Find research datasets worth reusing

Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"

<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>

Synthetic population for KEN

<p><strong>Synthetic populations for regions of the World (SPW) | Kenya</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Kenya</td> </tr> <tr> <td>Region ID</td> <td>ken</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0_adm1</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>39129968.0</td> </tr> <tr> <td>Average age</td> <td>21.6</td> </tr> <tr> <td>Households</td> <td>10938486.0</td> </tr> <tr> <td>Average household size</td> <td>3.6</td> </tr> <tr> <td>Residence locations</td> <td>10938486.0</td> </tr> <tr> <td>Activity locations</td> <td>838758.0</td> </tr> <tr> <td>Average number of activities</td> <td>5.4</td> </tr> <tr> <td>Average travel distance</td> <td>134.7</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ken_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ken_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ken_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ken_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ken_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ken_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ken_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ken_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ken_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for URY

<p><strong>Synthetic populations for regions of the World (SPW) | Uruguay</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Uruguay</td> </tr> <tr> <td>Region ID</td> <td>ury</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>3208260.0</td> </tr> <tr> <td>Average age</td> <td>36.3</td> </tr> <tr> <td>Households</td> <td>1161045.0</td> </tr> <tr> <td>Average household size</td> <td>2.8</td> </tr> <tr> <td>Residence locations</td> <td>1161045.0</td> </tr> <tr> <td>Activity locations</td> <td>65127.0</td> </tr> <tr> <td>Average number of activities</td> <td>6.1</td> </tr> <tr> <td>Average travel distance</td> <td>18.9</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ury_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ury_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ury_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ury_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ury_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ury_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ury_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ury_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ury_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for JOR

<p><strong>Synthetic populations for regions of the World (SPW) | Jordan</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Jordan</td> </tr> <tr> <td>Region ID</td> <td>jor</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>5723567.0</td> </tr> <tr> <td>Average age</td> <td>23.5</td> </tr> <tr> <td>Households</td> <td>1235755.0</td> </tr> <tr> <td>Average household size</td> <td>4.6</td> </tr> <tr> <td>Residence locations</td> <td>1235755.0</td> </tr> <tr> <td>Activity locations</td> <td>131978.0</td> </tr> <tr> <td>Average number of activities</td> <td>6.4</td> </tr> <tr> <td>Average travel distance</td> <td>44.5</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (jor_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>jor_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>jor_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>jor_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>jor_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>jor_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>jor_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>jor_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>jor_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for ITA

<p><strong>Synthetic populations for regions of the World (SPW) | Italy</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Italy</td> </tr> <tr> <td>Region ID</td> <td>ita</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ita_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ita_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ita_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ita_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ita_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ita_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ita_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ita_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ita_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for GTM

<p><strong>Synthetic populations for regions of the World (SPW) | Guatemala</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Guatemala</td> </tr> <tr> <td>Region ID</td> <td>gtm</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>14137968.0</td> </tr> <tr> <td>Average age</td> <td>22.8</td> </tr> <tr> <td>Households</td> <td>3107786.0</td> </tr> <tr> <td>Average household size</td> <td>4.6</td> </tr> <tr> <td>Residence locations</td> <td>3107786.0</td> </tr> <tr> <td>Activity locations</td> <td>650298.0</td> </tr> <tr> <td>Average number of activities</td> <td>7.2</td> </tr> <tr> <td>Average travel distance</td> <td>53.2</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (gtm_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>gtm_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>gtm_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>gtm_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>gtm_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>gtm_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>gtm_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>gtm_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>gtm_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for FIN

<p><strong>Synthetic populations for regions of the World (SPW) | Finland</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Finland</td> </tr> <tr> <td>Region ID</td> <td>fin</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DYB</td> <td></td> <td><a href="https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm">https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (fin_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>fin_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>fin_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>fin_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>fin_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>fin_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>fin_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>fin_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>fin_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for NOR

<p><strong>Synthetic populations for regions of the World (SPW) | Norway</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Norway</td> </tr> <tr> <td>Region ID</td> <td>nor</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0_adm1</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DYB</td> <td></td> <td><a href="https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm">https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (nor_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>nor_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>nor_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>nor_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>nor_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>nor_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>nor_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>nor_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>nor_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

Synthetic population for ISL

<p><strong>Synthetic populations for regions of the World (SPW) | Iceland</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Iceland</td> </tr> <tr> <td>Region ID</td> <td>isl</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>293477.0</td> </tr> <tr> <td>Average age</td> <td>36.8</td> </tr> <tr> <td>Households</td> <td>115570.0</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>115570.0</td> </tr> <tr> <td>Activity locations</td> <td>48082.0</td> </tr> <tr> <td>Average number of activities</td> <td>5.8</td> </tr> <tr> <td>Average travel distance</td> <td>53.2</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (isl_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>isl_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>isl_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>isl_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>isl_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>isl_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>isl_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>isl_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>isl_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>

opencc-by-4.0Apr 2021View details →

Fine-scale population spatialization data of China in 2018 based on real location-based big data

<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>

opencc-by-4.0May 2022View details →

"Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" - Genomes and annotation

<p>File-dump associated with the manuscript:</p> <p>"<strong>Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" (Not yet published)</strong></p> <p>MGVs refer to the viral genome database in the publication: https://www.nature.com/articles/s41564-021-00928-6 </p> <p> </p> <p>Following uploaded:</p> <p>File 1: VOG Markers in vOTUs/vMAGs and MGV genomes</p> <p>File 2: Viral Tree Newick file with vOTUs/vMAGs and MGV genomes</p> <p>File 3: All vOTUs/vMAGs genomes</p> <p>File 4: Master table annotation of vOTUs/vMAGs</p> <p>File 5: Centenarian bacterial isolate proviruses</p>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - CIFAR10

<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "cifar_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Raw Datasets

<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - SVHN

<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from SVHN. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "svhn_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - Fashion-MNIST

<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from Fashion-MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "fmnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - MNIST

<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "mnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models.</p> <p>For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p>

opencc-by-4.0Jul 2022View details →

Data for: Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed

<p>These are the data used in the analyses described in the paper titled "Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed", accepted at Estuaries and Coasts. We acknowledge the tangata whenua for the rohe in which these data were collected, Ngāi Tārewa and Ngāti Īrakehu. We thank the Akaroa Taiāpure for their support of this research.</p> <p>The data included are:</p> <p>Raw count data of taxa for each tow, associated with additional metadata including the date of collection, tow coordinates, and estimated seagrass cover (MonthlyRawSampling_Duvauchelle_2020.csv). This data was put through cleaning steps outlined in the file docs/dataCleaning.Rmd prior to being used in any analyses.</p> <p>The cleaned community composition data (cleanedCommunity.csv), output from <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a> and used in the downstream community and population analyses.</p> <p>The GPS coordinates for the tows (gpsdat.csv). These were extracted from the raw data in the data cleaning process.</p> <p>NZsyngnathids_measurements.csv contains the measurements of the pipefish from images. These data also underwent a cleaning process documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a>.</p> <p>The cleaned pipefish trait data (pipefishTraits.csv), output from docs/dataCleaning.Rmd and used in the downstream population analysis documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/populationAnalyses.Rmd">docs/populationAnalyses.Rmd</a>.<br>  </p>

opencc-by-4.0Dec 2021View details →

Lizards from warm and declining populations are born with extremely short telomeres

<p>These two datasets report the information at the populational ("Population_Biogeography2017-2018.csv") and individual ("Telomere_Zootocavivipara_2015-2017.csv") levels. At populational level, we studied the covariation of multiple biogeographic measures to obtain an integrative index of population extinction risk. At individual level, we examined what factors best explained the variation in lizard telomere length.</p> <p>We also uploaded the R code ("DataAnalysis_Telomerelizards_AndreazDupoue.R") used to analyse these data, in which we detailed all variables.</p>

opencc-by-4.0Jul 2022View details →

High-resolution maps of material stock and population in Germany from 1985 to 2018

<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>… material stock in buildings <ul> <li>… in commercial and industrial buildings</li> <li>… in multi-family residential buildings</li> <li>… in single-family residential buildings</li> <li>… in high-rise buildings</li> <li>… in lightweight buildings</li> </ul> </li> <li>… material stock in road infrastructure</li> <li>… material stock in rail infrastructure</li> <li>… material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p>Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Corresponding publication</strong></p> <p>Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>

Corpus and list of keywords from Improving sustainable crop protection using population genetics concepts

<p>Corpus extracted in April 2021 from the ISI Web of Science portal (https://www.webofscience.com) with the following request: ‘Plant AND Resistan* AND Durab*’. A first corpus of 2522 articles was built considering all publication years for this extraction. This collection was then refined by categories to remove articles outwith the scope of our search (e.g. related to durable resistant materials for constructions). We also kept only articles cited at least once. The final corpus was composed of 1783 articles from 1979 to 2021:</p> <ul> <li>CORPUS_plant_resistance_durability.zip</li> </ul> <p>List of keywords used for the network presented in the article:</p> <ul> <li>keywords_list.csv</li> </ul>