Find research datasets worth reusing

openmit-licenseMar 2023View details →

STARSS23: Sony-TAu Realistic Spatial Soundscapes 2023

DESCRIPTION: The Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset contains multichannel recordings of sound scenes in various rooms and environments, together with temporal and spatial annotations of prominent events belonging to a set of target classes. The dataset is collected in two different countries, in Tampere, Finland by the Audio Researh Group (ARG) of Tampere University (TAU), and in Tokyo, Japan by SONY, using a similar setup and annotation procedure. The dataset is delivered in two 4-channel spatial recording formats, a microphone array one (MIC), and first-order Ambisonics one (FOA). These recordings serve as the development dataset for the <a href="https://dcase.community/challenge2023/task-sound-event-localization-and-detection-evaluated-in-real-spatial-sound-scenes">DCASE 2023 Sound Event Localization and Detection Task</a> of the <a href="https://dcase.community/challenge2023/">DCASE 2023 Challenge</a>. The STARSS23 dataset is a continuation of the <a href="https://zenodo.org/record/6600531">STARSS22 dataset</a>. It extends the previous version with the following: <ul> <li>An additional additional 2hrs 30mins of recordings in the development set, from 5 new rooms distributed in 47 new recording clips.</li> <li>An additional 1hr 40mins of recordings added in the evaluation set of the dataset.</li> <li>360° videos spatially and temporally aligned to the audio recordings of the dataset (apart from 12 audio-only clips).</li> <li>Distance labels (in cm) for the spatially annotated sound events, instead of the previous azimuth and elevation only labels.</li> </ul> Contrary to the three previous datasets of synthetic spatial sound scenes of TAU Spatial Sound Events 2019 (<a href="https://zenodo.org/record/2599196">development</a>/<a href="https://zenodo.org/record/3377088">evaluation</a>), <a href="https://doi.org/10.5281/zenodo.4064792">TAU-NIGENS Spatial Sound Events 2020</a>, and <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Events 2021</a> associated with previous iterations of the DCASE Challenge, the STARS22-23 dataset contains recordings of real sound scenes and hence it avoids some of the pitfalls of synthetic generation of scenes. Some such key properties are: <ul> <li>annotations are based on a combination of human annotators for sound event activity and optical tracking for spatial positions,</li> <li>the annotated target event classes are determined by the composition of the real scenes,</li> <li>the density, polyphony, occurences and co-occurences of events and sound classes is not random, and it follows actions and interactions of participants in the real scenes.</li> </ul> The first round of recordings was collected between September 2021 and January 2022. A second round of recordings was collected between November 2022 and February 2023. Collection of data from the TAU side has received funding from Google. A demo video combining the different modalities and spatial annotations can be found <a href="https://www.youtube.com/watch?v=ZtL-8wBYPow">here</a>. REPORT & REFERENCE: If you use this dataset you could cite this report on its design, capturing, and annotation process: Kazuki Shimada, Archontis Politis, Parthasaarathy Sudarsanam, Daniel Krause, Kengo Uchida, Sharath Adavanne, Aapo Hakala, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Tuomas Virtanen, Yuki Mitsufuji (2023). STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events, found <a href="https://arxiv.org/abs/2306.09126">here</a>, and Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen (2022). STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), Nancy, France. found <a href="https://dcase.community/documents/workshop2022/proceedings/DCASE2022Workshop_Politis_51.pdf">here</a>. AIM: The STARSS22-23 dataset is suitable for training and evaluation of machine-listening models for sound event detection (SED), general sound source localization with diverse sounds or signal-of-interest localization, and joint sound-event-localization-and-detection (SELD). Additionally, the dataset can be used for evaluation of signal processing methods that do not necessarily rely on training, such as acoustic source localization methods and multiple-source acoustic tracking. The dataset allows evaluation of the performance and robustness of the aforementioned applications for diverse types of sounds, and under diverse acoustic conditions. Specifically the STARSS23 allows evaluation of audiovisual processing methods with a spatial dimension, such as audiovisual source localization or audiovisual object recognition. SPECIFICATIONS: General: <ul> <li>Recordings are taken in two different sites.</li> <li>Each recording clip is part of a recording session happening in a unique room.</li> <li>Groups of participants, sound making props, and scene scenarios are unique for each session (with a few exceptions).</li> <li>To achieve good variability and efficiency in the data, in terms of presence, density, movement, and/or spatial distribution of the sounds events, the scenes are loosely scripted.</li> <li>13 target classes are identified in the recordings and strongly annotated by humans.</li> <li>Spatial annotations for those active events are captured by an optical tracking system.</li> <li>Sound events out of the target classes are considered as interference.</li> <li>Occurrences of up to 3 simultaneous events are fairly common, while higher numbers of overlapping events (up to 5) can occur but are rare.</li> </ul> Volume, duration, and data split: <ul> <li>A total of 16 unique rooms captured in the recordings, 4 in Tokyo and 12 in Tampere (development set).</li> <li>70 recording clips of 30 sec ~ 5 min durations, with a total time of ~2hrs, captured in Tokyo (development dataset).</li> <li>98 recording clips of 40 sec ~ 9 min durations, with a total time of ~5.5hrs, captured in Tampere (development dataset).</li> <li>79 recordings clips of 40 sec ~ 7 min durations, with a total time of ~3.5hrs, captured in both sites (evaluation dataset).</li> <li>A training-testing split is provided for reporting results using the development dataset.</li> <li>40 recordings contributed by Sony for the training split, captured in 2 rooms (dev-train-sony).</li> <li>30 recordings contributed by Sony for the testing split, captured in 2 rooms (dev-test-sony).</li> <li>50 recordings contributed by TAU for the training split, captured in 7 rooms (dev-train-tau).</li> <li>48 recordings contributed by TAU for the testing split, captured in 5 rooms (dev-test-tau).</li> </ul> Audio: <ul> <li>Sampling rate: 24kHz.</li> <li>Bit depth:         16 bits.</li> <li>Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array (MIC).</li> </ul> Video: <ul> <li>Video 360° format: equirectangular</li> <li>Video resolution: 1920x960</li> <li>Video frames per second (fps): 29.97</li> <li>All audio recordings are accompanied by synchronised video recordings, apart from 12 audio recordings with missing videos (fold3_room21_mix001.wav - fold3_room21_mix012.wav)</li> </ul> More detailed information on the dataset can be found in the included README file. SOUND CLASSES: 13 target sound event classes are annotated. The classes follow loosely the <a href="https://research.google.com/audioset/ontology/index.html">Audioset ontology</a>.   0. Female speech, woman speaking   1. Male speech, man speaking   2. Clapping   3. Telephone   4. Laughter   5. Domestic sounds   6. Walk, footsteps   7. Door, open or close   8. Music   9. Musical instrument   10. Water tap, faucet   11. Bell   12. Knock The content of some of these classes corresponds to events of a limited range of Audioset-related subclasses. For more information see the README file. EXAMPLE APPLICATION: An implementation of a trainable model performing audio-only joint SELD, trained and evaluated with this dataset is provided <a href="https://github.com/sharathadavanne/seld-dcase2023">here</a>. This implementation will serve as the baseline method in the DCASE 2023 Sound Event Localization and Detection Task, under the audio-only inference track. Additionally, an implementation of a trainable model performing audiovisual SELD, trained and evaluated with this dataset is provided <a href="https://github.com/sony/audio-visual-seld-dcase2023">here</a>. This implementation will serve as the baseline method in the DCASE 2023 Sound Event Localization and Detection Task, under the audiovisual inference track. DEVELOPMENT AND EVALUATION: The current version (Version 1.1) of the dataset includes development audio/video recordings and labels and the evaluation recordings without labels, used by the participants of Task 3 of DCASE2023 Challenge to train and validate their submitted systems (development), and produce system outputs for the challenge evaluation phase. If researchers wish to compare their system against the submissions of DCASE2023 Challenge, they will have directly comparable results if they use the evaluation data as their testing set. DOWNLOAD INSTRUCTIONS: The file foa_dev.zip, correspond to audio data of the FOA recording format. The file mic_dev.zip, correspond to audio data of the MIC recording format. The file video_dev.zip contains the common videos for both audio formats. The file metadata_dev.zip contains the common metadata for both audio formats. The file foa_eval.zip corresponds to audio data of the FOA recording format for the evaluation dataset. The file mic_eval.zip corresponds to audio data of the MIC recording format for the evaluation dataset. The file video_eval.zip contains the common videos for both audio formats of the evaluation dataset. Download the zip files corresponding to the format of interest and use your favourite compression tool to unzip these zip files.

opencc-by-4.0May 2023View details →

LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery

The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation. Raw LSFM imaging data collection was carried out between 2017-2021,  the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023.  Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. et al. LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. Nat Commun 15, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1 For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de

opencc-by-4.0Dec 2022View details →

A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)

This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.

opencc-zeroMay 2023View details →

Standardized reference grids for spatial analyses at various grain sizes

Description: These Reference grids have been created for the <a href="https://naturaconnect.eu/">NaturaConnect project</a> and are based on an intersection of the<a href="https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile"> European Coastline delineation</a> and the <a href="https://gadm.org/">GADM database</a>. Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width. Filestructure: ReferenceGrid_Europe_{format}_{grain} <ul> <li> format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1).</li> <li> grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request.</li> </ul> File format: The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering. Geographic projection: We use the <a href="https://epsg.io/3035">Lamberts-Equal-Area Projection</a> by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the <a href="https://epsg.io/54009">equal-area World Mollweide projection</a> is used. Sourcecode: The code to reproduce the layers has been made available in the "code" file.

opencc-by-4.0Nov 2022View details →

Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics.

This dataset contains a single zipped folder containing: <ul> <li> data </li> <li> scripts </li> </ul> needed to reproduce the manuscript entitled "Spatial transcriptomics of B and T cell receptors uncovers lymphocyte clonal dynamics". Each folder is organized by tissue type, methodology, and analysis. A readme file accompanies each folder with details on the files/scripts within that folder. Alongside the paper and supplementary materials, it should be possible to reproduce all the figures in the manuscript.

Spatial models of topsoil properties in Romania using digital soil mapping techniques

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in: Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning, PLOS ONE, 2023 The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging). The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70). The soil variables are classified as follows: <table> <tbody> <tr> <td> Variable </td> <td> Classes </td> </tr> <tr> <td> 1 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 5 </td> <td> 5 </td> <td> 7 </td> </tr> <tr> <td> pH </td> <td> ≤ 5 (strongly acid) </td> <td> 5.1 – 5.8 (moderately acid) </td> <td> 5.9 – 6.8 (weakly acid) </td> <td> 6.9 – 7.2 (neutral) </td> <td> 7.3 – 8.4 (weakly alkaline) </td> <td> 8.5 – 8.8 (moderately alkaline) </td> <td>   </td> </tr> <tr> <td> EC (mS m-1) </td> <td> ≤ 12.75 </td> <td> 12.76 – 16.49 </td> <td> 16.50 – 20.04 </td> <td> 20.05 – 24.18 </td> <td> 24.19 – 29.11 </td> <td> 29.12 – 35.23 </td> <td> ≤ 35.24 </td> </tr> <tr> <td> OC (g kg-1) </td> <td> < 7.5 (very low) </td> <td> 7.5 – 17.4 (low) </td> <td> 17.4 – 37.8 (moderate) </td> <td> 37.8 – 61.0 (high) </td> <td> > 61 (very high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> CaCO3 (g kg-1) </td> <td> 0 (no carbonates) </td> <td> 1 – 10 (low) </td> <td> 11 – 40 (medium 1) </td> <td> 41 – 80 (medium 2) </td> <td> 81 – 107 (medium 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> P (mg kg-1) </td> <td> < 4 (extremely low) </td> <td> 4 – 8 (very low) </td> <td> 8 – 18 (low) </td> <td> 18 – 36 (medium) </td> <td> 36 – 72 (high) </td> <td> > 72 (very high) </td> <td>   </td> </tr> <tr> <td> N (g kg-1) </td> <td> ≤ 1 (very low) </td> <td> 1.1 – 1.4 (low) </td> <td> 1.5 – 2.0 (medium 1) </td> <td> 2.1 – 2.7 (medium 2) </td> <td> 2.8 – 6.0 (high) </td> <td> > 6 (very high) </td> <td>   </td> </tr> <tr> <td> K (mg kg-1) </td> <td> ≤ 40 * (extremely low) </td> <td> 41 – 65 * (very low) </td> <td> 66 – 130 (low) </td> <td> 131 – 200 (medium) </td> <td> 201 – 300 (high) </td> <td> > 300  (very high) </td> <td>   </td> </tr> <tr> <td> Clay (%) </td> <td> ≤ 25  (low 1) </td> <td> 26 – 32 (low 2) </td> <td> 33 – 40 (medium 1) </td> <td> 41 – 45 (medium 2) </td> <td> ≥ 46  (high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Silt (%) </td> <td> < 25 (medium 1) </td> <td> 25 – 32 (medium 2) </td> <td> 33 – 40 (high 1) </td> <td> 41 – 50 (high 2) </td> <td> > 50 (high 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Sand (%) </td> <td> < 15 (low 1) </td> <td> 15 – 25 (low 2) </td> <td> 26 – 35 (low 3) </td> <td> 36 – 56 (medium) </td> <td> > 56 (high) </td> <td>   </td> <td>   </td> </tr> </tbody> </table>   * classes not present on the Romanian territory

opencc-by-4.0Apr 2023View details →

Data Used for "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910"

This repository contains data used for our article, "Mapping Tribes: Ottoman Spatial Thinking in Iraq and Arabia, c. 1910". The README.md file explains the contents. This data can also be found at https://github.com/opengulf/ottoman-map.

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles: <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> The spatial reference system is EPSG 25830.

Simulated spatially explicit dataset (300 m) on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways

This is a simulated spatially explicit dataset on future forest cover changes in Southeast Asia projected under the baseline shared socioeconomic pathways. It includes six raster maps at a spatial resolution of 300 m: (1) 2015 baseline forest and non-forest map; (2) SSP1 2050 projected net forest gain map; (3) SSP2 2050 projected net forest gain map; (4) SSP3 2050 projected net forest loss map; (5) SSP4 2050 projected net forest gain map; and SSP5 2050 projected net forest loss map. This dataset is the result of a study published in Nature Communications (2019) (https://doi.org/10.1038/s41467-019-09646-4).

opencc-by-4.0Mar 2023View details →

The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism

This dataset contains the following figures: Table 1, The Radar Chart Map 1, ROME, 2nd CENTURY BCE Map 2, ROME, 1st CENTURY BCE Map 3, ROME, 1st CENTURY ACE Map 4, ROME, 2nd CENTURY ACE Map 5, ROME, 3rd CENTURY ACE

Data and code associated with "Spatial wavefront shaping with a nanostructured metasurface for structured illumination microscopy"

Data and code associated with the manuscript "Spatial wavefront shaping with a nanostructured metasurface for structured illumination microscopy"

opencc-by-4.0Aug 2023View details →

Recalibration of the lunar chronology due to spatial cratering-rate variability - Data and code

<ul> <li>CR_moon.csv: Relative cratering rate shown in Fig.2. The data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>cr_lefeuvre2011.txt: Relative cratering rate proposed by Le Feuvre and Wieczorek (2011). The data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>lunar_calib_points.csv: Table summarising the lunar chronology calibration points used in this study. </li> <li>Lagain_AA_convert_age.m: Matlab code converting model ages of Plutarch and Kirkwood craters from Neukum et al. (2001) chronology into the one presented in this study. The code also computes the chronology model from Le Feuvre and Wieczorek (2011) and the one presented in this study for different locations, and compares it with the Neukum et al. (2001) chronology.</li> </ul>

opencc-by-4.0Sep 2023View details →

Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries

Panzeri D.1, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović &Scaron;ifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S2. 1 dpanzeri@ogs.it 2 slibralato@ogs.it Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures. Data (csv) for Panzeri et al. 2023 1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values  (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a>  2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a> 3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a>

opencc-by-4.0Oct 2023View details →

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"

Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization". Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a>

openCC (other)Jan 2022View details →

Spatial and Social Behavior of Acanthurus triostegus on Moorea (French Polynesia) and Palmyra Atoll (USA), 2017-2018

These data describe the schooling behavior of coral reef fish on Moorea (French Polynesia) and Palmyra Atoll (USA) in 2017 and 2018. Data are grouped into two sets of observations: 1) surveys measuring the abundance of reef fish and the proportion of those fish occurring in schools, and 2) behavioral observations including time spent grazing and GPS tracks of schooling and solitary Acanthurus triostegus.

openCC (other)Jul 2017View details →

LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.

This dataset includes data for the lake water quality and geospatial variables that describe climate, hydrology, land use land cover, and lake characteristics that were used to study spatial structure in lake properties at the sub-continental scales (Lapierre et al. Quantifying spatial structure to improve understanding of the relationships between climate, landscape, and lake ecosystem properties, to be submitted to Ecology). All observations came from LAGOS-NELIMNO v. 1.054.1 and LAGOS-NEGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS-NE contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for the LAGOS-NELIMNO v. 1.054.1 dataset and were mostly generated by government agencies (state, federal, tribal) and universities. In this analysis, we compiled lake water quality data from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOS-NELIMNO v. 1.054.1 (2002-2011). We report the median total nitrogen, total phosphorus, secchi depth, and chlorophyll values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics including variables related to lake morphometry, climate, hydrology, atmospheric deposition, land use and land cover.

openCC (other)Sep 2023View details →

Spatial Survey of Water Quality in Lake Arrowhead, TX, USA

A rapid spatial survey of surface water was conducted in in Lake Arrowhead, Texas, USA on 2022 Aug 12. A sensor-equipped boat (Whaler Montawk 210) covered 62.3 km in 180 minutes, prioritizing water overlying the main river channel and navigable sections of reservoir arms. The mean boat speed over the entire survey was 21 km/hr, but in deeper water speeds of 30 to 55 km/hr were common. With this method, a pump brings surface lake water through a transom-mounted intake pipe onto the boat deck in a continuous flow-through system that contains a YSI Exo 2 sonde, flow cell, and probes for turbidity, specific conductivity, pH, and water temperature. A Garmin GPS 18X LVC was used to log latitude, longitude, and boat speed. All sensor and GPS data were logged in the field at 1 s intervals.

openCC0Nov 2023View details →

Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15.

Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15. Spatial beta-diversity may increase landscape productivity if there are positive spatial selection effects. Alternatively, dominant species in mixtures might not be the most productive species in monoculture leading to negative or neutral spatial selection effects. However, these hypotheses remain untested experimentally. Seedling survival can determine species establishment, influencing productivity later. To address this knowledge gap, we experimentally tested whether transplanted seedlings of dominant species optimally sort among habitat types (grassland dominated by Andropogon gerardii, savanna by Quercus macrocarpa, deciduous forest by Acer rubrum, coniferous forest by Pinus strobus, bog by Larix laricina), creating positive effects of landscape diversity on seedling survival and net biodiversity effects at Cedar Creek Ecosystem Science Reserve (CCESR) in Minnesota, USA. The study is named BetaDIV and consists of 100 plots (20 plots per habitat × 5 habitats). Each of the five habitats includes two true replicate monocultures for each of the five species and two true replicates for each of the five possible mixture compositions of four species (leaving each one out in turn to eventually explore the effect of species identity). Each plot is 1.5 by 1.5 m, with 12 seedlings planted 0.5 m apart in a 4 × 4 square grid, except in the plot corners. In the early June 2022, we tagged and planted all seedlings (i.e., bareroot seedlings for trees and plugs for the grass A. gerardii). Two weeks after the initial transplanting, we started tracking seedling survival (presented here) to investigate how seedlings responded to local habitat conditions. We conducted a seedling census for each of the 1200 tagged seedlings (12 seedlings per plot×100 plots), in early September 2022, which was two months at the end

openCC0Jan 2020View details →

Temporal heterogeneity increases with spatial heterogeneity in ecological communities

Heterogeneity is increasingly recognized as a foundational characteristic of ecological systems. Indeed, spatial heterogeneity is commonly used in alternative state theory as an early indicator of regime shifts. To evaluate if spatial heterogeneity of communities is a predictor of temporal heterogeneity, we used mixed effects models to synthesize 68 community datasets spanning freshwater and terrestrial systems where measures of species abundance were replicated over space and time. Overall, we found a significant positive relationship between spatial and temporal heterogeneity across all ecosystems. In addition, lifespan and successional stage were related to temporal heterogeneity. Therefore we found evidence that spatial heterogeneity is a potential tool to predict temporal heterogeneity in ecological communities. This data package consists of six files. First we used a (1) R script to derive community dynamic metrics from source files to calculate (2) spatial and temporal heterogeneity over time as well as other measures of the community. We used this derived dataset to run analyses (3) with a R script to study the relationship between spatial and temporal heterogeneity communities. These analyses resulted in three figures, (4) the overall relationship between spatial and temporal heterogeneity, (5) output of mixed models investigating how experimental and biological factors affect this relationship, and (6) figures exploring how lifespan of the study organism affects the relationship between spatial and temporal datasets.