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1,163 results for “demonstration”
Sonification of data from Deep Sea Hunter Demonstrator (KM3NeT)
<p>The video present an image sonification of data obtained from the Deep Sea Hunter demonstrator. The development was grant by the European Union, under the project REINFORCE. The data was obtained during this collaboration from researchers from the KiloMeter Cube Neutrino Telescope (KM3NeT).</p> <p>"The REINFORCE project has received funding from the European Union’s Horizon 2020 project call H2020-SwafS-2018-2020 funded project Grant Agreement no. 872859"</p> <p> </p> <p>For more information visit:</p> <p>www.reinforceeu.eu/</p> <p>www.zooniverse.org/projects/reinforce/deep-sea-explorers</p>
Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
<p><strong>Background </strong></p> <p>Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population. <strong> </strong></p> <p><strong>Methods</strong></p> <p>We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2.</p> <p><strong>Results</strong></p> <p>No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways. <strong> </strong></p> <p><strong>Conclusion</strong></p> <p>In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.</p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
Data for Demonstration of quantum-digital payments
<p>Timetags that lead to coincidence count events in a 3ns window for Client and TTP.<br> For linear (commitment to M0) and diagonal (M1) basis.</p> <p>Format:<br> <uint8 c>\t<double t></p> <p>c: channel on timetagger<br> t: photon arrival time in ns</p> <p><br> TTP Channels:<br> 2: H<br> 3: V<br> 4: +<br> 5: -</p> <p>Client Channels:<br> 2: H / +<br> 4: V / -</p> <p>--------------<br> The files in folder "tagfile_example" are intended as input to the analysis scripts provided under DOI 10.5281/zenodo.7997961</p> <p> </p>
Relapsed multiple myeloma demonstrates distinct patterns of immune microenvironment and malignant cell-mediated immunosuppression
<p> </p> <table> <tbody> <tr> <td>rowID</td> <td>filename</td> <td>date</td> <td>batch</td> <td>group</td> <td>ID</td> </tr> <tr> <td>1</td> <td>20200121_bm003696_RMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>RMM</td> <td>bm003696</td> </tr> <tr> <td>2</td> <td>20200121_bm054122_DRMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>DRMM</td> <td>bm054122</td> </tr> <tr> <td>3</td> <td>20200121_bm054496_RMM_tx_02.FCS</td> <td>20200121</td> <td>1</td> <td>RMM</td> <td>bm054496</td> </tr> <tr> <td>4</td> <td>20200121_bm059645_NDMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>NDMM</td> <td>bm059645</td> </tr> <tr> <td>5</td> <td>20200121_bm060791_NDMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>NDMM</td> <td>bm060791</td> </tr> <tr> <td>6</td> <td>20200121_bm064862_DRMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>DRMM</td> <td>bm064862</td> </tr> <tr> <td>7</td> <td>20200121_REF2_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>8</td> <td>20200124_BM038232_RMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>RMM</td> <td>BM038232</td> </tr> <tr> <td>9</td> <td>20200124_BM051757_NDMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>NDMM</td> <td>BM051757</td> </tr> <tr> <td>10</td> <td>20200124_BM052673_DRMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>DRMM</td> <td>BM052673</td> </tr> <tr> <td>11</td> <td>20200124_BM053393_NDMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>NDMM</td> <td>BM053393</td> </tr> <tr> <td>12</td> <td>20200124_BM053570_RMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>RMM</td> <td>BM053570</td> </tr> <tr> <td>13</td> <td>20200124_BM059775_DRMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>DRMM</td> <td>BM059775</td> </tr> <tr> <td>14</td> <td>20200124_REF2_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>15</td> <td>20200128_3-1-BM060915-DRMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>DRMM</td> <td>BM060915</td> </tr> <tr> <td>16</td> <td>20200128_3-2-BM064896-DRMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>DRMM</td> <td>BM064896</td> </tr> <tr> <td>17</td> <td>20200128_3-3-BM059424-NDMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>NDMM</td> <td>BM059424</td> </tr> <tr> <td>18</td> <td>20200128_3-4-BM059429-NDMM_TX_02.FCS</td> <td>20200128</td> <td>3</td> <td>NDMM</td> <td>BM059429</td> </tr> <tr> <td>19</td> <td>20200128_3-5-BM043778-RMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>RMM</td> <td>BM043778</td> </tr> <tr> <td>20</td> <td>20200128_3-6-BM008007-RMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>RMM</td> <td>BM008007</td> </tr> <tr> <td>21</td> <td>20200129_3-7-ref2_tax_01.FCS</td> <td>20200129</td> <td>3</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>22</td> <td>20200204_5-1_BM054866-DRMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>DRMM</td> <td>BM054866</td> </tr> <tr> <td>23</td> <td>20200204_5-2_BM065069-DRMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>DRMM</td> <td>BM065069</td> </tr> <tr> <td>24</td> <td>20200204_5-3_BM035491-NDMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>NDMM</td> <td>BM035491</td> </tr> <tr> <td>25</td> <td>20200204_5-4_BM0333015-NDMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>NDMM</td> <td>BM0333015</td> </tr> <tr> <td>26</td> <td>20200204_5-5_BM0052990-RMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM0052990</td> </tr> <tr> <td>27</td> <td>20200204_5-5_BM0052990-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM0052990</td> </tr> <tr> <td>28</td> <td>20200204_5-5_BM052990-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM052990</td> </tr> <tr> <td>29</td> <td>20200204_5-6_BM052692-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM052692</td> </tr> <tr> <td>30</td> <td>20200204_5-7 -Ref2-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>31</td> <td>20200207_6-1_BM063515_DRMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>DRMM</td> <td>BM063515</td> </tr> <tr> <td>32</td> <td>20200207_6-2_BM054226_DRMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>DRMM</td> <td>BM054226</td> </tr> <tr> <td>33</td> <td>20200207_6-3_BM008346_NDMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>NDMM</td> <td>BM008346</td> </tr> <tr> <td>34</td> <td>20200207_6-4_BM008718_NDMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>NDMM</td> <td>BM008718</td> </tr> <tr> <td>35</td> <td>20200207_6-5_BM052764_RMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>RMM</td> <td>BM052764</td> </tr> <tr> <td>36</td> <td>20200207_6-6_Ref2_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>37</td> <td>20200211_7-1_BM061912_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM061912</td> </tr> <tr> <td>38</td> <td>20200211_7-2_BM059328_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM059328</td> </tr> <tr> <td>39</td> <td>20200211_7-3_BM065082_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM065082</td> </tr> <tr> <td>40</td> <td>20200211_7-4_BM008353_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM008353</td> </tr> <tr> <td>41</td> <td>20200211_Ref2_Tax_02.FCS</td> <td>20200211</td> <td>7</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>42</td> <td>20200214_4-1_BM062618_DRMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>DRMM</td> <td>BM062618</td> </tr> <tr> <td>43</td> <td>20200214_4-2_BM062255_DRMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>DRMM</td> <td>BM062255</td> </tr> <tr> <td>44</td> <td>20200214_4-3_BM032596_NDMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>NDMM</td> <td>BM032596</td> </tr> <tr> <td>45</td> <td>20200214_4-4_BM047845_NDMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>NDMM</td> <td>BM047845</td> </tr> <tr> <td>46</td> <td>20200214_4-5_BM042666_RMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>RMM</td> <td>BM042666</td> </tr> <tr> <td>47</td> <td>20200214_4-6_Ref2_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>REF</td> <td>REF2</td> </tr> </tbody> </table> <p> </p> <p>*files with row ID #26 and #27 need to be concatenated since they represent the same sample acquired over 2 files</p>
Data for Demonstration of a Quantum Switch in a Sagnac Configuration
<p># Data accompanying the paper"Demonstration of a Quantum Switch in a Sagnac Configuration" (arXiv:2211.12540v1).</p> <p>"datastore_H.h5" and "datastore_P.h5" contain python/pandas dataframes holding coincidences counts aquired during the measurement.</p> <p>"tomos.h5" cointains a python/pandas datafraframe holding the polarization tomography measurements on the individual polarization gadgets.</p> <p>"read.py" gives an example of how to open the files.</p> <p>The DataFrames "datastore_H.h5" and "datastore_P.h5" contain the following columns:</p> <p>- **i, j**: indices of the implemented unitaries, as defined in the paper.<br> - **run**: index of repetition of the whole measurement.<br> - **cc_h_com**: integrated coincidences aquired in the H-port of the polarization tomography stage put after the commutator output port of the sagnac interferometer.<br> - **cc_v_com**: integrated coincidences aquired in the V-port of the polarization tomography stage put after the commutator output port of the sagnac interferometer.<br> - **cc_h_acom**: integrated coincidences aquired in the H-port of the polarization tomography stage put after the anticommutator output port of the sagnac interferometer.<br> - **cc_v_acom**: integrated coincidences aquired in the V-port of the polarization tomography stage put after the anticommutator output port of the sagnac interferometer.<br> - **cc_h_com_dark**: integrated dark coincidences aquired in this port.<br> - **cc_v_com_dark**: integrated dark coincidences aquired in this port.<br> - **cc_h_acom_dark**: integrated dark coincidences aquired in this port.<br> - **cc_v_acom_dark**: integrated dark coincidences aquired in this port.<br> - **triggers_com**: integrated number of trigger events leading to coincidences in the commutator port.<br> - **triggers_acom**: integrated number of trigger events leading to coincidences in the anticommutator port. </p> <p>"datastore_H.h5" holds data acquired using horizontally polarized input light. <br> "datastore_P.h5" holds data acquired using horizontally polarized input light.</p> <p>The DataFrame "tomos.h5" contains the following columns:</p> <p>- **U**: The unitary intended to be implemented by the gadgets<br> - **U_fwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **U** gadget in forwards direction.<br> - **U_fwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **U** gadget in forwards direction.<br> - **Ur_fwd**: Unitary implemented by the **U** gadget in forwards direction, reconstructed using **U_fwd_tomoH** and **U_fwd_tomoP**<br> - **U_bwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **U** gadget in backwards direction.<br> - **U_bwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **U** gadget in backwards direction.<br> - **Ur_bwd**: Unitary implemented by the **U** gadget in backwards direction, reconstructed using **U_fwd_tomoH** and **U_fwd_tomoP**<br> - **V_fwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **V** gadget in forwards direction<br> - **V_fwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **V** gadget in forwards direction.<br> - **Vr_fwd**: Unitary implemented by the **V** gadget in forwards direction, reconstructed using **V_fwd_tomoH** and **V_fwd_tomoP**<br> - **V_bwd_tomoH**: Polarization tomography on the state created by horizontally polarized light passing through the **V** gadget in backwards direction.<br> - **V_bwd_tomoP**: Polarization tomography on the state created by diagonally polarized light passing through the **V** gadget in backwards direction.<br> - **Vr_bwd**: Unitary implemented by the **V** gadget in backwards direction, reconstructed using **V_fwd_tomoH** and **V_fwd_tomoP**.<br> - **fid_Ufwd_h**: Fidelity between the theoretically expected and experimentally obtained state U|H> in forwards direction.<br> - **fid_Ubwd_h**: Fidelity between the theoretically expected and experimentally obtained state U|H> in backwards direction.<br> - **fid_Vfwd_h**: Fidelity between the theoretically expected and experimentally obtained state V|H> in forwards direction.<br> - **fid_Vbwd_h**: Fidelity between the theoretically expected and experimentally obtained state V|H> in backwards direction.<br> - **fid_Ufwd_p**: Fidelity between the theoretically expected and experimentally obtained state U|P> in forwards direction.<br> - **fid_Ubwd_p**: Fidelity between the theoretically expected and experimentally obtained state U|P> in backwards direction.<br> - **fid_Vfwd_p**: Fidelity between the theoretically expected and experimentally obtained state V|P> in forwards direction.<br> - **fid_Vbwd_p**: Fidelity between the theoretically expected and experimentally obtained state V|P> in backwards direction.<br> - **fid_Ufwd_Ubwd_h**: Fidelity between the experimentally obtained states U|H> in forwards and backwards direction.<br> - **fid_Vfwd_Vbwd_h**: Fidelity between the experimentally obtained states V|H> in forwards and backwards direction.<br> - **fid_Ufwd_Ubwd_p**: Fidelity between the experimentally obtained states U|P> in forwards and backwards direction.<br> - **fid_Vfwd_Vbwd_p**: Fidelity between the experimentally obtained states V|P> in forwards and backwards direction.</p> <p>The format for polarization tomography data is:<br> [h,v,p,m,r,l] <br> where<br> **h** is the power acquired projecting the state in |H>,<br> **v** on |V>,<br> **p** on |+>,<br> **m** on |->,<br> **r** on |R>, and<br> **l** on |L>.</p>
Demonstrator of a rotor-fed asynchronous start for a Salient Pole Wound Field Synchronous Machine
<p>The video shows the asynchronous run-up (up to ca. 620 rpm) of a four pole, 60 kVA, 400V, 50 Hz salient pole synchronous generator. The run-up is obtained by AC-supplying a special rotor winding arrangement, capable of both exciting the machine at synchronism and providing a multiphase rotating MMF during the run-up. The armature phases, which have two parallel current paths, are conveniently short-circuited during the rotor acceleration. </p>
Unifying Skill-Based Programming and Programming by Demonstration through Ontologies
<p>Smart manufacturing requires easily reconfigurable robotic systems to increase the flexibility in presence of market uncertainties by reducing the set-up times for new tasks. One enabler of fast reconfigurability is given by intuitive robot programming methods.<br> On the one hand, offline, skill-based programming (OSP) allows the definition of new tasks by sequencing pre-defined, parameterizable building blocks termed as skills in a graphical user interface. On the other hand, Programming by Demonstration (PbD) is a well known technique that uses kinesthetic teaching for intuitive robot programming, where this work presents an approach to automatically recognize skills from the human demonstration and parameterize them using the recorded data. The approach further unifies both programming modes of OSP and PbD with the help of an ontological knowledge base and empowers the end user to choose the preferred mode for each phase of the task. In the experiments, we evaluate two scenarios with different sequences of programming modes being selected by the user to define a task. In each scenario, skills are recognized by a data-driven classifier and automatically parameterized from the recorded data. The fully defined tasks consist of both manually added and automatically recognized skills and are executed in the context of a realistic industrial assembly environment.</p>
H2020 Platone German Demonstrator Use Case 1, 2, 3 and 4 Measurement Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). This dataset contains measurement data and processed data relevant for the evaluation of UseCases (UCs) applied in the field test side.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single Point of Common Coupling (PCC). i.e., a secondary substation that includes a transformer with sensors on the LV busbar to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Description of data set:</strong></p> <p>p_tei - arithmetic mean of measured power exchange at PCC (Total residual power exchange Export/Import) measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb - arithmetic mean of measured charging/discharging power of CBES in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</p> <p>p_tcb_set – triggered charging/discharging power of CBES</p> <p>p_tei_c - Computed power exchange at PPC. That value indicates the value p_tei if no UC would have been applied (baseline).</p> <p>e_im –cumulated measured energy import (from MV grid into LV grid)</p> <p>e_ex - cumulated measured energy export (from LV grid into MV grid)</p> <p>soc – State Of Charge of CBES</p> <p>soc_max – maximum permissible SOC of CBES</p> <p>soe - State Of Energy of CBES</p> <p>soc_min – minimum permissible SOC of CBES</p> <p>id – ID of UC that is active at point of time</p> <p>setpoint - Charging/discharging power for CBES triggered by EMS (ALF-C) during active an UC</p> <p>subtype - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p>type – Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p>bulk – (yes/no) – indicates whether bulk energy import or export is active. Only relevant for UC 3 and 4.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear
<p>Examples of PET tracers synthesized via arene C-H radiofluorination (A) Maximum intensity projection (MIP) PET images of [18F]Fenoprofen (42) demonstrate higher uptake in TPA-treated mouse ear (A-1) compared with control (A-2) mouse ear. (B) PET/CT images demonstrate preferential tumor (MCF-7) accumulation of 39, compared with longer blood circulation and higher non-specific binding of 41 at 1 hour post-injection. (C) Structures of the tracers used in the preceding panels are shown.</p>
Study to Demonstrate the Efficacy (Including Inhibition of Structural Damage), Safety and Tolerability up to 2 Years of Secukinumab in Active Psoriatic Arthritis
ClinicalTrials.gov study NCT02404350. IPD Sharing: UNDECIDED. Countries: 28. Publications: 6.
Data from: Insights from a 31-year study demonstrate an inverse correlation between recreational activities and red deer fecundity, with body weight as a mediator
Open the record for dataset details and reuse information.
Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats
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Data from: Multiple refugia from penultimate glaciations in East Asia demonstrated by phylogeography and ecological modelling of an insect pest
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Historical baleen plates indicate that once abundant Antarctic blue and fin whales demonstrated distinct migratory and foraging strategies
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Variation in the location and timing of experimental severing demonstrates that the persistent rhizome serves multiple functions in a clonal forest understory herb
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Data for: Frond orientations with independent current indicators demonstrate the reclining rheotropic mode of life of several Ediacaran rangeomorph taxa
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Data from: Stuck in the mud: experimental taphonomy and computed tomography demonstrate the critical role of sediment in stabilizing the three-dimensional external morphology of arthropod carcasses during early fossil diagenesis - DRAGONFLY sessions
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MorphoGraphX2: Datasets that demonstrate how to create positional information with local coordinate systems
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Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
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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.