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Figure 1 in Does logging affect soil biodiversity and its functions? A review
Figure 1. General overview of this synthesis review. Silvicultural practices, which can affect soil biodiversity and ecosystem functioning driven by soil organisms, can be categorized into two main aspects: (a) alterations in tree strata and understory vegetation, as silvicultural practices often lead to the simplification of tree strata and bring about changes in the composition of understory vegetation. It is important to note that logging equipment also involves the utilization of temporary roads, trails, and log collection points as integral components of this practice, and (b) technology and infrastructure: the incorporation of technology and the development of infrastructure play a crucial role in shaping the effects of silvicultural practices on soil organisms and the overall functionality of ecosystems.
Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset
<div> <h2>Description</h2> <p>Old-growth forests in Southeast Asia are dominated by trees of the Dipterocarpaceae family which are targeted by selective logging. Their traits (supra-annual mast fruiting, limited dispersal, and recalcitrant seeds that form no seed bank) mean they can have poor natural regeneration rates in some selectively logged forests. Enrichment planting is commonly used to overcome recruitment limitation and increase restoration success. However, it is still unclear what factors influence the success rate of planted seedlings, including the characteristics of the surrounding tree matrix and local neighbourhood. <br>Here, we examine the growth and survival of 721 enrichment line-planted seedlings within 24 plots of the selectively logged forest of the Sabah Biodiversity Experiment, in Malaysian Borneo, in relation to their species identity and local neighbourhoods. We mapped the spatial location, size, and identity of nearly 5,000 surrounding matrix trees (≥ 10 cm diameter at breast height ) within a 10 m radius of focal planted seedlings in 2012 and 2015. We analysed levels of tree density-dependence (in terms of canopy openness and total basal area of surrounding matrix trees), asymmetric competition with naturally occurring trees (the proportion of total basal area within a 10 m radius belonging to the largest tree), and confamilial density-dependence (the proportion of total basal area within a 10 m radius belonging to dipterocarps) for each seedling. The following research questions were addressed: how do changes in (a) light availability, (b) local competition, (c) asymmetric competition, and (d) confamilial density-dependence affect the (1) growth and (2) survival of enrichment planted dipterocarp seedlings? To answer these questions, we mapped the tree size, identity (dipterocarp or non-dipterocarp), and spatial location of surrounding matrix trees with DBH ≥ 10 cm within a 10 m radius of each planted seedling. Column descriptions:<br>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end.<br>IDPlots: The plot number the tree in question is located in.<br>Line_number: The specific line within a plot that the tree in question is located in.<br>sp.plot: Column specifying both the species and the plot number of the tree in question, separated by a period.<br>sp.cohort: Column specifying both the species and the cohort of the tree in question, separated by a comma.<br>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen).<br>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0).<br>Y_m: Relative y coordinate.<br>Z_m: Relative z coordinate.<br>genus: The genus-level identity of the seedling.<br>species: The species-level identity of the seedling<br>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead).<br>planting.date: The date of planting of the seedling.<br>survey_2002: the date of the survey for the 2002 census<br>survey_2011: the date of the survey for the 2011 census.<br>survey_2012: the date of the survey for the 2012 census.<br>survey_2015: the date of the survey for the 2015 census.<br>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died.<br>age_2002: the age (in days) of seedlings at the time of their survey in 2002<br>age_2011: the age (in days) of seedlings at the time of their survey in 2011<br>age_2012: the age (in days) of seedlings at the time of their survey in 2012<br>age_2015: the age (in days) of seedlings at the time of their survey in 2015<br>diam_2002: Basal diameter of seedling in 2002, measured in mm.<br>diam_2011: Basal diameter of seedling in 2011, measured in mm.<br>diam_2012: Basal diameter of seedling in 2012, measured in mm.<br>diam_2015: Basal diameter of seedling in 2015, measured in mm.<br>DBH_2002: DBH of seedling in 2002, measured in mm.<br>DBH_2011: DBH of seedling in 2011, measured in mm.<br>DBH_2012: DBH of seedling in 2012, measured in mm.<br>DBH_2015: DBH of seedling in 2015, measured in mm.<br>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002).<br>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011).<br>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012).<br>openness: The average canopy openness score across 4 cardinal directions, taken during the second census.<br>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling <br>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling <br>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling <br>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling <br>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling <br>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling <br>dbh_max: The basal area of the largest tree within 10 m of the focal seedling <br>no_trees_5: The number of trees within 5 m of the focal seedling<br>no_trees_10: The number of trees between 5 and 10 m of the focal seedling<br>no_trees: The number of trees between 5 and 10 m of the focal seedling<br>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding dipterocarps<br>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>openness_log: the openness column now expressed on a natural log scale.<br>ba_total_log: The ba_total column expressed on a natural log scale.<br>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE).<br>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps<br>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000.<br>prop_ba_max: the proportion of the basal area that belongs to the largest single tree</p> <h2>Contact Person</h2> <p>Ryan Veryard (ryan@veryard.co.uk)</p> <h2>Funding</h2> <p>These data were collected as part of research funded by:</p> <ul> <li>National Environmental Research Council (Standard grant , NE/S007474/1 , <a href="https://www.environmental-research.ox.ac.uk/">https://www.environmental-research.ox.ac.uk/</a> )</li> <li>Agencia Estatal de Investigación de España (Standard grant , RYC2021-032049-I , <a href="https://www.aei.gob.es/">https://www.aei.gob.es/</a> )</li> <li>Ministry of Education, Youth and Sports of the Czech Republic (Standard grant , INTER-TRANSFER LTT17017 , <a href="https://msmt.gov.cz/">https://msmt.gov.cz/</a> )</li> </ul> <p> </p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h2>Files</h2> <p>This dataset consists of 1 file: fieldmap_dataset.xlsx</p> <h3>fieldmap_dataset.xlsx</h3> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset</h3> <ul> <li>Worksheet: Fieldmap_data</li> <li>Description: Dataset for the published paper "Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest"</li> <li>Number of fields: 74</li> <li>Number of data rows: 721</li> <ul> <li>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end. (type: replicate)</li> <li>IDPlots: The plot number the tree in question is located in. (type: location)</li> <li>Line_number: The specific line within a plot that the tree in question is located in. (type: numeric)</li> <li>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen). (type: ordered categorical)</li> <li>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0). (type: numeric)</li> <li>Y_m: Relative y coordinate. (type: numeric)</li> <li>Z_m: Relative z coordinate. (type: numeric)</li> <li>genus: The genus-level identity of the seedling. (type: categorical)</li> <li>species: The species-level identity of the seedling (type: taxa)</li> <li>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead). (type: numeric trait)</li> <li>planting.date: The date of planting of the seedling. (type: date)</li> <li>survey_2002: the date of the survey for the 2002 census (type: date)</li> <li>survey_2011: the date of the survey for the 2011 census. (type: date)</li> <li>survey_2012: the date of the survey for the 2012 census. (type: date)</li> <li>survey_2015: the date of the survey for the 2015 census. (type: date)</li> <li>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died. (type: ordered categorical)</li> <li>age_2002: the age (in days) of seedlings at the time of their survey in 2002 (type: numeric trait)</li> <li>age_2011: the age (in days) of seedlings at the time of their survey in 2011 (type: numeric trait)</li> <li>age_2012: the age (in days) of seedlings at the time of their survey in 2012 (type: numeric trait)</li> <li>age_2015: the age (in days) of seedlings at the time of their survey in 2015 (type: numeric trait)</li> <li>diam_2002: Basal diameter of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>diam_2011: Basal diameter of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>diam_2012: Basal diameter of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>diam_2015: Basal diameter of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>DBH_2002: DBH of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>DBH_2011: DBH of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>DBH_2012: DBH of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>DBH_2015: DBH of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002). (type: numeric trait)</li> <li>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011). (type: numeric trait)</li> <li>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012). (type: numeric trait)</li> <li>openness: The average canopy openness score across 4 cardinal directions, taken during the second census. (type: numeric)</li> <li>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling (type: numeric)</li> <li>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling (type: numeric)</li> <li>dbh_max: The basal area of the largest tree within 10 m of the focal seedling (type: numeric)</li> <li>no_trees_5: The number of trees within 5 m of the focal seedling (type: numeric)</li> <li>no_trees_10: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>no_trees: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>openness_log: the openness column now expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log: The ba_total column expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE). (type: numeric)</li> <li>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps (type: numeric)</li> <li>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000. (type: numeric)</li> <li>prop_ba_max: the proportion of the basal area that belongs to the largest single tree (type: numeric)</li> </ul> </ul> <h2>Extents</h2> <ul> <li>Date range: 2002-07-01 to 2015-05-28</li> <li>Latitudinal extent: 5.07° to 5.1°</li> <li>Longitudinal extent: 117.64° to 117.67°</li> </ul> <h2>Taxonomic coverage</h2> <p>This dataset contains data associated with taxa and these have been validated against appropriate taxonomic authority databases.</p> <h3>GBIF taxa details</h3> <p>The following taxa were validated against the GBIF backbone dataset (version 2023-08-28). If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <div> - Plantae<br> -  - Tracheophyta<br> -  -  - Magnoliopsida<br> -  -  -  - Malvales<br> -  -  -  -  - Dipterocarpaceae<br> -  -  -  -  -  - <em>Dipterocarpus</em><br> -  -  -  -  -  -  - <em>Dipterocarpus conformis</em><br> -  -  -  -  -  - <em>Dryobalanops</em><br> -  -  -  -  -  -  - <em>Dryobalanops lanceolata</em><br> -  -  -  -  -  - <em>Hopea</em><br> -  -  -  -  -  -  - <em>Hopea ferruginea</em><br> -  -  -  -  -  -  - <em>Hopea sangal</em><br> -  -  -  -  -  - <em>Parashorea</em><br> -  -  -  -  -  -  - <em>Parashorea malaanonan</em><br> -  -  -  -  -  -  - <em>Parashorea warburgii</em> (as synonym: <em>Parashorea tomentella</em>)<br> -  -  -  -  -  - <em>Shorea</em><br> -  -  -  -  -  -  - <em>Shorea argentifolia</em><br> -  -  -  -  -  -  - <em>Shorea beccariana</em><br> -  -  -  -  -  -  - <em>Shorea faguetiana</em><br> -  -  -  -  -  -  - <em>Shorea gibbosa</em><br> -  -  -  -  -  -  - <em>Shorea johorensis</em><br> -  -  -  -  -  -  - <em>Shorea leprosula</em><br> -  -  -  -  -  -  - <em>Shorea macrophylla</em><br> -  -  -  -  -  -  - <em>Shorea macroptera</em><br> -  -  -  -  -  -  - <em>Shorea ovalis</em><br> -  -  -  -  -  -  - <em>Shorea parvifolia</em></div> </div>
Object-Centric Event Log for Age of Empires Game Interactions
<p>The dataset contains object-centric event logs in the OCEL 2.0 ( <a href="https://www.ocel-standard.org/" target="_blank" rel="noopener">https://www.ocel-standard.org/ </a>) format.</p> <p>The event logs originate from 100,000 Age of Empires 2 matches. In this real-time strategy game, players control units (like villagers or archers) and build structures (like houses or lumber camps) to create an efficient economy and win against the other players. Players can control multiple units at once, and they can utilize game mechanics to automate parts of the process for them, so they must not trigger every event themselves. The beginning of the game focuses on building economic structures that are as efficient as possible. Normative process descriptions, so-called build orders, describe battle-tested interaction patterns for the beginning of the game, similar to chess openings.</p> <p>The large object-centric event log contains 1000 matches and has the following properties:</p> <table> <tbody> <tr> <td><strong>Property</strong></td> <td><strong>Value</strong></td> </tr> <tr> <td>Objects</td> <td>361,935</td> </tr> <tr> <td>Object Types</td> <td>30</td> </tr> <tr> <td>Events</td> <td>2,372,505</td> </tr> <tr> <td>Event Types</td> <td>829</td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important object types:<br><br></p> <table> <tbody> <tr> <td><strong>Object Type or (Group of Object Types)</strong></td> <td><strong><span>Explanation</span> </strong></td> </tr> <tr> <td>Match</td> <td>The match represents the competition of two players.</td> </tr> <tr> <td>Player</td> <td>There is one object per player. They are connected to all events that involve player input.</td> </tr> <tr> <td>Session</td> <td>There is one session per player in a match. The session is connected to all events happening on the machine of a player. The events can involve the player directly or they can also be game logic-based events triggered by the game engine.</td> </tr> <tr> <td>Villager</td> <td>Worker units to gather resources and build infrastructure.</td> </tr> <tr> <td>Town Center</td> <td>Central buildings for villager production and resource drop-off. Capable of setting automated gather points to assign tasks for newly created villagers.</td> </tr> <tr> <td>(Resource Drop-Off Group)</td> <td>Includes Lumber Camps, Mining Camps, and Mills. These facilities not only serve as drop-off points but can automatically command workers to gather the corresponding resources upon build completion.</td> </tr> <tr> <td>Farms</td> <td>Agricultural units for a continuous food supply. Farms can sometimes be replenished automatically, depending on game settings or upgrades.</td> </tr> <tr> <td>(Military Buildings)</td> <td>Structures for training military units and producing siege weaponry. Capable of setting gather points to automate unit deployment.</td> </tr> <tr> <td>(Research Buildings)</td> <td> <p>Facilities dedicated to technological advancements and upgrades.</p> </td> </tr> <tr> <td>(Military Units)</td> <td> <p>Units used for combat operations.</p> </td> </tr> </tbody> </table> <p> </p> <p>The following table describes the most important activities:</p> <p> </p> <table> <tbody> <tr> <td><strong>Event Type</strong></td> <td><strong><span>Explanation</span></strong></td> </tr> <tr> <td>Command Build [Structure]</td> <td>Issued by players to direct units to construct buildings.</td> </tr> <tr> <td>Start Build [Structure]</td> <td>Marks the beginning of the construction of a building by a designated group of villagers.</td> </tr> <tr> <td>Complete Build [Structure]</td> <td>Signals the completion of a building's construction, making the building operational and freeing up capacity of the constructing units.</td> </tr> <tr> <td>Gather [Resource]</td> <td>Represents the command to a unit to collect resources such as wood, stone, food, or gold.</td> </tr> <tr> <td>Command Research [Technology]</td> <td>Issued by players to initiate a research task in a research building.</td> </tr> <tr> <td>Start Research [Technology]</td> <td>Marks the beginning of the research process once resources arrived.</td> </tr> <tr> <td>Complete Research [Technology]</td> <td>Denotes the completion of a research task, unlocking new technologies or enhancements, and freeing up production capacity.</td> </tr> <tr> <td>Command Queue [Unit]</td> <td>Issued by players to add units to the production queue of a building.</td> </tr> <tr> <td>Start Production [Unit]</td> <td>Marks the beginning of unit production within a facility, as soon as there is capacity.</td> </tr> <tr> <td>Complete Queue [Unit]</td> <td>Signals the end of unit production, resulting in the deployment of a new unit and freeing up production capacity.</td> </tr> </tbody> </table> <p> </p> <p>The zip file contains filtered object-centric event logs that only contain 10 matches to explore the data set with faster loading time.</p>
Synthetic XES Event Log of Malignant Melanoma Treatment
<p>The synthetic event log described in this document consists of 25,000 traces, generated using the process model outlined in Geyer et al. (2024) [1] and the DALG tool [2]. This event log simulates the treatment process of malignant melanoma patients, adhering to clinical guidelines. Each trace in the log represents a unique patient journey through various stages of melanoma treatment, providing detailed insights into decision points, treatments, and outcomes.</p> <p>The DALG tool [2] was employed to generate this data-aware event log, ensuring realistic data distribution and variability. </p> <p> </p> <p>DALG: <a href="https://github.com/DavidJilg/DALG">https://github.com/DavidJilg/DALG</a></p> <p> </p> <p>[1] Geyer, T., Grüger, J., & Kuhn, M. (2024). Clinical Guideline-based Model for the Treatment of Malignant Melanoma (Data Petri Net) (1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.10785431">https://doi.org/10.5281/zenodo.10785431</a></p> <p>[2] Jilg, D., Grüger, J., Geyer, T., Bergmann, R.: DALG: the data aware event log generator. In: BPM 2023 - Demos & Resources. CEUR Workshop Proceedings, vol. 3469, pp. 142–146. CEUR-WS.org (2023)</p>
XRP Ledger Consensus Protocol Debug-level Log Traces
<p>A dataset of log traces from the consensus protocol of a <a href="https://github.com/ripple/rippled">rippled server</a> instance. The traces are filtered at the debug (DBG) level. Each file contains a separate trace, representing a full round of the consensus protocol.</p>
Webis Netspeak Instant Query Log 2021 (Webis-NIL-21)
<p>The Webis Netspeak Instant Search Log 2021 (Webis-NIL-21) is an excerpt of the log of the Netspeak search engine. The dataset contains about 37,000 log entries, which correspond to keystroke interactions the users of Netspeak made with it's search interface while entering their queries. This enables the study of instant search logs in general, and that of identifying keystroke interactions belonging to the same query in particular. The latter is annotated in the log.</p>
Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas
<p>Dataset of sampled and/or logged Chlorophyll, Total Suspended Matter, Turbidity and Water temperature in Dutch Case study areas at multiple stations in 2018, 2019 and 2020.</p>
Text-fig. 4. Sedimentological log of the Main Cenoceras Bed and associated strata in the Quantocks Beds (Lyra Subzone) at Helwell Bay, Doniford (measured at NGR ST 0802 4314 and ST 0336 4305). BGS bed no. refers to bed numbers employed in Whittaker and Green (1983). in 'Cenoceras Islands' In The Blue Lias Formation (Lower Jurassic) Of West Somerset, Uk: Nautilid Dominance And Influence On Benthic Faunas
Text-fig. 4. Sedimentological log of the Main Cenoceras Bed and associated strata in the Quantocks Beds (Lyra Subzone) at Helwell Bay, Doniford (measured at NGR ST 0802 4314 and ST 0336 4305). BGS bed no. refers to bed numbers employed in Whittaker and Green (1983).
HYPERCOG_Process data_Welding log_2020-07-30
<p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three di_erent sectors: steel, cement and chemical.</p> <p>Participating entities: LORTEK. The data was gathered and used in the proof of concept of the architecture introduced in the paper in the way that is described in it.</p> <p><strong>Dataset 1: Welding process data (xlsx files)</strong></p> <p>Real time data of the process of a welding cell on Excel sheets. The data is obtained at a frequency of 100 Hz and variables of voltage, current, temperature, gas flux, etc. are registered in the Excel file by rows.</p> <p><strong>Dataset 2: Temperature and movement of the piece constructed (zip files)</strong></p> <p>Data of temperatures obtained by thermocouple sensors and distortion of the structure measured by a laser sensor. The zip files contain coma separated values of 8 thermocouples welded to the substrate of the piece constructed by the welding cell. The reading of a laser sensor is also recorded along the x coordinate of the movement of the robot for synchronization purposes.</p> <p>The article corresponding to these datasets are available in open access in </p> <pre><a href="https://doi.org/10.5281/zenodo.5533904">https://doi.org/10.5281/zenodo.5533904</a></pre>
Logging data at Site ITA-1
<p>This dataset is logging data at Site ITA-1, which is located on the Kii Peninsula, southwest Japan.</p> <p>Drilling was conducted by Geological Survey of Japan, AIST.</p> <p>Please refer Kiguchi et al. (2014) for details.</p> <p>ITA_res_gr.csv includes depth [m], resistivity [ohm m], and natural gamma ray [API].</p> <p>ITA_vp_vs.csv includes depth [m], Vp [km/s], and Vs [km/s].</p> <p> </p> <p> </p>
Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging
<p>In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species' range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of <em>Trichocentrum</em> <em>undulatum</em>, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (𝜆 and 𝜆<sub>s</sub>) among sites.</p>
Simulated XES event log of a marketing campaign system
<p>We relied on the interview-driven methodology defined in our previous work \cite{benvenuti2022}, which allowed us to specify various simulation scenarios to frame the boundaries of all possible pipeline executions.</p> <p>Then, we generated this simulated event logs in the traditional XES format using the Simio (https://www.simio.com/), obtaining 10,000 execution traces compliant with the simulation scenarios. In the picture it is shown the Directly-Follow Graph (DFG) representing the pipeline structure, discovered by feeding a process discovery tool (https://fluxicon.com/disco/) with the simulated event log.</p> <p>The pipeline is triggered when the system receives a request to model a new marketing campaign or to report on how an already existing one is performing. In both cases, the first two steps of the pipeline are querying the required data and to apply specific transformations on it. Then, if the request was for a report there is the need of merging the queried data, while for the request of a model an algorithm to compute it is launched. Next, if the request was for a model, the result needs to be stored.<br> Finally, the pipeline ends with either the report or the model being generated.</p>
IODP Expedition 376 Piece log
<p>Dataset includes length data for every whole-round piece: bin length, whole-round piece length (measured by curation staff), and both the archive- and working-half piece lengths (optionally measured by scientists).</p>
IODP Expedition 385 Piece log
<p>Dataset includes length data for every whole-round piece: bin length, whole-round piece length (measured by curation staff), and both the archive- and working-half piece lengths (optionally measured by scientists).</p>
IODP Expedition 396 Piece log
<p>Dataset includes length data for every whole-round piece: bin length, whole-round piece length (measured by curation staff), and both the archive- and working-half piece lengths (optionally measured by scientists).</p>
Results of SPARQL log summarisation on DBpedia logs from LSQ 2.0
<p>This dataset contains the results of applying a method called SPARQL log summarisation on the DBpedia logs from the <a href="http://lsq.aksw.org/">Linked SPARQL Queries (LSQ) 2.0 dataset</a>. This release is meant as a companion of a publication describing the method.</p> <p><strong>Description of the files</strong></p> <ul> <li><strong>datasetLabels_all.csv</strong> association between the identifiers used internally for the datasets and the corresponding labels and full names;</li> <li><strong>datasetLabels_forPaper.csv</strong> same as above, with some datasets omitted for brevity;</li> <li><strong>sparql-clustering-1.1.0.zip</strong> release 1.1.0 of the source code, available at <a href="https://github.com/miguel76/sparql-clustering">https://github.com/miguel76/sparql-clustering</a>;</li> <li><strong>templatesAsCSV.zip</strong> list of the templates found for each dataset, along with information and statistics;</li> <li><strong>templatesAsRDF.zip</strong> the templates found for each dataset, represented as RDF and linked to the RDF representation of LSQ queries.</li> </ul>
IODP Expedition 369 Piece log
<p>Dataset includes length data for every whole-round piece: bin length, whole-round piece length (measured by curation staff), and both the archive- and working-half piece lengths (optionally measured by scientists).</p>
South Haven Lighthouse Log Passing Vessels Dataset
<p>This dataset was created from the digitized log “Record of passing vessels at the South Haven light-station” housed within the <a href="https://luna.library.wmich.edu/luna/servlet/detail/WMUwmu~90~90~1246014~154475:Record-of-passing-vessels-at-the-So?sort=title%2Calternative_title%2Ccreator%2Cdate">South Haven Michigan Lighthouse Log collection at Western Michigan University</a>.</p> <p>The log notes the passing vessels at the South Pierhead, South Haven, Michigan, from July 1, 1878 to June 30, 1882. Entries were recorded by the keeper James S. Donahue. The log contains notations for the barges, brigs, sloops, schooners, and steamers entering in and leaving South Haven. In addition to vessels, it includes information on weather conditions.</p>
(Un)Fair Process Mining Event Logs
<p><strong>License: </strong>CC-BY-4.0</p> <p><strong>Event Logs:</strong></p> <p>We introduce a set of 12 distinct event logs, three for each of the four domains: hiring, healthcare, lending, and renting. These event logs have been carefully curated and simulated, each containing 10,000 cases, thereby providing an extensive resource for researchers focusing on fairness in process mining.</p> <p>In each of these domains, the three event logs represent varying degrees of discrimination, offering researchers an opportunity to explore the nuances and complexities that arise in diverse real-world scenarios. By presenting each log with a thorough description of the inherent processes and their respective attributes, we aim to provide a robust groundwork for understanding the potential sources of discrimination and addressing fairness in process mining.</p> <p>We have ensured that all the event logs are provided in the eXtensible Event Stream (XES) standard format. This adherence to a recognized standard not only ensures broad compatibility but also facilitates interoperability across a variety of process mining tools. By choosing this common format, we aim to encourage and simplify the utilization of these logs for researchers across different platforms.</p> <p><strong>* Hiring</strong></p> <p>The data describes a multifaceted recruitment process with diverse application pathways ranging from minimal processing to extensive multi-step procedures. The variability of these routes, largely dependent on numerous determinants, yields a spectrum of outcomes from instant rejection to successful job offers.</p> <p>The logs include attributes such as age, citizenship, German proficiency, gender, religion, and years of education. While these attributes may inform candidate profiles, their misuse could engender discrimination. Variables like age and education may signify experience and skills, citizenship and German language may address job logistics, but these should not unjustly eliminate applicants. Gender and religion, unrelated to job performance, must not sway hiring. Therefore, the use of these attributes must uphold fairness, avoiding any potential bias.</p> <p><strong>* Hospital</strong></p> <p>The data depicts a hospital treatment process that commences with registration at an Emergency Room or Family Department and advances through stages of examination, diagnosis, and treatment. Notably, unsuccessful treatments often entail repetitive diagnostic and treatment cycles, underscoring the iterative nature of healthcare provision.</p> <p>The logs incorporate patient attributes such as age, underlying condition, citizenship, German language proficiency, gender, and private insurance. These attributes, influencing the treatment process, may unveil potential discrimination. Factors like age and condition might affect case complexity and treatment path, while citizenship may highlight healthcare access disparities. German proficiency can impact provider-patient communication, thus affecting care quality. Gender could spotlight potential health disparities, while insurance status might indicate socio-economic influences on care quality or timeliness. Therefore, a comprehensive examination of these attributes vis-a-vis the treatment process could shed light on potential biases or disparities, fostering fairness in healthcare delivery.</p> <p><strong>* Lending</strong></p> <p>This data illustrates the steps within a loan application process. From an initial appointment request, the process navigates various stages, including information verification and underwriting, culminating in loan approval or denial. Additional steps may be required, such as co-signer enlistment or collateral assessment. Some cases experience outright appointment denial, indicating the process's variability, reflecting applicants' differing credit situations.</p> <p>The logs' attributes can aid in identifying influences on outcomes and detecting discrimination. Personal characteristics ('age', 'citizen', 'German speaking', and 'gender') and socio-economic indicators ('YearsOfEducation' and 'CreditScore') can impact the process. While 'yearsOfEducation' and 'CreditScore' can validly inform creditworthiness, 'age', 'citizen', 'language ability', and 'gender' should not bias loan decisions, ensuring these attributes are used responsibly fosters equitable loan processes.</p> <p><strong>* Renting</strong></p> <p>The data represents a rental process. It begins with a prospective tenant applying to view a property. Subsequent steps include an initial screening phase, viewing, decision-making, and a potential extensive screening. The process ends with the acceptance or rejection of the prospective tenant. In some cases, a tenant may apply for viewing but be rejected without the viewing occurring.</p> <p>The logs contain attributes that can shed light on potential biases in the process. 'Age', 'citizen', 'German speaking', 'gender', 'religious affiliation', and 'yearsOfEducation' might influence the rental process, leading to potential discrimination. While some attributes may provide useful insights into a potential tenant's reliability, misuse could result in discrimination. Thus, fairness must be observed in utilizing these attributes to avoid potential biases and ensure equitable treatment.</p>
Example of log sessions of the Edible City Game
<p>The Edible City Game is a serious game for urban planning of edible city solutions (aka urban agriculture) developed under the Edicitnet project. This dataset contains several log sessions result of a workshop in the Edible Cities Network Annual Conference 2023, held in Barcelona.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.