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Selective electroreduction of acetylene to 1,3-butadiene on iodide-induced Cuδ+–Cu0 sites (Raw Data Set)
<p>This depository contains the raw data set for the experiments performed for the publication 'Selective electroreduction of acetylene to 1,3-butadiene on iodide-induced Cuδ+–Cu0 sites', published in Nature Catalysis (<a title="https://ddec1-0-en-ctp.trendmicro.com:443/wis/clicktime/v1/query?url=https%3a%2f%2furldefense.com%2fv3%2f%5f%5fhttps%3a%2f%2fddec1%2d0%2den%2dctp.trendmicro.com%3a443%2fwis%2fclicktime%2fv1%2fquery%3furl%3dhttps%2a3a%2a2f%2a2fwww.nature.com%2a2farticles%2a2fs41929%2a2d024%2a2d01250%2a2d0%26umid%3d02b9092a%2d1af6%2d4cb5%2d849f%2d1e4e69c3362b%26auth%3d8d3ccd473d52f326e51c0f75cb32c9541898e5d5%2d49d0802b0b156dfd40e197957bc2396004387b42%5f%5f%3bJSUlJSUlJSU%21%21NLFGqXoFfo8MMQ%21riTc23aLZjIqs2CX4e3E48cfguXGobRD34gzGdqNClmD54WJ3g%5fR2kOcbDJ2ohtXGotgiSceoB28KNBBw%5ftR7GrMu3Q%24&umid=a0571516-9094-4fcf-aed3-d6b3ce8837a5&auth=8d3ccd473d52f326e51c0f75cb32c9541898e5d5-2320f313d5a4855ab4fc2c0c8713c98a5673ffa4" href="https://ddec1-0-en-ctp.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2furldefense.com%2fv3%2f%5f%5fhttps%3a%2f%2fddec1%2d0%2den%2dctp.trendmicro.com%3a443%2fwis%2fclicktime%2fv1%2fquery%3furl%3dhttps%2a3a%2a2f%2a2fwww.nature.com%2a2farticles%2a2fs41929%2a2d024%2a2d01250%2a2d0%26umid%3d02b9092a%2d1af6%2d4cb5%2d849f%2d1e4e69c3362b%26auth%3d8d3ccd473d52f326e51c0f75cb32c9541898e5d5%2d49d0802b0b156dfd40e197957bc2396004387b42%5f%5f%3bJSUlJSUlJSU%21%21NLFGqXoFfo8MMQ%21riTc23aLZjIqs2CX4e3E48cfguXGobRD34gzGdqNClmD54WJ3g%5fR2kOcbDJ2ohtXGotgiSceoB28KNBBw%5ftR7GrMu3Q%24&umid=a0571516-9094-4fcf-aed3-d6b3ce8837a5&auth=8d3ccd473d52f326e51c0f75cb32c9541898e5d5-2320f313d5a4855ab4fc2c0c8713c98a5673ffa4">https://www.nature.com/articles/s41929-024-01250-0</a>).</p>
Data set related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"
<p>This record contains raw data related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"</p>
AgriLink - Data Set on Suppliers of farm advice in 7 European countries
<p>This Data Set is derived from the WP4 of the AgriLink project.</p> <p>It is part of task T4.4 of Work Package (WP) 4 of the H2020 AgriLink project. AgriLink [Agricultural Knowledge: linking farmers, advisors and researchers to boost innovation] aims at better understanding the role of advisory services in farmers’ decision making and at boosting their contribution to innovation for sustainable development of agriculture. WP4 addresses more specifically the governance of farm advisory services. The objective of the research presented in this report is to understand the institutions that influence how farm advisory services function on the ground, and to discuss implications for the support for sustainable development innovation.</p> <p><strong>Data were collected in seven European countries: the Czech Republic, France, Greece, Poland, Portugal, Spain and the UK.</strong></p> <p><strong>Data were collected for a diversity of types of innovation: Market, Technological, Process, and Social Innovation.</strong></p> <p>The Data set was built based on interviews with farm advisory suppliers.</p> <p>In total 170 farm advisory suppliers were interviewed.</p> <p>The table below provides the distribution of interviews according to countries.</p> <table> <tbody> <tr> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Market innovation (NCRO & RETRO)</strong></p> </td> <td> <p><strong>Technological innovation (TECH)</strong></p> </td> <td> <p><strong>Process innovation (BIOP & SOIL)</strong></p> </td> <td> <p><strong>Social innovation </strong><strong>(LABO & COMM)</strong></p> </td> <td> <p><strong>TOTAL</strong></p> </td> </tr> <tr> <td> <p><strong>Czech Republic</strong></p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p>16</p> </td> <td> <p> </p> </td> <td> <p><strong>20</strong></p> </td> </tr> <tr> <td> <p><strong>France</strong></p> </td> <td> <p>14</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p><strong>25</strong></p> </td> </tr> <tr> <td> <p><strong>Greece</strong></p> </td> <td> <p>11</p> </td> <td> <p> </p> </td> <td> <p>10</p> </td> <td> <p> </p> </td> <td> <p><strong>21</strong></p> </td> </tr> <tr> <td> <p><strong>Poland</strong></p> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>18</p> </td> <td> <p><strong>24</strong></p> </td> </tr> <tr> <td> <p><strong>Portugal</strong></p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p>20</p> </td> <td> <p> </p> </td> <td> <p><strong>31</strong></p> </td> </tr> <tr> <td> <p><strong>Spain</strong></p> </td> <td> <p>9</p> </td> <td> <p> </p> </td> <td> <p>29</p> </td> <td> <p> </p> </td> <td> <p><strong>38</strong></p> </td> </tr> <tr> <td> <p><strong>UK</strong></p> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>34</strong></p> </td> <td> <p><strong>28</strong></p> </td> <td> <p><strong>75</strong></p> </td> <td> <p><strong>33</strong></p> </td> <td> <p><strong>170</strong></p> </td> </tr> </tbody> </table> <p>The data has two aims.</p> <p><strong>First, to characterise farm advisory suppliers, in terms of (table below):</strong></p> <ul> <li><strong>what do they provide?</strong></li> <li><strong>Who is in control of the supplier?</strong></li> </ul> <table> <tbody> <tr> <td><strong>What do they provide</strong></td> <td><strong>Farmers</strong></td> <td><strong>NGO</strong></td> <td><strong>Private</strong></td> <td><strong>Public</strong></td> <td><strong>semi-public</strong></td> <td><strong>Total</strong></td> </tr> <tr> <td><strong>Advice and Bookkeeping</strong></td> <td>8</td> <td> </td> <td>4</td> <td>1</td> <td> </td> <td>13</td> </tr> <tr> <td><strong>Advice and Digital tech</strong></td> <td>1</td> <td> </td> <td>3</td> <td> </td> <td> </td> <td>4</td> </tr> <tr> <td><strong>Advice and Education</strong></td> <td>2</td> <td>4</td> <td>3</td> <td>5</td> <td> </td> <td>14</td> </tr> <tr> <td><strong>Advice and Health services</strong></td> <td> </td> <td> </td> <td>1</td> <td>2</td> <td> </td> <td>3</td> </tr> <tr> <td><strong>Advice and Inputs</strong></td> <td>1</td> <td> </td> <td>14</td> <td> </td> <td> </td> <td>15</td> </tr> <tr> <td><strong>Advice and Inputs and Outputs</strong></td> <td>15</td> <td> </td> <td>5</td> <td> </td> <td> </td> <td>20</td> </tr> <tr> <td><strong>Advice and Machinery</strong></td> <td> </td> <td> </td> <td>7</td> <td> </td> <td> </td> <td>7</td> </tr> <tr> <td><strong>Advice and Outputs</strong></td> <td>8</td> <td>1</td> <td>8</td> <td>2</td> <td> </td> <td>19</td> </tr> <tr> <td><strong>Advice and Research</strong></td> <td>2</td> <td>2</td> <td>5</td> <td>12</td> <td>1</td> <td>22</td> </tr> <tr> <td><strong>Only advice and training</strong></td> <td>16</td> <td> </td> <td>26</td> <td>10</td> <td>1</td> <td>53</td> </tr> <tr> <td><strong>Total</strong></td> <td>53</td> <td>7</td> <td>76</td> <td>32</td> <td>2</td> <td>170</td> </tr> </tbody> </table> <p><strong>Second, we have set a series of variables to characterise the services they provide. The main variables are:</strong></p> <ul> <li><strong>Number of advisors of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of advisors</strong></td> <td><strong>Number of organisations in that group</strong></td> </tr> <tr> <td><strong>[0:5]</strong></td> <td>96</td> </tr> <tr> <td><strong>]10:50]</strong></td> <td>35</td> </tr> <tr> <td><strong>]5:10]</strong></td> <td>16</td> </tr> <tr> <td><strong>>50</strong></td> <td>19</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Percentage of advisors in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>% of advisors</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>43</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>17</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>30</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>70</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>10</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Share of back-office activities in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Share of back-office (%)</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>41</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>26</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>66</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>24</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>13</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Number of farmers client of the supplier per advisor</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of clients per organisation</strong></td> <td><strong>Number of organisation</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>31</td> </tr> <tr> <td><strong>[25:75[</strong></td> <td>43</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>3</td> </tr> <tr> <td><strong>[75:175[</strong></td> <td>28</td> </tr> <tr> <td><strong>>175</strong></td> <td>36</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>29</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Main advisory method</strong></li> </ul> <table> <tbody> <tr> <td><strong>Main Advisory method</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>Group Advice</strong></td> <td>19</td> </tr> <tr> <td><strong>IT tool (app, software…)</strong></td> <td>2</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>1</td> </tr> <tr> <td><strong>One to One Advice</strong></td> <td>129</td> </tr> <tr> <td><strong>Phone or web helpdesk</strong></td> <td>15</td> </tr> <tr> <td><strong>Publications</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li>Main funding source</li> </ul> <table> <tbody> <tr> <td><strong>Main funding source</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>EU funds</strong></td> <td>15</td> </tr> <tr> <td><strong>Fee-for-advice</strong></td> <td>46</td> </tr> <tr> <td><strong>Joint trade</strong></td> <td>42</td> </tr> <tr> <td><strong>Membership</strong></td> <td>11</td> </tr> <tr> <td><strong>Membership fee</strong></td> <td>6</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>16</td> </tr> <tr> <td><strong>Public funding</strong></td> <td>3</td> </tr> <tr> <td><strong>Public funds</strong></td> <td>4</td> </tr> <tr> <td><strong>State budget</strong></td> <td>27</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <p>More detailed information about the variables collected can be found in the questionnaire that is available in the appendix of the deliverable D4.2 of AgriLink</p> <p> </p>
Data set of "Context-sensitive Requirements Search in Natural Language Specifications"
<p>We use this data set for the evaluation of our Context-sensitive Requirements Search approach. It contains three files and 15 searches.</p> <p> </p> <p><strong>Attribution</strong></p> <p>The foundation of this data set is PURE:<br> A. Ferrari, G. O. Spagnolo, and S. Gnesi. <em>PURE: a Dataset of Public Requirements Documents</em>. Version 1.0. Sept. 2018.<br> Available at: <a href="https://doi.org/10.5281/zenodo.1414117">https://doi.org/10.5281/zenodo.1414117</a></p> <p>The original data is licensed under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <p> </p> <p><strong>Annotations</strong></p> <ul> <li>Each of the provided files contains annotations that we created with Label Studio for the corresponding file of the PURE data set.</li> <li>These annotations depict the search results of Plain Keyword Search (PKS) and the corresponding gold standard.</li> <li>The following is an example of an annotation in the .json file:</li> </ul> <pre><code class="language-json">{ "id": "UnaxUjB6oY", "meta": { "text": ["0"] }, "type": "labels", "value": { "end": "/text()[2]", "text": "speed", "start": "/text()[2]", "labels": ["speed: train speed profile"], "endOffset": 364, "startOffset": 359, "globalOffsets": { "end": 1403, "start": 1398 } }, "origin": "manual", "to_name": "text", "from_name": "ner" },</code></pre> <p> </p> <p><strong>Important Json attributes to consider</strong></p> <p><strong>Meta</strong></p> <p>We encode the relevance of each result in its metadata.</p> <p><em>Example:</em></p> <pre><code class="language-json">"meta": { "text": ["0"] },</code></pre> <ul> <li>1 = relevant for this search and found by PKS</li> <li>0 = irrelevant for this search but found by PKS</li> <li>-1 = relevant for this search and not found by PKS</li> </ul> <p> </p> <p><strong>Labels</strong></p> <p>This element is the search identifier we use in Label Studio. It corresponds to the table in the thesis that describes each search in detail.</p> <p><em>Example:</em></p> <pre><code class="language-json">"labels": ["speed: train speed profile"],</code></pre> <p> </p> <p><strong>GlobalOffsets</strong></p> <p>"globalOffsets" describes the position of a label in the text.</p> <p><em>Example:</em></p> <pre><code class="language-json">"globalOffsets": { "end": 1403, "start": 1398 }</code></pre> <p> </p>
CRDM@NCRC Test Data Set Wizard and Witch vaccination project against dragon pox
<p>This is a dummy data set to test restricted access. We created test data about vaccination of wizards and witches against dragon pox. The data set contains over 60 entries of the vaccination.</p>
Data Set on Local Government Indicators in Chile
<p><strong>Data Set on Local Government Indicators in Chile</strong></p> <p>This repository contains a dataset in progress on local government indicators. The open-access version is available at <a href="https://doi.org/10.5281/zenodo.7568387">https://doi.org/10.5281/zenodo.7568387</a></p> <p><strong>GitHub repository:</strong> <a href="https://github.com/bgonzalezbustamante/local-gov-indicators">https://github.com/bgonzalezbustamante/local-gov-indicators</a></p>
Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law
<p>Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law</p> <p>Labeling in plots are slightly different with the preprint paper.</p>
SMARTFARM Eddy Covariance Demonstration Data Set of Five Agro-ecosystem Sites
<p>Demonstration data set from five eddy covariance towers at agro-ecosystem sites that are funded under the DOE ARPA-E SMARTFARM program in Phase I. Data set is provided to restricted users for the purpose of operational and performance qualification only. Data sets include 13-26 weeks of all 30 minute interval data for all sensor modalities per the sensor workbook and FOA. Full data sets will be published through Ameriflux at https://ameriflux.lbl.gov/ starting August 12, 2022, with annual updates thereafter. Some data, such as methane and nitrous oxide are embargoed.</p> <p>This work was funded through the U.S. Department of Energy, ARPA-E, under Cooperative Agreement DE-AR0001228 led by ARVA Intelligence Corp. (https://www.arvaintelligence.com) in collaboration with Lawrence Berkeley National Laboratory (LBNL).</p>
Data set of Sepsis awareness at the university hospital level: a survey-based cross-sectional study
<p><strong>This is the dataset for the SAfE survey</strong></p> <p>We conducted a survey on nurses and physicians distributed over all adult departments of the Lausanne University Hospital (LUH) and local paramedics. The survey aimed to assess professionals’ demographics (age, profession, seniority, unit of activity), prior sepsis education, perceptions, knowledge of sepsis epidemiology, definition, recognition and management. Correlation between surveyed personel and sepsis perceptions and knowledge were assessed with univariate logistic regression models.</p> <p><strong>Methods:</strong></p> <p>The research team designed a survey inspired from previously published surveys assessing knowledge and awareness of sepsis.</p> <p>The questions were tailored to the profession (clinical scenario adapted activity sector - medicine, surgery, emergency department or gynecology). The survey was written and completed in French. Each section of the survey (paramedics’, nurses’ and physicians’ section) was submitted to three focus groups consisting of 3 to 6 participants of each profession, commonly involved in care of patients with sepsis. These focus groups assessed the applicability and appropriateness (validity) of the survey. The focus group were constituted of nurses, physicians and paramedics of all seniority levels. Their primary task was in assessing whether formulations and relevance of questions were adequate. The survey was revised using feedback from the groups. Surveys of nursing staff and paramedics were more focused on screening, initial evaluation and early management whereas physicians were also tested on diagnosis and management. Responses options included Likert-type scales, binary (e.g. “yes/no”) or multiple choices. Each question was locked upon answering, which prevented post hoc changes that could be influenced by information provided at a later stage of the survey. The final survey contained questions on participants’ demographic characteristics (5/7/6 questions for nurses/paramedics/physicians), sepsis continuous education (3/3/3 questions), self-evaluation of sepsis knowledge and clinical management (2/2/2 questions), definitions, scores and epidemiology (11/12/14 questions), and sepsis management (4/4/5 questions). The survey was developed in REDCap (Research Electronic Data Capture) software so as to automatically export participants’ responses to a database. Surveys are provided as supplementary material (supp. meth. survey).</p> <p>Participants were recruited between January 20 and October 10, 2020. We aimed for a convenience sample size of 1,000 persons (approx. 20% of the active HCPs) distributed over all departments (Emergency department (ED), intensive care unit (ICU), Medicine, Paramedic, Psychiatry, or Surgery) and professions (paramedics, nurses and physicians) to reach 20% of LUH staff considered HCPs, being as representative as possible. Pediatrics and neonatology staff (not covered by Sepsis-3 consensus definitions) as well as nurses and physicians not in daily contact with patients (i.e., who were working in research team or in administration) were excluded. We favored a supervised approach rather than a dissemination of the survey to all HCPs by email. Participants answered the online survey under trained interviewer supervision so as maximize data quality and to avoid biased responses (internet queries, discussions between colleagues). Furthermore, to avoid multiple answers by a same HCP, surveys were accessed by QR-code only available at screening; timing of survey completion was registered and email addresses were registered.</p> <p>Thus, participants were screened amongst the medical (n=1664) and nursing staff (n=2463) in daily contact with patients of LUH and amongst paramedics of the Canton of Vaud (n=290) during the screening period. Screening by trained interviewers took place during scheduled patient hand-offs, seminars or group meetings, as permitted by heads of units. Participation was voluntary and anonymous. Participants completed the online survey using tablets or smartphones (participants’ or provided by the investigators).</p> <p><strong>Results:</strong></p> <p>Between January and October 2020, 1,116 of 1,216 contacted professionals completed the survey (participation rate 91.8%). These participants represented about 25% of the workforce (n=4417) – i.e., 25.1% of nurses (619/2,463), 20.9 % of physicians (348/1,664),and 51.4% of paramedics (149/290). Only 13% of participants (physicians: 28.4%, nurses: 5.9%, paramedics: 6.8%) correctly identified the Sepsis-3 consensus definition. Similarly, less than 50% of participants (physicians: 48.6%, nurses: 10.0%) selected the SOFA score as a sepsis defining score for infected patients. Furthermore, 24% of participants properly identified the qSOFA score as a predictor of increased mortality; and 6% selected correctly its components. For a suspected sepsis, 96.1%, 91.6% and 75.8% of physicians respectively chose blood cultures, broad-spectrum antibiotics and fluid resuscitation as required interventions; 76.4% and 18.2% of physicians requested initial measures within 1 and 3 hours, respectively. For physicians, recent training correlated with awareness regarding definitions, SOFA and qSOFA score use and components: ORs (95%CI) 2.2 (1.4-3.6), 4.3 (2.7-6.7), 3.4 (2.2-5.2), and 2.6 (1.5-4.6), respectively).</p> <p><strong>Conclusions:</strong></p> <p>We identify a deficit of awareness among physicians, nurses and paramedics at LUH correlating with a lack of sepsis-specific training.</p>
Data set for "Stability limitations of optical frequency transfer in telecommunication DWDM networks"
<p>Here we share the relevant data of the manuscript “Stability limitations of optical frequency transfer in telecommunication DWDM networks”.</p> <p>The files:</p> <ul> <li>“fig_2a_total.txt”</li> <li>“fig_2a_diff.txt”</li> </ul> <p>contains respectively the timeseries data of phase fluctuations of total delay and differential delay between two fibers in 50 km-long soil-deployed cable. Same results for 110 km-long aerial cable are in files:</p> <ul> <li>“fig_2b_total.txt”</li> <li>“fig_2b_diff.txt”</li> </ul> <p>The files:</p> <ul> <li>“fig_3_aerial_MDEV_points.txt”</li> <li>“fig_3_soil_MDEV_points.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the differential delay fluctuations in 110 km-long aerial and 70 km-long soil-deployed cable. For short averaging the stability is limited by the noise of time interval counter (MTC 108). Before MDEV calculations the differential delay fluctuations was divided by factor of two, as a half of these fluctuations is present at the output of a stabilized system.</p> <p>The files:</p> <ul> <li>“fig_4_soil_MDEV_points_CW-total.txt”</li> <li>“fig_4_soil_MDEV_points_CW-diff.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the total and differential delay fluctuations in 70km soil-deployed cable, measured with the unmodulated coherent optical carrier.</p> <p>The files:</p> <ul> <li>“fig_7a_EDFA_phase.txt” and “fig_7a_EDFA_temp.txt”</li> <li>“fig_7b_Raman_phase.txt” and “fig_7b_Raman_temp.txt”</li> <li>“fig_7c_ROADM_phase.txt” and “fig_7c_ROADM_temp.txt”</li> </ul> <p>contains the timeseries data of simultaneous measurement of phase and temperature changes in individual cards (modules) of the DWDM system: EDFA amplifier, card containing the Raman module, as well as ROADM (Reconfigurable Optical Add Drop Multiplexer) module.</p> <p>Time series data of the fluctuations of the total and differential propagation delay of the signal transfer in the unmodified DWDM system (over 1500 km long route) include files:</p> <ul> <li>“fig_9_total_phase_fluct_part1”, “fig_9_total_phase_fluct_part2”, “fig_9_total_phase_fluct_part1” </li> <li>“fig_9_diff_phase_fluct_part1”, “fig_9_diff_phase_fluct_part2”, “fig_9_diff_phase_fluct_part3”</li> </ul> <p>The files:</p> <ul> <li>“fig_10_mdev_acusticNOISE.txt”</li> <li>“fig_10_mdev_air-conditioning_ON.txt”</li> <li>“fig_10_mdev_air-conditioning_MODIFIED.txt”</li> </ul> <p>contains points of modify Allan deviation that shows respectively: the influence of acoustic noise, stability of the frequency transfer in DWDM network in the case of sub-optimal (asymmetric) cooling within a single ILA node, while the last file shows the stability of the transfer after optimizing the airflow within this node.</p> <ul> </ul>
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</p>
Data set for Typology of Standard Negation in South Asia
<p>The R script contains code to reproduce all figures. It also generates the binary data out of the data set for all languages. The languages.txt file contains data for all languages in the shape illustrated in Table 4 (with additional information on the languages) in the thesis. The binary_data.txt is left empty on purpose since the script fills it out.</p> <p>The R script requires two files in the same directory: the empty binary_data.txt sheet and the filled out languages.txt sheet.</p> <p>The former is a blank which will be filled out within R. The latter is basis for most of the commands and will be supplemented with additional information, again, within R. Thus, some of the information is not stored in the .txt files but accessible after running the script.</p>
integration of two scRNA-seq data sets covering 15 human liver donors (Ramachandran, 2019 Nature and Nkongolo, 2023 J Clin Invest)
<p>rds file for R software, containing a seurat object. Load with after calling Seurat library. https://satijalab.org/seurat/</p> <p>Original data matrices with raw readcounts were downloaded from NCBI GEO</p>
NMR data set from human and mice serum for ketone bodies evaluation
<p>This dataset contains the raw data from nuclear magnetic resonance analysis of serum samples from human and mice subjects. </p> <p> </p>
Data set for the article 'Electroreduction of CO to 2.8 A cm-2 C2+ Products: Maximizing Efficiency with Minimalist Electrode Design Featuring a Mesopore-Rich Hydrophobic Copper Catalyst Layer'
<p>This depository contains the data set for the article 'Electroreduction of CO to 2.8 A cm-2 C2+ Products: Maximizing Efficiency with Minimalist Electrode Design Featuring a Mesopore-Rich Hydrophobic Copper Catalyst Layer' (<a href="https://doi.org/10.1002/advs.202405938">https://doi.org/10.1002/advs.202405938</a>)</p>
Metabolomics data set for the project EHP-BFNU-OVNKM-4-139-2024
Open the record for dataset details and reuse information.
Data set for Sinhala Textbook Summarization (Grade 6)
<p>This data set was derived to fine-tune GPT-3 models, to support auto-summarization in Sinhala.</p> <p> </p> <p>The attached datasets were validated by School experts (teachers with more than 10 years of teaching experience in the Sinhala Language for grade 6 students)</p>
Data set related to article "PBX1-directed stem cell transcriptional program drives tumor progression in myeloproliferative neoplasm"
<p>This record contains raw data related to article "PBX1-directed stem cell transcriptional program drives tumor progression in myeloproliferative neoplasm"</p> <p>PBX1 regulates the balance between self-renewal and differentiation of hematopoietic stem cells and maintains proto-oncogenic transcriptional pathways in early progenitors. Its increased expression was found in myeloproliferative neoplasm (MPN) patients bearing the JAK2V617F mutation. To investigate if PBX1 contributes to MPN, and to explore its potential as therapeutic target, we generated the JP mouse strain, in which the human JAK2 mutation is induced in the absence of PBX1. Typical MPN features, such as thrombocythemia and granulocytosis, did not develop without PBX1, while erythrocytosis, initially displayed by JP mice, gradually resolved over time; splenic myeloid metaplasia and in vitro cytokine independent growth were absent upon PBX1 inactivation. The aberrant transcriptome in stem/progenitor cells from the MPN model was reverted by the absence of PBX1, demonstrating that PBX1 controls part of the molecular pathways deregulated by the JAK2V617F mutation. Modulation of the PBX1-driven transcriptional program might represent a novel therapeutic approach.</p>
Ecotoxicity data realted to the ELECTRA 826244 H2020 project Deliverable 2.1 entitled "Description of the prototypes set-up for field testing"
<p>Ecotoxicity testing was carried out within the frame of the ELECTRA No. 826244 H2020 project. Samples were provided by partners from laboratory experiments. This dataset contains ecotoxicity data related to technologies described in Deliverable 2.1 entitled Description of the prototypes set-up for field testing.</p>
Duke Spleen Data Set
<p>CT and MRI images with associated binary spleen segmentation masks; includes axial and coronal images, MRI includes SSFSE and T1w opposed phase contrasts; includes a range of normal and abnormal spleen sizes and shapes in the setting of chronic liver disease.</p>
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.