129A6C8CA03C8DB491D8E2B81BD7292640C1CD66D", $ grep --max-count=1 '"y"' remote_timings.json "y": "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code></pre> <p>This shows the DSA parameters match (JSON <code>p,q,g</code>, above <code>P,Q,G</code>):</p> <pre><code>$ grep --max-count=1 '"p"' remote_timings.json "p": "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grep --max-count=1 '"q"' remote_timings.json "q": "0xCA6DDFFC7B962E3530274F1FCF572FE94C409753A1FAD089568D2C25", $ grep --max-count=1 '"g"' remote_timings.json "g": "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code></pre> <p>Example: Extract a single entry</p> <p>Here we use the python script <code>pickone.py</code> to extract the entry at index 2 (starting from 0).</p> <pre><code>$ python3 pickone.py remote_timings.json 2 | jq . > 2.json $ cat 2.json { "latency": "399901598", "y": "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g": "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h": "0xC7DEAC64C95157992CB0D77CF944CB107C756F3E30D1C49C0C48A6EA", "k": "0x742A7562E2A192996440AE2A4FDF5D37E1A532E1E6A50BCA3964BBDA", "q": "0xCA6DDFFC7B962E3530274F1FCF572FE94C409753A1FAD089568D2C25", "p": "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s": "0x79FD73D901BB077D14334D8CC714804577515A1E0ADC9F995BB7534C", "r": "0x61F949D772E22EA9EFBB36442BC229767B28BE2A8061FA7339AFDDC8", "msg": "0x318198301A06092A864886F70D010903310D060B2A864886F70D0109100104301C06092A864886F70D010905310F170D3230303432343131333135375A302B060B2A864886F70D010910020C311C301A30183016041470AD64D33E65A855E6C332AA52736F71D58E7527302F06092A864886F70D0109043122042090C90BFC7A8C459EDB5AF58A8878EE826B6FD02A20E2BAAF2C73984FA380FDD2", "x": "0x1F8768EB57E1F4F129A6C8CA03C8DB491D8E2B81BD7292640C1CD66D", "id": "335451053151725", "k_len": "223" }</code></pre> <p>Example: Dump message to binary file</p> <p>Extract the <code>msg</code> field from the target JSON and dump it as binary.</p> <pre><code>$ sed -n 's/^ "msg": "0x\(.*\)",$/\1/p' 2.json | xxd -r -p > 2.msg $ xxd -g1 2.msg 00000000: 31 81 98 30 1a 06 09 2a 86 48 86 f7 0d 01 09 03 1..0...*.H...... 00000010: 31 0d 06 0b 2a 86 48 86 f7 0d 01 09 10 01 04 30 1...*.H........0 00000020: 1c 06 09 2a 86 48 86 f7 0d 01 09 05 31 0f 17 0d ...*.H......1... 00000030: 32 30 30 34 32 34 31 31 33 31 35 37 5a 30 2b 06 200424113157Z0+. 00000040: 0b 2a 86 48 86 f7 0d 01 09 10 02 0c 31 1c 30 1a .*.H........1.0. 00000050: 30 18 30 16 04 14 70 ad 64 d3 3e 65 a8 55 e6 c3 0.0...p.d.>e.U.. 00000060: 32 aa 52 73 6f 71 d5 8e 75 27 30 2f 06 09 2a 86 2.Rsoq..u'0/..*. 00000070: 48 86 f7 0d 01 09 04 31 22 04 20 90 c9 0b fc 7a H......1". ....z 00000080: 8c 45 9e db 5a f5 8a 88 78 ee 82 6b 6f d0 2a 20 .E..Z...x..ko.* 00000090: e2 ba af 2c 73 98 4f a3 80 fd d2 ...,s.O....</code></pre> <p>Note the <code>xxd</code> output matches the <code>msg</code> byte string from the target JSON.</p> <p>Example: Dump hash to binary file</p> <p>Extract the <code>h</code> field from the target JSON and dump it as binary.</p> <pre><code>$ sed -n 's/^ "h": "0x\(.*\)",$/\1/p' 2.json | xxd -r -p > 2.hash $ xxd -g1 2.hash 00000000: c7 de ac 64 c9 51 57 99 2c b0 d7 7c f9 44 cb 10 ...d.QW.,..|.D.. 00000010: 7c 75 6f 3e 30 d1 c4 9c 0c 48 a6 ea |uo>0....H..</code></pre> <p>Note the <code>xxd</code> output matches the <code>h</code> byte string from the target JSON.</p> <p>Example: Hash is consistent</p> <pre><code>$ sha256sum 2.msg c7deac64c95157992cb0d77cf944cb107c756f3e30d1c49c0c48a6ea809e6d58 2.msg</code></pre> <p>Note the first 28 bytes of <code>sha256sum</code> output match the <code>h</code> byte string from the target JSON. (DSA maps to GF(q) with truncation.)</p> <p>Example: Dump signature to DER</p> <p>The <code>hex2der.sh</code> script takes as an argument the target JSON filename, and outputs the DER-encoded DSA signature to stdout by extracting the <code>r</code> and <code>s</code> fields from the target JSON.</p> <pre><code>$ ./hex2der.sh 2.json > 2.der $ openssl asn1parse -in 2.der -inform DER 0:d=0 hl=2 l= 60 cons: SEQUENCE 2:d=1 hl=2 l= 28 prim: INTEGER :61F949D772E22EA9EFBB36442BC229767B28BE2A8061FA7339AFDDC8 32:d=1 hl=2 l= 28 prim: INTEGER :79FD73D901BB077D14334D8CC714804577515A1E0ADC9F995BB7534C</code></pre> <p>Note the <code>asn1parse</code> output contains a sequence with two integers, matching the <code>r</code> and <code>s</code> fields from the target JSON.</p> <p>Example: Verify the signature (post-hash)</p> <p>We use <code>pkeyutl</code> here to verify the raw hash directly.</p> <pre><code>$ openssl pkeyutl -in 2.hash -inkey public.pem -pubin -verify -sigfile 2.der Signature Verified Successfully</code></pre> <p>Note it fails for other hashes (messages), a fundamental security property for digital signatures:</p> <pre><code>$ dd if=/dev/urandom of=bad.hash bs=1 count=28 28+0 records in 28+0 records out 28 bytes copied, 0.000647097 s, 43.3 kB/s $ openssl pkeyutl -in bad.hash -inkey public.pem -pubin -verify -sigfile 2.der Signature Verification Failure</code></pre> <p>Example: Verify the signature (pre-hash)</p> <p>We use <code>dgst</code> here to verify by recomputing the hash.</p> <pre><code>$ openssl dgst -sha256 -verify public.pem -signature 2.der 2.msg Verified OK</code></pre> <p>Example: Message analysis</p> <p>The <code>msg</code> JSON field is an <a href="https://tools.ietf.org/html/rfc3161">RFC 3161</a> Time Stamp Request. You can examine it:</p> <pre><code>$ openssl asn1parse -in 2.msg -inform DER 0:d=0 hl=3 l= 152 cons: SET 3:d=1 hl=2 l= 26 cons: SEQUENCE 5:d=2 hl=2 l= 9 prim: OBJECT :contentType 16:d=2 hl=2 l= 13 cons: SET 18:d=3 hl=2 l= 11 prim: OBJECT :id-smime-ct-TSTInfo 31:d=1 hl=2 l= 28 cons: SEQUENCE 33:d=2 hl=2 l= 9 prim: OBJECT :signingTime 44:d=2 hl=2 l= 15 cons: SET 46:d=3 hl=2 l= 13 prim: UTCTIME :200424113157Z 61:d=1 hl=2 l= 43 cons: SEQUENCE 63:d=2 hl=2 l= 11 prim: OBJECT :id-smime-aa-signingCertificate 76:d=2 hl=2 l= 28 cons: SET 78:d=3 hl=2 l= 26 cons: SEQUENCE 80:d=4 hl=2 l= 24 cons: SEQUENCE 82:d=5 hl=2 l= 22 cons: SEQUENCE 84:d=6 hl=2 l= 20 prim: OCTET STRING [HEX DUMP]:70AD64D33E65A855E6C332AA52736F71D58E7527 106:d=1 hl=2 l= 47 cons: SEQUENCE 108:d=2 hl=2 l= 9 prim: OBJECT :messageDigest 119:d=2 hl=2 l= 34 cons: SET 121:d=3 hl=2 l= 32 prim: OCTET STRING [HEX DUMP]:90C90BFC7A8C459EDB5AF58A8878EE826B6FD02A20E2BAAF2C73984FA380FDD2</code></pre> <p>Example: Statistics</p> <p>The <code>stats.py</code> script shows how to extract the desired fields from the JSON. It computes the median latency over each nonce bit length.</p> <pre><code>$ python3 stats.py remote_timings.json Len Median 205 121.2 206 121.2 207 120.6 208 138.3 209 122.9 210 123.0 211 123.0 212 123.0 213 125.0 214 125.0 215 125.0 216 125.0 217 127.1 218 127.1 219 127.1 220 127.1 221 129.1 222 129.1 223 129.1 224 129.1</code></pre> <p>You can verify these medians are consistent with Figure 1 in the paper.</p> <p>The <code>stats.py</code> script can be easily modified for more advanced analysis.</p> <p>Credits</p> <p>Some parts borrowed from <a href="https://doi.org/10.5281/zenodo.3736311">this artifact</a>.</p> <p>Authors</p> <ul> <li>Sohaib ul Hassan (Tampere University, Tampere, Finland)</li> <li>Iaroslav Gridin (Tampere University, Tampere, Finland)</li> <li>Ignacio M. Delgado-Lozano (Tampere University, Tampere, Finland)</li> <li>Cesar Pereida García (Tampere University, Tampere, Finland)</li> <li>Jesús-Javier Chi-Domínguez (Tampere University, Tampere, Finland)</li> <li>Alejandro Cabrera Aldaya (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 <a href="LICENSE">MIT license</a>.</p>
Highlights from 10+ years of lichenological research in Great Smoky Mountains National Park: celebrating the United States National Park Service Centennial
<p>Great Smoky Mountains National Park is renowned as one of the most biologically diverse tracts of land in North America and is the most visited national park in the United States. The park comprises ∼830 square miles, epitomizes eastern temperate hardwood forests of North America, and serves as a refuge for nearly 20,000 documented species from microbes to plants and mammals. Lichens comprise one particularly diverse group of organisms in the park. In this study, we review data from our 11 years of lichenological research in Great Smoky Mountains National Park. Based on approximately 6,000 new field collections generated, the park checklist now includes 920 species, a 129% increase over estimates made two decades ago. Nearly a quarter of the lichens reported in the park are known from only a single occurence whereas only 7% of the lichens are known from 20 or more occurences. An assessment of commonness/rarity for all 920 species indicates that nearly half of the park's lichens should be considered to be infrequent, rare, or exceptionally rare. We assessed the distributions of all 920 species and found that 54 are endemic to the southeastern United States, 30 are endemic to the southern Appalachians, and eight occur nowhere else than within the confines of the national park. We discuss biogeographical affinities of the park's lichen biota as a whole, delimiting six regional "floristic" connections. Our 11 years of research have resulted in the discovery of several species presumed to be extinct or near-extinct. We make one new combination (<em><strong>Fuscopannaria frullaniae</strong></em>) and describe five species as new to science, each commemorating National Park Service staff instrumental to the completion of the study: <em><strong>Heterodermia langdoniana</strong></em>, <em><strong>Lecanora darlingiae</strong></em>, <em><strong>Lecanora sachsiana</strong></em>, <em><strong>Leprocaulon nicholsiae,</strong></em> and <em><strong>Pertusaria superiana</strong></em>.</p>
Figure 1 in The role of female cephalopod researchers: past and present
Figure 1. Bibliometric analysis of the papers presented during the CIAC 2012 Symposium. (A) Registrations by gender; (B) authorship of oral presentations by gender; (C) presenters of oral presentations by gender; (D) authorship of poster presentations by gender.
SmartUpLab- Co-Creation in sustainable mobility research - Systematic Reviews and Case Studies
<p>The current dataset presents the results of systematic reviews and case studies about co-creation tools best practice for sustainable mobility, carried out in the context of the research project SmartUpLab (funded by EFRE).</p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Higher Education Research: A Compilation of Journals and Abstracts 2016
<p>Revised dataset to original publication:</p> <p>Hertwig, Alexandra (2017): Higher Education Research: A Compilation of Journals and Abstracts 2016. Kassel: INCHER-Kassel. <a href="http://www.uni-kassel.de/einrichtungen/fileadmin/datas/einrichtungen/incher/Higher_Education_Research_-_A_Compilation_of_Journals_and_Abstracts_2016.pdf">http://www.uni-kassel.de/einrichtungen/fileadmin/datas/einrichtungen/incher/Higher_Education_Research_-_A_Compilation_of_Journals_and_Abstracts_2016.pdf</a> (accessed November 04, 2020)</p> <p>Datasets to volume 2013 to 2018 might vary with regard to covered journals of the original publication. The datasets include journals and respective publication data providing persistent identifiers, explicitly.</p> <p>The Research Information Service (RIS) of INCHER-Kassel, Germany provides annual compilation of academic journals since 2013. The datasets allow for further evaluation of single or multiple volumes. For more information on original publications and available datasets please visit INCHER’s RIS websites.</p> <p>http://www.uni-kassel.de/einrichtungen/en/incher/risspecial-research-library/ris-documents.html</p>
Corresponding spreadsheet to the Paper 'An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council' submitted to the Scientometric journal by Roberta Ruggieri, Fabrizio Pecoraro and Daniela Luzi from National Research Council, Italy.
<p>Gender equality and Open Access (OA) are priorities within the European Research Area (ERA) and cross-cutting issues in European research program H2020. Gender and openness are also key elements of Responsible Research and Innovation (RRI). However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent, unrelated topics.<br> This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016-2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support.<br> Our results indicate that gender disparities in scientific production still persist in particularly in STEM disciplines (Science, Technology, Engineering and Mathematics), while in medical and agricultural sciences the gender gap is the closest to parity. A positive dynamic toward OA publishing and women scientific production is shown when open disciplines with well-established practices are related to articles supported by funds. A slightly higher women propensity toward OA is shown when considering Gold OA,OA or authorships with women in the first and last article by-line position. Moreover, the prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the week enforcement of the Italian OA policies.</p>
Participatory Conceptual Diagrams to research small-scale farmers´ information sharing for adapting to climate change in Mozambique
<p>Data collected from focus groups discussions with local communities of 4 distrcits of Mozambique in November 2019. The data are a series of conceptual maps describing a) the farming practices improvements most needed to adapt to climate change, and b) the most useful information for enabling the selected improvements, the most effective information sharing sources - e.g. institutional actors, members of the community, technical support, etc. - and means of communication - e.g. radio, mobile phone, word-of-mouth, etc. For the second purpose, connections were drawn by the members of the community between information sources and the actions needed for climate change adaptation. Participants also assigned a weight to the connections, selecting between: strong, medium or a weak connection.</p> <p>Notes about the discussions and opinions expressed by participants, written down by the research team, are also included.</p> <p>Together with the data, PDF files describing metadata and detailed methodology followed are included.</p>
Know Your Research Rights: The Legal Perspective on Copyright and Open Science
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to two copyright lawyers, Malcolm Bain and Lucrezia Berto, about what the legal framework of research creation and sharing is. Who owns your research? What needs to be considered before you make it open? </p> <p><strong>Episode Links:</strong></p> <p><a href="https://acrosslegal.com/en/">Across Legal</a></p> <p><a href="https://www.linkedin.com/in/malcolm-bain-9216415/?originalSubdomain=es">Malcolm Bain</a></p> <p><a href="https://www.linkedin.com/in/lucrezia-berto/?originalSubdomain=es">Lucrezia Berto</a></p>
A Slice of the Research Cake: The Impact of Open Science in Africa
<p><strong>Episode Summary: </strong></p> <p>In this episode we talk to Joy Owango, Founding Director at Training Centre in Communication. We discussed how Open Science is democratising research and access to data and publishing in Africa, the importance of ownership in research, and the challenges inherent in widespread change. </p> <p>Originally we interviewed another two guests who are involved in the Open Science movement on the African continent: Osman Aldirdiri, founding director of Open Sudan initiative and Jo Havemann, co-founder of the AfricArXiv. Due to technical difficulties we can only publish the audio from Joy Owango. However, we do have a <a href="https://drive.google.com/file/d/1sOhAaiWjAUwQlDMuaqd5YCC3SOhXDFSP/view?usp=sharing">transcript of the full interview</a> with all three guests.</p> <p><strong>Episode Links:</strong></p> <ul> <li><a href="https://info.africarxiv.org/">AfricArXiv</a> <p><a href="https://twitter.com/AfricArxiv">Twitter</a></p> </li> <li>Joy Owango <ul> <li><a href="https://www.tcc-africa.org/">Training Centre in Communication</a></li> <li><a href="https://twitter.com/JoyOwango">Twitter</a></li> </ul> </li> <li>Osman Aldirdiri <ul> <li><a href="https://blog.okfn.org/2017/03/30/open-data-day-sudan-2017-openness-for-advancing-research-and-discovery/">Open Sudan's Open Data Day</a></li> <li><a href="https://twitter.com/aldirdiri">Twitter</a></li> </ul> </li> <li>Jo Havemann <ul> <li><a href="https://access2perspectives.com/team/jo-havemann/">Access2Perspectives </a></li> </ul> </li> </ul>
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.