Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Data to "Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli"

This record contains analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Dehn, K.&dagger;, Maiello, G.&dagger;, Hartmann, F., Morgenstern, Y., Hawkins, S.J., Offner, T., Walter, J., Hassenklöver, T., Manzini, I., Fleming, R.W. (2024) Human shape perception spontaneously discovers the biological origin of novel, but natural, stimuli. bioRxiv, 2024-12. https://doi.org/10.1101/2024.12.21.629735   &dagger;Co-first author

opencc-by-4.0Sep 2022View details →

Protein structure files for the paper "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al.

This archive contains models of HRAS, KRAS, and NRAS homo- and heterodimers with various mutations discussed in the paper,  "Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth" in Nature Cell Biology by Tang et al. as well as crystallographic dimers of these proteins as identified by the ProtCAD database, http://dunbrack2.fccc.edu/ProtCAD/Results/PfamArchClusterInfo.aspx?GroupId=8 (cluster 5). Several of the models are shown in Supp. Figure 11b and the crystallographic dimers of RAS that provide evidence for the possible biological relevance of these models are shown in Supp. Figure 11a. The crystallographic dimers were identified by clustering all possible interfaces generated by symmetry operators in crystals of HRAS, KRAS, and NRAS as described in the paper: Xu, Q., Dunbrack, R.L. ProtCID: a data resource for structural information on protein interactions. Nat Commun 11, 711 (2020). https://doi.org/10.1038/s41467-020-14301-4. The models were created by superposing monomers of HRAS, KRAS, or NRAS onto the alpha4-alpha5 dimer present in the crystal of PDB entry 3k8y. Mutations were made in PyMOL. The structures were relaxed with the FastRelax protocol and the Ref2015 scoring function in the program Rosetta, which uses the backbone-dependent rotamer library of Shapovalov and Dunbrack to repack side chains. The crystallographic dimers are contained in a zipped PyMOL session. The mmCIF format for all the structures is present in a zip file, Tang_et_al_crystallographic_and_modeled_RAS_dimer_ciffiles.zip. The PyMOL session and zip file contains 87 HRAS dimers, 14 KRAS dimers, and 1 NRAS dimer, all having the interface consisting of the alpha4 and alpha5 helices. The PyMOL session also contains the modeled structures. Only Mg ions and GTP/GNP/GDP ligands are shown. Others are present but hidden and may be displayed by PyMOL ("show sticks, het").

opencc-by-4.0Sep 2022View details →

BIOLOGICAL DATA in CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system

BIOLOGICAL DATA to estimate the carbon dioxide budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system. Álvarez-Salgado et al. (2022)  estimate the carbon dioxide and total alkalinity budgets due to the Mediterranean mussels (Mytilus galloprovicialis) growing in suspended culture in a low seston environment such as the Galician Rías (NW Spain). This database contains the biological data needed to estimate the carbon dioxide fluxes and changes in total alkalinity induced by the different biological processes involved in mussel growth.   Manuscript available at: <a href="https://doi.org/10.1016/j.scitotenv.2022.157867">https://doi.org/10.1016/j.scitotenv.2022.157867</a> Álvarez-Salgado, X.A., Fernández-Reiriz, M.J., Fuentes-Santos, I., Antelo, L.T., Alonso, A.A., Labarta, U., 2022. CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system. Sci. Total Environ. 849, 157867.

opencc-by-4.0May 2024View details →

Simulation systems for: "Pore formation in complex biological membranes: torn between evolutionary needs"

Simulation systems for the publication: <div> <div> <div> Leonhard J. Starke, Christoph Allolio, and Jochen S. Hub, Pore formation in complex biological membranes: torn between evolutionary needs, BioRxiv (2024), doi: <a href="https://doi.org/10.1101/2024.05.06.592649">10.1101/2024.05.06.592649</a> Required software: GROMACS Chain Coordinate, a modified GROMACS variant for pore formation across membranes or stalk formation between membranes: <a href="https://gitlab.com/cbjh/gromacs-chain-coordinate">https://gitlab.com/cbjh/gromacs-chain-coordinate</a> See README_small.sh and README_large.sh files for instructions on how to run pulling simulations for inducing pores in the provided complex membrane models. </div> </div> </div>

opencc-by-4.0May 2021View details →

Complete Rxivist dataset of scraped biology preprint data

<a href="https://rxivist.org">rxivist.org</a> allowed readers to sort and filter the tens of thousands of preprints posted to <a href="https://www.biorxiv.org">bioRxiv</a> and <a href="https://www.medrxiv.org">medRxiv</a>. Rxivist used a custom web crawler to index all papers posted to those two websites; this is a snapshot of Rxivist the production database. The version number indicates the date on which the snapshot was taken. See the included "README.md" file for instructions on how to use the "rxivist.backup" file to import data into a PostgreSQL database server. Please note this is a different repository than the one used for <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">the Rxivist manuscript</a>—that is in <a href="https://doi.org/10.5281/zenodo.2465689">a separate Zenodo repository</a>. You're welcome (and encouraged!) to use this data in your research, but please cite our paper, now published <a href="https://doi.org/10.7554/eLife.45133">in eLife</a>. Previous versions are also available pre-loaded into Docker images, available at <a href="https://hub.docker.com/r/blekhmanlab/rxivist_data">blekhmanlab/rxivist_data</a>. Version notes: <ul> <li>2023-03-01 <ul> <li>The final Rxivist data upload, more than four years after the first and encompassing 223,541 preprints posted to bioRxiv and medRxiv through the end of February 2023.</li> </ul> </li> <li>2020-12-07*** <ul> <li>In addition to bioRxiv preprints, the database now includes all medRxiv preprints as well. <ul> <li>The website where a preprint was posted is now recorded in a new field in the "articles" table, called "repo".</li> </ul> </li> <li>We've significantly refactored the web crawler to take advantage of developments with the bioRxiv API. <ul> <li>The main difference is that preprints flagged as "published" by bioRxiv are no longer recorded on the same schedule that download metrics are updated: The Rxivist database should now record published DOI entries the same day bioRxiv detects them.</li> </ul> </li> <li>Twitter metrics have returned, for the most part. Improvements with the Crossref Event Data API mean we can once again tally daily Twitter counts for all bioRxiv DOIs. <ul> <li>The "crossref_daily" table remains where these are recorded, and daily numbers are now up to date.</li> <li>Historical daily counts have also been re-crawled to fill in the empty space that started in October 2019.</li> <li>There are still several gaps that are more than a week long due to missing data from Crossref.</li> <li>We have recorded available Crossref Twitter data for all papers with DOI numbers starting with "10.1101," which includes all medRxiv preprints. However, there appears to be almost no Twitter data available for medRxiv preprints.</li> </ul> </li> <li>The download metrics for article id 72514 (DOI 10.1101/2020.01.30.927871) were found to be out of date for February 2020 and are now correct. This is notable because article 72514 is the most downloaded preprint of all time; we're still looking into why this wasn't updated after the month ended.</li> </ul> </li> <li>2020-11-18 <ul> <li>Publication checks should be back on schedule.</li> </ul> </li> <li>2020-10-26 <ul> <li>This snapshot fixes most of the data issues found in the previous version. Indexed papers are now up to date, and download metrics are back on schedule. The check for publication status remains behind schedule, however, and the database may not include published DOIs for papers that have been flagged on bioRxiv as "published" over the last two months. Another snapshot will be posted in the next few weeks with updated publication information.</li> </ul> </li> <li>2020-09-15 <ul> <li>A crawler error caused this snapshot to exclude all papers posted after about August 29, with some papers having download metrics that were more out of date than usual. The "last_crawled" field is accurate.</li> </ul> </li> <li>2020-09-08 <ul> <li>This snapshot is misconfigured and will not work without modification; it has been replaced with version 2020-09-15.</li> </ul> </li> <li>2019-12-27 <ul> <li>Several dozen papers did not have dates associated with them; that has been fixed.</li> <li>Some authors have had two entries in the "authors" table for portions of 2019, one profile that was linked to their ORCID and one that was not, occasionally with almost identical "name" strings. This happened after bioRxiv began changing author names to reflect the names in the PDFs, rather than the ones manually entered into their system. These database records are mostly consolidated now, but some may remain.</li> </ul> </li> <li>2019-11-29 <ul> <li>The Crossref Event Data API remains down; Twitter data is unavailable for dates after early October.</li> </ul> </li> <li>2019-10-31 <ul> <li>The Crossref Event Data API is still <a href="https://status.crossref.org/">experiencing problems</a>; the Twitter data for October is incomplete in this snapshot.</li> <li>The README file has been modified to reflect changes in the process for creating your own DB snapshots if using the newly released PostgreSQL 12.</li> </ul> </li> <li>2019-10-01 <ul> <li>The Crossref API is back online, and the "crossref_daily" table should now include up-to-date tweet information for July through September.</li> <li>About 40,000 authors were removed from the author table because the name had been removed from all preprints they had previously been associated with, likely because their name changed slightly on the bioRxiv website ("John Smith" to "J Smith" or "John M Smith"). The "author_emails" table was also modified to remove entries referring to the deleted authors. The web crawler is being updated to clean these orphaned entries more frequently.</li> </ul> </li> <li>2019-08-30 <ul> <li>The Crossref Event Data API, which provides the data used to populate the table of tweet counts, has not been fully functional since early July. While we are optimistic that accurate tweet counts will be available at some point, the sparse values currently in the "crossref_daily" table for July and August should not be considered reliable.</li> </ul> </li> <li>2019-07-01 <ul> <li>A new "institution" field has been <a href="https://github.com/blekhmanlab/rxivist/commit/1cb570703085841e80cc3073af445bc86f0cbb63#diff-a04b1c1a66d16f9b3acfed9b9d2128c5">added</a> to the "article_authors" table that stores each author's institutional affiliation as listed on that paper. The "authors" table still has each author's most recently observed institution. <ul> <li>We began collecting this data in the middle of May, but it has not been applied to older papers yet.</li> </ul> </li> </ul> </li> <li>2019-05-11 <ul> <li>The README was updated to correct a link to the Docker repository used for the pre-built images.</li> </ul> </li> <li>2019-03-21 <ul> <li>The license for this dataset has been changed to <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY</a>, which allows use for any purpose and requires only attribution.</li> <li>A new table, "publication_dates," has been added and will be continually updated. This table will include an entry for each preprint that has been published externally for which we can determine a date of publication, based on data from Crossref. (This table was previously included in the "paper" schema but was not updated after early December 2018.)</li> <li>Foreign key constraints have been added to almost every table in the database. This should not impact any read behavior, but anyone writing to these tables will encounter constraints on existing fields that refer to other tables. Most frequently, this means the "article" field in a table will need to refer to an ID that actually exists in the "articles" table.</li> <li>The "author_translations" table has been removed. This was used to redirect incoming requests for outdated author profile pages and was likely not of any functional use to others.</li> <li>The "README.md" file has been renamed "1README.md" because Zenodo only displays a preview for the file that appears first in the list alphabetically.</li> <li>The "article_ranks" and "article_ranks_working" tables have been removed as well; they were unused.</li> </ul> </li> <li>2019-02-13.1 <ul> <li>After consultation with bioRxiv, the "fulltext" table will not be included in further snapshots until (and if) concerns about licensing and copyright can be resolved.</li> <li>The "docker-compose.yml" file was added, with corresponding instructions in the README to streamline deployment of a local copy of this database.</li> </ul> </li> <li>2019-02-13 <ul> <li>The redundant "paper" schema has been removed.</li> <li>BioRxiv has begun making the full text of preprints available online. Beginning with this version, a new table ("fulltext") is available that contains the text of preprints that have been processed already. The format in which this information is stored may change in the future; any digression will be noted here.</li> <li>This is the first version that has <a href="https://cloud.docker.com/u/blekhmanlab/repository/docker/blekhmanlab/rxivist_data">a corresponding Docker image</a>.</li> </ul> </li> </ul>

opencc-by-4.0Oct 2022View details →

A Curated Gene and Biological System Annotation of Adverse Outcome Pathways Related to Human Health

Adverse Outcome Pathways (AOPs) are multi-scale models of biological mechanisms connecting molecular initiating events to adverse outcomes through measurable key events. AOPs can guide the use and development of new approach methodologies (NAMs) aimed at reducing animal experimentation in chemical safety assessment. Here, we present a comprehensive molecular annotation of AOPs relevant to human health to embed the AOP framework into molecular data interpretation, which supports the development and application of novel AOP-based approaches in biomedical research. Please cite the following publication alongside this Zenodo entry when using the data: Saarimäki, L.A., Fratello, M., Pavel, A. et al. A curated gene and biological system annotation of adverse outcome pathways related to human health. Sci Data 10, 409 (2023). https://doi.org/10.1038/s41597-023-02321-w

opencc-by-4.0Jul 2023View details →

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Abstract Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks. Data Description This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.  As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those. Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets. Paper & Code The original paper for this article can be accessed here: <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> To access the codes relevant for this project, please access the project GitHub Repos: <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> If using this work, please cite it based on the above paper, or using the following BibTex: <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre>   Data Access The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi Funding ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

openCC (other)Feb 2024View details →

Tree recruitment from sites across Southern Michigan including the University of Michigan Biological Station, Pellston, MI (2022)

As a result of current climate change, flooding events are becoming more frequent and lasting longer, resulting in temporal floods in areas that have not historically experienced this disturbance. One critical aspect of forest dynamics that could be significantly impacted by increasing flooding is tree species recruitment. While adult trees may be able to survive temporary flooding, establishing seedlings with shallow root systems may not. A single flooding event could jeopardize decades of recruitment if seedlings are unable to survive the anaerobic conditions imposed by higher water levels. Despite the potential impact of flooding on forest dynamics, there is little information on seedling recruitment patterns after exposure to flooding. To understand how flooding conditions could possibly be impacting forest recruitment, we conducted a field observational study across seven temperate forests. We gathered data on seedling abundance and diversity in areas with signs of recent flooding, as well as in nearby control (dry) areas. Our results document the adverse effects flooding conditions have on temperate forest recruitment dynamics, providing insights into how tree recruitment might be impacted by shifts in flooding patterns.

openCC (other)Jun 2025View details →

Douglas Lake Ice Cover at the University of Michigan Biological Station, Pellston, MI 1931 to 2025

This dataset represent the ice on and ice off dates for Douglas Lake in Pellston, Michigan. The first observations are from the 1930 and were intermittently documented until the mid 1970s. The observations are complete since then to current.

openCC (other)Apr 2024View details →

Snail Shell Strength and Total Crush Force of a Northern Michigan Snail as a Function of Predation Risk at the University of Michigan Biological Station Stream Research Facility (5/31/23-8/1/23)

Many prey organisms respond to the non-consumptive effects of predators by altering their physiology, morphology, and behavior. These inducible defenses can create refuges for prey by decreasing the likelihood of consumption by predators. Some prey, as in marine mollusks, have been shown to alter their morphology in response to the presence of size-limited predation. To extend this work into the freshwater realm, we presented pointed campeloma snails (Campeloma decisum) to chemical cues from a natural predator, the rusty crayfish (Faxonius rusticus), to better understand how snail morphology changes under the threat of predation. The total force needed to crush shells, total shell length, aperture width, and total weight, along with changes to these three body measurements were recorded for each individual and used to quantify morphological changes as a function of risk. Snails exposed to crayfish chemical cues needed significantly more force to crush their shells than controls (p = 0.002). Total shell length was greater in crayfish exposed snails than control snails (p = 0.002), and snails in the crayfish treatment also showed significantly more change in shell length than control snails (p = 0.003). Similarly, aperture width was significantly greater in exposed snails (p = 0.002). However, exposed snails exhibited significantly less change in aperture width than controls (p = 0.017). Finally, we found that snails exposed to crayfish weighed significantly more than snails in the control (p = 0.0009). Thus, the results of this study show that morphology of gastropods is altered in the presence of predators, and this may be an antipredator tactic directly related to risk.

openCC (other)Nov 2024View details →

Protist Dispersal Detection: University of Michigan Biological Station, July 2024

This dataset contains the results of a field dispersal array assembled in Gates Bog, Pellston, Michigan. The data were collected by a graduate student, and consist of measurements of protist presence or absence in 1mL fluid samples taken from pitcher plants and centrifuge tubes in the array. The dataset contains both initial protist detection from the fluid samples, as well as detection after a 24 hour incubation period. The dataset also contains the positions of each plant and tube used for sample collection and their distances from the established source population at the center of the array. We used the purple pitcher plant, Sarracenia purpurea, as a model system to explore questions of specialist protist dispersal. Newly opened pitchers are sterile, providing virgin habitat open to community assembly of highly specialized protist species (Peterson 2008). The placement of a known community of protists at the center of an uncolonized array of habitat patches allows us to identify both sources and destinations of dispersing microbes in the array. The purpose of this study is to measure dispersal rates for a subset of pitcher plant protist species.

openCC (other)Nov 2024View details →

Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)

Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.

openCC (other)Dec 2024View details →

Pitcher plant herbivory experimental data at the University of Michigan Biological Station, Pellston, MI 2024-2025

Coping with low-nutrient environments has led to the repeated evolution of plant carnivory. Given the repeated evolution of carnivory as well as the facultative nature of this otherwise costly trait, why are carnivorous plants not more speciose in wet, sunny, nutrient-poor sites? Recent evidence suggests herbivores may play an important role in limiting the success of plants with specialized nutrient acquisition strategies (e.g. nitrogen-fixing bacterial associates), as herbivores are drawn to more nutrient-rich plant tissue. To test this hypothesis in carnivorous plants, we conducted a factorial herbivore exclusion and prey addition experiment on Sarracenia purpurea, the purple pitcher plant. Specifically, we examined whether 1) plant growth rate is maximized at intermediate levels of prey intake, and 2) if this pattern is caused by preferential consumption by herbivores of plants with high nutrient intake. To test these hypotheses, we measured plant growth and herbivore damage on 110 pitcher plants (Sarracenia purpurea) growing at Mud Lake Bog near UMBS from June to August 2024. To measure effects of stored nutrients on plant growth and herbivory, we plan to collect 2nd year early season growth data in June of 2025.

openCC (other)Dec 2024View details →

The fate of a plant defense mutualism in a warming world at the University of Michigan Biological Station, Pellston, MI (2024-2026)

Mutualisms are vital to plant survival and reproduction, but climate warming has the potential to alter these interactions. One such mutualism involves foliar mites, which provide plants with defense by consuming harmful fungi. In exchange, plants offer protective structures on their leaves called domatia. Both mite and fungal communities are potentially temperature-sensitive, and warming may shift community composition, potentially altering trophic interactions between botch groups. However, the specific changes in mite and fungal community composition and their implications for plant-mite mutualism and plant performance remain unclear. To investigate the responses of both of these communities to warming, and the effects these changes will have on plants, I conducted a nested factorial field experiment with 96 P. serotina seedlings at the University of Michigan Biological Station. Plants were warmed using open top chambers, nested within which were fully factorial manipulations of the mite and fungal communities. Each group was manipulated using either pruning tar to exclude mites or Quilt fungicide to exclude fungi.

openCC (other)Jan 2025View details →

Continuous Climate Measurements from Highlands Biological Station, Highlands, North Carolina, USA, 2020-2025

The Highlands Biological Station (HBS) has been collecting rainfall and air temperature measurements since 1961. In October 2020 a new Campbell Scientific Instruments climate station was deployed on the north campus of HBS. Temperature, humidity, rainfall, wind speed, wind direction, and photosynthetically radiation (PAR) measurements are collected every 60 seconds and output as averages/total every hour.

Daily Summary of Continuous Climate Measurements from Highlands Biological Station, Highlands, North Carolina, USA

Continuous water quality measurements at Lindenwood Lake, Highlands Biological Station, Highlands, North Carolina, USA, 2022-2025

Measurements of turbidity, conductivity, dissolved oxygen, and water temperature were collected via an YSI EXO3 sonde in a 1.1 ha lake on the campus of Highlands Biological Station, Macon County, North Carolina. The sonde collects measurements every 15 minutes at a depth of ~0.5 m.

Daily Summary of Continuous water quality measurements at Lindenwood Lake, Highlands Biological Station, Highlands, North Carolina, USA, 2022-2025

Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 01, Highlands Biological Station, Highlands, NC, USA, 2022-2025

Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Betula alleghanensis and formerly Tsuga canadensis, the latter of which has mostly succombed to the Hemlock Woolly Adelgid.

Daily Summary of Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 01, Highlands Biological Station, Highlands, NC