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536 results for “Office”
DK9 - Multi-apartment building & Office - Kibæk (Denmark)
<p>Data files for building: DK9 - Multi-apartment building & Office - Kibæk (Denmark)</p> <p>Languages: Danish, English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
Data supporting research on journalistic production on science by press offices of universities and university centers in Rio Grande do Sul (2016)
<p>These data are part of a final paper called Scientific Journalism in Community Higher Education Institutions in Rio Grande do Sul. The work was developed at the University of Vale do Taquari - Univates, between 2016 and 2017. The data is in Portuguese. The abstract of the paper is available below. Science occupies an important place in today's society. It is what has allowed us to reach our current stages of intellectual development and also to achieve memorable feats as a species. Science is produced, to a large extent, in the academic environment. This monograph focuses its efforts on trying to understand how 15 universities and university centers in the state of Rio Grande do Sul, partners in the Consortium of Community Universities of Rio Grande do Sul (Comung), carry out the dissemination of their academic production. It is understood that it is important to disseminate scientific information to the population so that individuals can critically evaluate the actions developed in the field of science. The general objective of this study is to investigate the production of scientific news in higher education institutions linked to Comung, as well as to characterize the relationships between the actors and processes related to the journalistic dissemination of science produced in these same institutions. The qualitative and quantitative analysis of the scientific dissemination texts was directed to the news published by the organizations, through an exploratory study that mapped all the production of the press offices between January and August 2016. Subsequently, a qualitative analysis of the discourse of press officers was carried out, after applying a questionnaire. Throughout the investigation, the hypothesis that the institutions disseminate their scientific production was confirmed, but they do so in markedly different ways. The study is available at this link: https://www.univates.br/bdu/items/4cdf0835-1fbe-425a-a216-2a511e9aabf8. </p>
LoLiPoP-IoT TU/e Dataset: Preferred Illuminance at Different Correlated Color Temperatures for Paper- and Tablet-based Reading Tasks in an Office Environment
<p>The dataset has the following <strong>576</strong> (=i.e., <strong>32</strong> Participants × <strong>3</strong> CCTs × <strong>2</strong> task media × <strong>3</strong> repetitions) preferred illuminance values (unit: lx).</p> <table> <tbody> <tr> <td> </td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> <td><strong>Paper</strong></td> </tr> <tr> <td><strong>Participant ID</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>1098.00</td> <td>1668.00</td> <td>452.70</td> <td>396.20</td> <td>812.70</td> <td>589.60</td> <td>1139.00</td> <td>603.40</td> <td>502.60</td> </tr> <tr> <td><strong>2</strong></td> <td>275.90</td> <td>322.20</td> <td>316.00</td> <td>455.60</td> <td>412.30</td> <td>387.40</td> <td>382.30</td> <td>346.10</td> <td>411.10</td> </tr> <tr> <td><strong>3</strong></td> <td>2872.00</td> <td>399.70</td> <td>304.60</td> <td>1814.00</td> <td>339.80</td> <td>574.70</td> <td>1750.00</td> <td>725.60</td> <td>442.20</td> </tr> <tr> <td><strong>4</strong></td> <td>843.20</td> <td>686.40</td> <td>1088.00</td> <td>755.40</td> <td>1001.00</td> <td>818.20</td> <td>817.10</td> <td>790.70</td> <td>810.40</td> </tr> <tr> <td><strong>5</strong></td> <td>541.00</td> <td>404.10</td> <td>561.10</td> <td>513.50</td> <td>380.20</td> <td>419.70</td> <td>335.20</td> <td>343.30</td> <td>459.10</td> </tr> <tr> <td><strong>6</strong></td> <td>308.30</td> <td>289.90</td> <td>237.20</td> <td>485.20</td> <td>437.00</td> <td>403.90</td> <td>385.90</td> <td>462.80</td> <td>478.00</td> </tr> <tr> <td><strong>7</strong></td> <td>1111.00</td> <td>1531.00</td> <td>1280.00</td> <td>1422.00</td> <td>1273.00</td> <td>1812.00</td> <td>1408.00</td> <td>1618.00</td> <td>1734.00</td> </tr> <tr> <td><strong>8</strong></td> <td>744.20</td> <td>545.00</td> <td>462.90</td> <td>605.50</td> <td>616.00</td> <td>575.10</td> <td>1020.00</td> <td>721.20</td> <td>661.10</td> </tr> <tr> <td><strong>9</strong></td> <td>422.50</td> <td>707.40</td> <td>363.40</td> <td>460.30</td> <td>514.00</td> <td>353.40</td> <td>433.00</td> <td>463.80</td> <td>481.60</td> </tr> <tr> <td><strong>10</strong></td> <td>833.00</td> <td>626.80</td> <td>595.60</td> <td>773.60</td> <td>727.40</td> <td>363.40</td> <td>746.90</td> <td>723.70</td> <td>214.30</td> </tr> <tr> <td><strong>11</strong></td> <td>379.20</td> <td>689.10</td> <td>703.60</td> <td>526.80</td> <td>711.30</td> <td>981.40</td> <td>431.40</td> <td>511.20</td> <td>873.10</td> </tr> <tr> <td><strong>12</strong></td> <td>683.30</td> <td>764.60</td> <td>1124.00</td> <td>995.10</td> <td>990.10</td> <td>1116.00</td> <td>901.10</td> <td>962.50</td> <td>1296.00</td> </tr> <tr> <td><strong>13</strong></td> <td>318.30</td> <td>406.10</td> <td>492.20</td> <td>424.80</td> <td>531.40</td> <td>549.80</td> <td>510.70</td> <td>471.70</td> <td>529.20</td> </tr> <tr> <td><strong>14</strong></td> <td>228.80</td> <td>200.40</td> <td>233.30</td> <td>326.00</td> <td>322.30</td> <td>342.00</td> <td>425.40</td> <td>337.70</td> <td>411.10</td> </tr> <tr> <td><strong>15</strong></td> <td>252.10</td> <td>296.60</td> <td>293.60</td> <td>323.90</td> <td>318.40</td> <td>351.70</td> <td>328.10</td> <td>321.50</td> <td>572.70</td> </tr> <tr> <td><strong>16</strong></td> <td>473.70</td> <td>682.90</td> <td>709.00</td> <td>468.60</td> <td>855.50</td> <td>896.50</td> <td>611.00</td> <td>1165.00</td> <td>846.30</td> </tr> <tr> <td><strong>17</strong></td> <td>388.20</td> <td>266.90</td> <td>300.00</td> <td>386.30</td> <td>404.30</td> <td>372.30</td> <td>383.60</td> <td>364.20</td> <td>370.60</td> </tr> <tr> <td><strong>18</strong></td> <td>626.90</td> <td>740.70</td> <td>891.80</td> <td>644.60</td> <td>815.60</td> <td>938.50</td> <td>541.50</td> <td>558.20</td> <td>780.10</td> </tr> <tr> <td><strong>19</strong></td> <td>1025.00</td> <td>558.70</td> <td>535.70</td> <td>1056.00</td> <td>747.30</td> <td>1608.00</td> <td>819.70</td> <td>1092.00</td> <td>1259.00</td> </tr> <tr> <td><strong>20</strong></td> <td>661.80</td> <td>533.20</td> <td>464.60</td> <td>1093.00</td> <td>861.80</td> <td>1070.00</td> <td>894.60</td> <td>738.40</td> <td>1459.00</td> </tr> <tr> <td><strong>21</strong></td> <td>466.60</td> <td>401.70</td> <td>455.00</td> <td>542.90</td> <td>550.00</td> <td>491.30</td> <td>482.00</td> <td>540.60</td> <td>431.10</td> </tr> <tr> <td><strong>22</strong></td> <td>1093.00</td> <td>1037.00</td> <td>1106.00</td> <td>612.30</td> <td>777.20</td> <td>1041.00</td> <td>353.00</td> <td>848.10</td> <td>478.20</td> </tr> <tr> <td><strong>23</strong></td> <td>688.80</td> <td>1034.00</td> <td>746.90</td> <td>752.60</td> <td>1301.00</td> <td>817.80</td> <td>717.70</td> <td>730.20</td> <td>893.70</td> </tr> <tr> <td><strong>24</strong></td> <td>468.00</td> <td>231.50</td> <td>150.20</td> <td>666.90</td> <td>304.20</td> <td>282.40</td> <td>486.20</td> <td>319.30</td> <td>276.60</td> </tr> <tr> <td><strong>25</strong></td> <td>646.80</td> <td>1064.00</td> <td>743.10</td> <td>1072.00</td> <td>996.10</td> <td>961.20</td> <td>623.60</td> <td>1203.00</td> <td>1026.00</td> </tr> <tr> <td><strong>26</strong></td> <td>307.90</td> <td>224.70</td> <td>255.40</td> <td>410.10</td> <td>331.50</td> <td>331.80</td> <td>259.00</td> <td>354.60</td> <td>311.10</td> </tr> <tr> <td><strong>27</strong></td> <td>609.00</td> <td>661.80</td> <td>526.00</td> <td>451.50</td> <td>496.20</td> <td>557.70</td> <td>421.30</td> <td>475.60</td> <td>594.70</td> </tr> <tr> <td><strong>28</strong></td> <td>456.40</td> <td>479.60</td> <td>412.40</td> <td>458.60</td> <td>314.70</td> <td>349.90</td> <td>445.40</td> <td>331.80</td> <td>445.80</td> </tr> <tr> <td><strong>29</strong></td> <td>546.40</td> <td>921.10</td> <td>872.40</td> <td>906.50</td> <td>1001.00</td> <td>873.70</td> <td>576.90</td> <td>897.10</td> <td>840.70</td> </tr> <tr> <td><strong>30</strong></td> <td>438.90</td> <td>398.30</td> <td>372.90</td> <td>559.30</td> <td>344.60</td> <td>475.30</td> <td>359.90</td> <td>314.90</td> <td>403.50</td> </tr> <tr> <td><strong>31</strong></td> <td>667.10</td> <td>554.10</td> <td>449.70</td> <td>737.10</td> <td>730.10</td> <td>589.60</td> <td>789.00</td> <td>474.60</td> <td>603.80</td> </tr> <tr> <td><strong>32</strong></td> <td>1234.00</td> <td>1198.00</td> <td>1007.00</td> <td>849.00</td> <td>1158.00</td> <td>770.00</td> <td>909.70</td> <td>989.80</td> <td>726.70</td> </tr> </tbody> </table> <p> </p> <p> </p> <table> <tbody> <tr> <td><strong>Participant ID</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> <td><strong>Tablet</strong></td> </tr> <tr> <td> </td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 1 (3000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 2 (4000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> <td><strong>CCT 3 (6000 K)</strong></td> </tr> <tr> <td><strong>1</strong></td> <td>154.80</td> <td>149.50</td> <td>98.75</td> <td>630.00</td> <td>288.90</td> <td>304.10</td> <td>181.80</td> <td>192.80</td> <td>199.60</td> </tr> <tr> <td><strong>2</strong></td> <td>179.40</td> <td>225.40</td> <td>241.00</td> <td>242.60</td> <td>316.80</td> <td>372.50</td> <td>206.40</td> <td>333.80</td> <td>275.20</td> </tr> <tr> <td><strong>3</strong></td> <td>681.70</td> <td>662.10</td> <td>703.70</td> <td>484.90</td> <td>662.70</td> <td>468.90</td> <td>2085.00</td> <td>537.40</td> <td>385.70</td> </tr> <tr> <td><strong>4</strong></td> <td>632.10</td> <td>1171.00</td> <td>998.30</td> <td>613.10</td> <td>1012.00</td> <td>818.40</td> <td>678.30</td> <td>693.70</td> <td>675.30</td> </tr> <tr> <td><strong>5</strong></td> <td>421.70</td> <td>349.30</td> <td>234.40</td> <td>392.50</td> <td>303.40</td> <td>280.20</td> <td>307.20</td> <td>428.70</td> <td>268.00</td> </tr> <tr> <td><strong>6</strong></td> <td>217.40</td> <td>206.00</td> <td>198.10</td> <td>324.40</td> <td>304.40</td> <td>343.70</td> <td>283.70</td> <td>285.70</td> <td>293.10</td> </tr> <tr> <td><strong>7</strong></td> <td>992.50</td> <td>1338.00</td> <td>1807.00</td> <td>1849.00</td> <td>1301.00</td> <td>1423.00</td> <td>1637.00</td> <td>1442.00</td> <td>1700.00</td> </tr> <tr> <td><strong>8</strong></td> <td>840.20</td> <td>1131.00</td> <td>778.60</td> <td>891.20</td> <td>1019.00</td> <td>651.80</td> <td>810.80</td> <td>1016.00</td> <td>866.20</td> </tr> <tr> <td><strong>9</strong></td> <td>391.40</td> <td>337.40</td> <td>355.70</td> <td>314.90</td> <td>510.80</td> <td>463.60</td> <td>209.80</td> <td>450.60</td> <td>435.30</td> </tr> <tr> <td><strong>10</strong></td> <td>493.40</td> <td>535.00</td> <td>691.80</td> <td>657.60</td> <td>740.50</td> <td>342.60</td> <td>1710.00</td> <td>781.00</td> <td>339.60</td> </tr> <tr> <td><strong>11</strong></td> <td>361.70</td> <td>277.90</td> <td>431.00</td> <td>342.80</td> <td>327.80</td> <td>337.60</td> <td>350.40</td> <td>237.20</td> <td>308.90</td> </tr> <tr> <td><strong>12</strong></td> <td>857.10</td> <td>1114.00</td> <td>1348.00</td> <td>1302.00</td> <td>1408.00</td> <td>1294.00</td> <td>1031.00</td> <td>1142.00</td> <td>1078.00</td> </tr> <tr> <td><strong>13</strong></td> <td>371.80</td> <td>358.20</td> <td>731.40</td> <td>320.10</td> <td>459.50</td> <td>360.00</td> <td>440.40</td> <td>507.10</td> <td>436.90</td> </tr> <tr> <td><strong>14</strong></td> <td>361.20</td> <td>267.00</td> <td>285.80</td> <td>414.80</td> <td>398.70</td> <td>365.90</td> <td>411.50</td> <td>381.50</td> <td>392.30</td> </tr> <tr> <td><strong>15</strong></td> <td>376.00</td> <td>297.80</td> <td>294.20</td> <td>326.20</td> <td>330.50</td> <td>351.70</td> <td>327.90</td> <td>397.00</td> <td>357.00</td> </tr> <tr> <td><strong>16</strong></td> <td>221.20</td> <td>557.40</td> <td>536.30</td> <td>885.40</td> <td>518.90</td> <td>688.50</td> <td>192.40</td> <td>703.50</td> <td>568.90</td> </tr> <tr> <td><strong>17</strong></td> <td>435.70</td> <td>479.40</td> <td>444.50</td> <td>391.00</td> <td>452.90</td> <td>426.60</td> <td>421.40</td> <td>390.60</td> <td>397.80</td> </tr> <tr> <td><strong>18</strong></td> <td>431.50</td> <td>728.60</td> <td>985.70</td> <td>319.40</td> <td>542.10</td> <td>890.40</td> <td>326.90</td> <td>696.70</td> <td>575.80</td> </tr> <tr> <td><strong>19</strong></td> <td>470.80</td> <td>586.10</td> <td>476.90</td> <td>667.10</td> <td>570.10</td> <td>614.20</td> <td>803.80</td> <td>638.80</td> <td>679.50</td> </tr> <tr> <td><strong>20</strong></td> <td>186.10</td> <td>386.50</td> <td>335.60</td> <td>457.40</td> <td>471.20</td> <td>370.90</td> <td>324.70</td> <td>292.60</td> <td>273.40</td> </tr> <tr> <td><strong>21</strong></td> <td>420.80</td> <td>494.30</td> <td>447.60</td> <td>500.80</td> <td>470.30</td> <td>613.00</td> <td>581.80</td> <td>411.40</td> <td>361.40</td> </tr> <tr> <td><strong>22</strong></td> <td>510.20</td> <td>221.60</td> <td>228.60</td> <td>300.80</td> <td>285.50</td> <td>282.30</td> <td>477.30</td> <td>326.50</td> <td>185.20</td> </tr> <tr> <td><strong>23</strong></td> <td>202.20</td> <td>294.20</td> <td>252.00</td> <td>261.60</td> <td>308.80</td> <td>259.90</td> <td>476.10</td> <td>157.80</td> <td>313.10</td> </tr> <tr> <td><strong>24</strong></td> <td>464.10</td> <td>477.30</td> <td>516.50</td> <td>629.00</td> <td>516.70</td> <td>568.30</td> <td>842.50</td> <td>507.90</td> <td>613.20</td> </tr> <tr> <td><strong>25</strong></td> <td>451.80</td> <td>288.20</td> <td>444.10</td> <td>317.20</td> <td>326.40</td> <td>324.20</td> <td>456.40</td> <td>299.80</td> <td>294.80</td> </tr> <tr> <td><strong>26</strong></td> <td>157.40</td> <td>104.90</td> <td>198.70</td> <td>343.70</td> <td>328.60</td> <td>316.30</td> <td>157.40</td> <td>210.90</td> <td>225.10</td> </tr> <tr> <td><strong>27</strong></td> <td>510.00</td> <td>488.90</td> <td>552.30</td> <td>467.70</td> <td>572.20</td> <td>511.20</td> <td>440.10</td> <td>587.80</td> <td>376.60</td> </tr> <tr> <td><strong>28</strong></td> <td>257.20</td> <td>195.70</td> <td>240.90</td> <td>275.90</td> <td>286.00</td> <td>269.70</td> <td>410.00</td> <td>219.40</td> <td>235.40</td> </tr> <tr> <td><strong>29</strong></td> <td>732.00</td> <td>614.40</td> <td>567.20</td> <td>1046.00</td> <td>664.60</td> <td>693.30</td> <td>711.40</td> <td>733.80</td> <td>645.80</td> </tr> <tr> <td><strong>30</strong></td> <td>280.90</td> <td>279.90</td> <td>381.70</td> <td>376.30</td> <td>305.10</td> <td>333.30</td> <td>199.20</td> <td>311.20</td> <td>456.50</td> </tr> <tr> <td><strong>31</strong></td> <td>501.40</td> <td>341.50</td> <td>365.80</td> <td>549.80</td> <td>442.70</td> <td>442.00</td> <td>697.00</td> <td>499.20</td> <td>449.20</td> </tr> <tr> <td><strong>32</strong></td> <td>1039.00</td> <td>1198.00</td> <td>1256.00</td> <td>993.80</td> <td>1129.00</td> <td>1166.00</td> <td>916.50</td> <td>858.70</td> <td>881.30</td> </tr> </tbody> </table>
Tweets from the European Patent Office account (@epoorg): April 2009-July 2022
<p>19566 tweets released by th the European Patent Office account (@epoorg): April 2009-July 2022.</p> <p>Fields: id<gx:category>, author_id<gx:category>, author_name<gx:category>, author_handler<gx:category>, author_avatar<gx:url> ,user_created_at<gx:date>, user_description<gx:text>, user_favourites_count<gx:number>, user_followers_count<gx:number>, user_following_count<gx:number>, user_listed_count<gx:number>, user_tweets_count<gx:number>, user_verified<gx:boolean>, user_location<gx:text>, lang<gx:category>, type<gx:category>,text<gx:text>, date<gx:date>, mention_ids<gx:list[category]>, mention_names<gx:list[category]>, retweets<gx:number>, favorites<gx:number>, replies<gx:number>, quotes<gx:number>, links<gx:list[url]>, links_first<gx:url>, image_links<gx:list[url]>, image_links_first<gx:url>, rp_user_id<gx:category>, rp_user_name<gx:category>, location<gx:text>, tweet_link<gx:url>, source<gx:text>, search<gx:category></p>
Office Device Consumption
<p>List of office device (PC, monitor etc) consumption data for a month period (summer)</p>
Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations
<p>This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model<br>trained via DOmain RAndomization". The goal is to provide training data for occupancy detection.<br><br>The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.<br><br>When using the dataset, please cite:</p> <blockquote> <p><em>Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.</em></p> </blockquote> <h2>Dataset Structure</h2> <p>The following files are provided:<br><br> 1. dataset_office_rooms.h5 (provided as zip file)<br> 2. dataset_meeting_rooms.h5 (provided as zip file)<br> 3. simulated_occupancy_office_rooms.csv<br> 4. simulated_occupancy_meeting_rooms.csv</p> <p>Please use an archiving tool such as 7zip to unzip the hdf5 files.<br>Both hdf5 files contain two datasets with the following keys:<br><br> 1. "<em>data</em>": contains the simulated indoor climate and occupancy data<br> 2. "metadata": contains the metadata that were used for each simulation</p> <p>The csv files contain the time series of occupancy that were used for the simulations.<br><br></p> <h2>Data</h2> <p><em>Data</em> includes the following fields:</p> <p><em>Datetime:</em> day of the year (may be relevant due to seasonal differences) and time of the day<br><em>Zone Air CO2 Concentration:</em> CO2 level in ppm<br><em>Zone Mean Air Temperature:</em> temperature in °C<br><em>Zone Air Relative Humidity: </em>relative humidity in %<br><em>Occupancy: </em>level of occupancy relative to the maximum capacity of the room (in the range [0-1])<br><em>Ventilation:</em> fraction of window opening in the range [0.01, 1]<br><em>SimID:</em> foreign key to reference the room properties the simulation was based on<br><em>BinaryOccupancy:</em> 0 or 1 denoting absence or presence (for binary classification)</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th><em>Datetime</em></th> <th><em>Zone Air CO2 Concentration</em></th> <th><em>Zone Mean Air Temperature</em></th> <th><em>Zone Air Relative Humidity</em></th> <th><em>Occupancy</em></th> <th><em>Ventilation</em></th> <th><em>simID</em></th> <th><em>BinaryOccupancy</em></th> </tr> <tr> <td> <p>10/09 11:21:00</p> </td> <td> <p>1084.5624647371608</p> </td> <td> <p>24.545635909907148</p> </td> <td> <p>41.18393114737054</p> </td> <td> <p>0.7</p> </td> <td> <p>0.0</p> </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> <p><em>Metadata</em> includes the following fields. <br>Underscores denote that the field was not selected during randomization but calculated from the other values.</p> <p>width: room width in m<br>length: room length in m<br>height: hoom height in m<br>infiltration: infiltration per exterior area in m³/m²s<br>outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])<br>orientation: angle between the room's facade orientation and the north direction in degrees<br>maxOccupants: room occupation limit, i.e. the maximum number of occupants<br>_floorArea: floor area in m² (calculated from room dimensions)<br>_volume: room volume in m³ (calculated from room dimensions)<br>_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)<br>_winToFloorRatio: ratio between total window area and floor area (calculated from room model)<br>firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value<br>simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table> <p> </p> <h2>Occupancy Data</h2> <p>The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.</p> <p><em>Datetime, </em><em>Date, </em><em>Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)</em><br><em>Occupants: number of present occupants</em><br><em>Occupancy: binary occupancy state (0=unoccupied, 1=occupied)</em><br><em>WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)</em><br><em>maxOccupants: maximum number of occupants considered for the simulated sequence</em><br><em>WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]<br><br></em></p> <p>Example row:</p> <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table> <p> </p> <p> </p>
Рис. 8. A – Директор Зоологического института академик Е.Н. Павловский и Зам. директора института О.А. Скарлато в кабинете директора ЗИНа. 1963 г. Архив С.О. Скарлато; B – Директор Зоологического института член-корр. АН СССР О.А. Скарлато в кабинете директора ЗИНа. НоЯбрь 1990 г. Архив С.О. Скарлато. Fig. 8. A – Director of the Zoological Institute, Academician E.N. Pavlovsky and Deputy Director of the Institute O.A. Scarlato in the Director's Office. 1963. Archive of S.O. Scarlato; B – Director of the Zoological Institute, Corresponding Member O.A. Scarlato in the Director's Office. November 1990. Archive of S.O. Scarlato. in Orest A. Scarlato - scientist and organizer of science: on the 100th anniversary of his birth (1920-1994)
Рис. 8. A – Директор Зоологического института академик Е.Н. Павловский и Зам. директора института О.А. Скарлато в кабинете директора ЗИНа. 1963 г. Архив С.О. Скарлато; B – Директор Зоологического института член-корр. АН СССР О.А. Скарлато в кабинете директора ЗИНа. НоЯбрь 1990 г. Архив С.О. Скарлато. Fig. 8. A – Director of the Zoological Institute, Academician E.N. Pavlovsky and Deputy Director of the Institute O.A. Scarlato in the Director's Office. 1963. Archive of S.O. Scarlato; B – Director of the Zoological Institute, Corresponding Member O.A. Scarlato in the Director's Office. November 1990. Archive of S.O. Scarlato.
Dataset: Office Properties Income Trust (OPI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Office Properties Income Trust (OPINL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset of uGIM deployed in an office building
<p>A dataset of sensor and energy data collected from uGIM system in an office building located in Portugal.</p> <p>The dataset has a total of three days (24 hour records for each 10 seconds):</p> <p> - summer day</p> <p> - winter day</p> <p> - cloudy day</p> <p> </p> <p>uGIM related publications:<br> - Gomes, L., Vale, Z., & Corchado, J. M. (2020). Microgrid management system based on a multi-agent approach: An office building pilot. Measurement: Journal of the International Measurement Confederation, 154. <a href="http://doi.org/10.1016/j.measurement.2019.107427">https://doi.org/10.1016/j.measurement.2019.107427</a><br> - Gomes, L., Vale, Z. A., & Corchado, J. M. (2020). Multi-Agent Microgrid Management System for Single-Board Computers: A Case Study on Peer-to-Peer Energy Trading. IEEE Access, 8, 64169–64183. <a href="http://doi.org/10.1109/ACCESS.2020.2985254">https://doi.org/10.1109/ACCESS.2020.2985254</a><br> - Gomes, L. (2020). μGIM - Microgrid intelligen management system based on a multi-agent approach and the active participation of end-users [Universidad de Salamanca]. <a href="http://doi.org/10.14201/gredos.144238">https://doi.org/10.14201/gredos.144238</a><br> - Gomes, L., Spínola, J., Vale, Z., & Corchado, J. M. (2019). Agent-based architecture for demand side management using real-time resources’ priorities and a deterministic optimization algorithm. Journal of Cleaner Production, 241, 118154. <a href="http://doi.org/10.1016/j.jclepro.2019.118154">https://doi.org/10.1016/j.jclepro.2019.118154</a></p> <p> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Longitudinal dataset of human-building interactions in U.S. offices
<p>One year of longitudinal data (fifteen minute interval) on local thermal conditions, related behaviors, and comfort of twenty-four occupants of a medium-sized office building in Philadelphia, PA.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevincodebook.xlsx">langevincodebook.xlsx</a></strong> defines all the variables and their associated column number in the raw data .txt file.</p> <p><strong><a href="https://zenodo.org/api/files/378e889e-3651-4d7a-8648-6e4170d7db06/langevindata.txt.zip">langevindata.txt.zip</a></strong> includes the full one-year data from the study.</p>
Sleep problems are a strong predictor of stress-related metabolic changes in police officers. A prospective study
<p>Data used for a prospective study on occupational stress, sleep problems and metabolic syndrome in a sample of police officers, Italy, 2009-2014. Paper submitted to PLoS One, waiting for a decision.</p>
Extended 1.0 Dataset of "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary"
<p><strong>Introduction</strong></p> <p>We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file <em>Short_Description_of_Data_Analysis.pdf </em>and <em>Statistical_formulas.pdf </em></p> <p>The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.</p> <p><em>The dataset was expanded on <strong>23rd September 2024</strong> to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.</em></p> <p><strong>Short Description of Data Analysis and Attached Files (datasets):</strong></p> <p>Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.</p> <p>Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: <a href="https://www.ksh.hu/stadat_files/nep/hu/nep0035.html">https://www.ksh.hu/stadat_files/nep/hu/nep0035.html</a>, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the <em>Data_of_demography.xlsx</em> file.</p> <p>In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.</p> <p>The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): <a href="https://www.nnk.gov.hu/index.php/efi">https://www.nnk.gov.hu/index.php/efi</a> (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: <a href="https://semmelweis.hu/egeszsegfejlesztes/en/">https://semmelweis.hu/egeszsegfejlesztes/en/</a> (n=1). (accessed 05 Dec. 2023.)</p> <p>Geocoordinates were determined using Google Maps (N=108): <a href="https://www.google.com/maps">https://www.google.com/maps</a>. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: <em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file.</p> <p>The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:</p> <p><a href="https://qgis.org/en/site/forusers/download.html">https://qgis.org/en/site/forusers/download.html</a>. (accessed 04 Jan. 2024.)</p> <p>The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the</p> <p><em>Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv</em> file,</p> <p>imported via .csv file.</p> <p>The OpenStreetMap tileset is directly accessible from <a href="http://www.openstreetmap.org">www.openstreetmap.org</a> in QGIS. (accessed 04 Jan. 2024.)</p> <p>The Hungarian county administrative boundaries were downloaded from the following website: <a href="https://data2.openstreetmap.hu/hatarok/index.php?admin=6" target="_new">https://data2.openstreetmap.hu/hatarok/index.php?admin=6</a> (accessed 04 Jan. 2024.)</p> <p>HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.</p> <p>Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).</p> <p>A brief description of the statistical formulas applied is included in the <em>Statistical_formulas.pdf.</em></p> <p>Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.</p> <ul> <li>Aggregated number of HDOs by county: <em>Number_of_HDOs.xlsx</em></li> <li>Standardised data (Number of HDOs per 100,000 residents): <em>Standardized_data.xlsx</em></li> <li>Calculation of the Lorenz curve: <em>Lorenz_curve.xlsx</em></li> <li>Calculation of the Gini index: <em>Gini_Index.xlsx</em></li> <li>Calculation of the LQ index: <em>LQ_Index.xlsx</em></li> <li>Calculation of the Herfindahl-Hirschman Index: <em>Herfindahl_Hirschman_Index.xlsx</em></li> <li>Calculation of the Entropy index: <em>Entropy_Index.xlsx</em></li> <li>Regression and correlation analysis calculation: <em>Regression_correlation.xlsx</em></li> </ul> <p>Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:</p> <ul> <li>Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.</li> <li>Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv</li> <li>Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv</li> <li>Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv</li> <li>Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv</li> <li>Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv</li> <li>Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv</li> </ul> <p>For easier readability, the files have been provided in both SPV and PDF formats.</p> <p>The translation of these supplementary files into English was completed on 23rd Sept. 2024.</p> <p> </p> <p><em>If you have any further questions regarding the dataset, please contact the corresponding author: <a target="_new">domjan.peter@phd.semmelweis.hu</a></em></p> <p> </p>
Chilean observatories at home office, by Robert Barsa, Slovakia
<p>Third place in the 2021 IAU OAE Astrophotography Contest, category Wide star fields.</p> <p>This rich panoramic image shows a range of constellations, deep sky objects and planets. The brightest point of light with a pale reddish hue (left of center) is the planet Mars. In the bottom far left of the image, located just above the sloped roof of the building are two points of light appearing very close to each other, these are the planets Jupiter (brighter) and Saturn. To the far right of the image the two fuzzy cloud-like objects are the Large and Small Magellanic Clouds. These are satellite dwarf galaxies of the Milky Way located at a distance of approximately 160,000 and 200,000 light years, respectively. The Andromeda Galaxy, which is located at an approximate distance of 2.5 million light years, appears in this image as a small, angled smudge between Mars and the brightest glow on the horizon (closer to the horizon). The bright glow is not from the Sun or the Moon, but the city lights of San Pedro de Atacama.</p> <p>Prominent constellations include: Orion – identified by the three stars forming Orion’s Belt; Taurus – a small triangle shaped collection of stars to the left of Orion and identified by the pale orange star (Aldebaran) at the vertex of the triangle; Canis Major – to the bottom right of Orion, identified by the bright star Sirius (the brightest star in the night sky) and three stars in the shape of a triangle just above the observatory dome in the foreground; Pegasus – identified by the trapezium shape diagonally below Mars. Other constellations include Andromeda, Perseus and Aries. Diverse cultures around the world have their own names and rich stories associated with these constellations and many others.</p> <p>To the top right of Orion’s Belt is an angled line of stars (Orion’s Sword) within which is a pinkish smudge (looks like a fuzzy star when observed with the unaided eye) that is the Orion Nebula – a stellar nursery located 1500 light years from Earth – where stars are formed. To the left of Taurus is a compact fuzzy smudge, and that is the Pleiades star cluster. The variation in the colour of stars is the result of temperature of the stars, for example, the red orange star to the bottom left of Orion’s Belt is the red giant star Betelgeuse. Cooler stars appear redder, compared to the higher temperature white and bluish stars. The colours in the image are enhanced because of the higher sensitivity of the digital camera compared to the human eye.</p> <p>Credit: Robert Barsa/IAU OAE</p>
Volker Mahnert in my office, bringing new pseudoscorpions for the MHNG collection (photo P. Schwendinger). in Volker Mahnert 3 December 1943 – 23 November 2018
Volker Mahnert in my office, bringing new pseudoscorpions for the MHNG collection (photo P. Schwendinger).
Role of the RIO (Research Integrity Officer) at the University of Florida
<p><strong>Role of the RIO</strong><br> Learn more about the importance of research integrity at UF and the role of the Research Integrity Officer (RIO) in helping you navigate potential instances of research misconduct. Contact <a href="mailto:RIO@research.ufl.edu">RIO@research.ufl.edu</a> for assistance.</p>
Energy consumption data of office building and energy production data of a 185KW PV plant
<p>The dataset includes two-year monitoring data from the energy consumption of and office building located in center of Italy. The building has HVAC system, heat pumps for space heating /cooling (overall 120-140 KW load) and lighting subsystems controlled individually and/or overall by BMS.</p> <p>The building is a part of a small smart-grid which includes a PV plant (180KW). Energy production data are monitored and a two-year dataset is provided as well. </p>
IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data
<p>© Crown Copyright, Met Office</p> <p>This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>.</p> <p>Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.</p>
Time Series Measurement Data of Office Building in Jakarta Indonesia - Incoming Transformer of 20 kV | 0.4 kV
<p>Time Series Measurement Data of Office Buildings in Jakarta Indonesia - Incoming Three Transformers of 2 MVA rating (20 kV | 0.4 kV)<br> The data was taken by Standardized Power Quality Analyzer within minutes span data during eight days in 2016. <br> The data consist of Voltage, Current, Active/Reactive/Apparent Power, Power Factor, Total Harmonic Distortion (THD) within three-phase measurement. Related data was also included from with it i.e., calculated data for active power losses, Unbalanced Voltage per phase, efficiency etc</p>
Figure 1 in Massive infestation of Tyrophagus putrescentiae (Astigmata: Acaridae) inside an office in City of Panama, Panama
Figure 1. Infestation of Tyrophagus putrescentiae in furniture (A) bottle with alcohol 70% (B), cup of coffee (C), and inside pantry furniture (D) in administrative office in City of Panama, Panama.
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.