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Two decades of change in sea star abundance at a subtidal site in Puget Sound, Washington
<p>Long-term datasets can reveal otherwise undetectable ecological trends, illuminating the historical context of contemporary ecosystem states. We used two decades (1997–2019) of scientific trawling data from a subtidal, benthic site in Puget Sound, Washington, USA to test for gradual trends and sudden shifts in total sea star abundance across 11 species. We specifically assessed whether this community responded to the sea star wasting disease (SSWD) epizootic, which began in 2013. We sampled at depths of 10, 25, 50 and 70 m near Port Madison, WA, and obtained long-term water temperature data. To account for species-level differences in SSWD susceptibility, we divided our sea star abundance data into two categories, depending on the extent to which the species is susceptible to SSWD, then conducted parallel analyses for high-susceptibility and moderate-susceptibility species. The abundance of high-susceptibility sea stars declined in 2014 across depths. In contrast, the abundance of moderate-susceptibility species trended downward throughout the years at the deepest depths – 50 and 70 m – and suddenly declined in 2006 across depths. Water temperature was positively correlated with the abundance of moderate-susceptibility species, and uncorrelated with high-susceptibility sea star abundance. The reported emergence of SSWD in Washington State in the summer of 2014 provides a plausible explanation for the subsequent decline in abundance of high-susceptibility species. However, no long-term stressors or mortality events affecting sea stars were reported in Washington State prior to these years, leaving the declines we observed in moderate-susceptibility species preceding the 2013–2015 SSWD epizootic unexplained. These results suggest that the subtidal sea star community in Port Madison is dynamic, and emphasizes the value of long-term datasets for evaluating patterns of change.</p>
Auditory cortex single unit population activity during natural sound presentation -- dataset
<p><strong>Overview</strong></p> <p>High-density multi-channel neurophysiology data were collected from primary (A1) and secondary (PEG) fields of auditory cortex of passively listening ferrets during presentation of a large natural sound library. Single unit spikes were sorted using Kilosort. This dataset includes spike times for 849 A1 units and 398 PEG units. Stimulus waveforms were transformed to log-spaced spectrograms for analysis (18 channels, 10 ms time bins). Data set includes raw sound waveforms as well and high resolution (1000 samples/sec) single-trial spike data. The authors request that any publication using this data cite the following work: https://www.biorxiv.org/content/10.1101/2022.06.10.495698v2</p> <p>Version 1.1 is updated with more examples and documentation. It also includes a less-processed version of the spike data that permits reconstruction of the experimental sequence used at each recording site and single-trial responses to the repeated validation stimuli.</p> <p><strong>Data format/description</strong></p> <p>Preprocessed neural data are aggregated in two main files. All recordings were performed during presentation of the same natural sound library to passively listening animals. During each experiments, stimuli were presented in a random order, and repeated validation stimuli were interleaved throughout the experiment. In the main files, data have been aligned to the same order by stimulus and averaged across repeated presentations (for the validation stimuli, which were presented 20 times during each experiment). The averaged validation data make up the first 27 seconds of each recoding block.</p> <ul> <li><strong>A1_NAT4_ozgf.fs100.ch18.tgz</strong> - data from 849 A1 single units and log spectrogram of stimuli aligned with spike times. Data are aggregated across 64- or 128-channel recordings from 22 sites in 4 animals.</li> <li><strong>PEG_NAT4_ozgf.fs100.ch18.tgz</strong> - data from 398 PEG single units and log spectrogram of stimuli aligned with spike times. Data are aggregated across 64-channel recordings from 12 sites in 2 animals.</li> </ul> <p>Raw sound files (44100/s sampling, wav format) and spike times (1K/s sampling, in the original experimental order) are also provided in separate files. Summary data of model performance from the paper are also included.</p> <ul> <li><strong>wav.zip</strong> - raw wav files. As of version 2 of this repository, the wav files have been truncated to the 1-sec duration that was used in the experiments</li> <li><strong>A1_single_sites.zip</strong>, <strong>PEG_single_sites.zip</strong> - collections of files, one per recording site, with spike times stored in the actual order of data collection (including interleaved repeated validation stimuli). These spikes have been binned at 100 Hz and sorted to have matched order across all sites in the processed files (<strong>A1_NAT4_ozgf.fs100.ch18.tgz</strong>, <strong>PEG_NAT4_ozgf.fs100.ch18.tgz</strong>, respectively).</li> <li><strong>A1_pred_correlation.csv</strong>, <strong>PEG_pred_correation.csv</strong> - Comma-separated value file containing cross-validated prediction accuracy for each A1, PEG unit for each of the five exemplar models. The "sig_auditory" column is true for all units classified as having significant auditory responses, as classified in the publication.</li> </ul> <p><strong>Example scripts</strong></p> <p>Python scripts included with this dataset demonstrate how to load the neural data and perform a CNN model fit. Running the scripts requires the NEMS0 python library, which is available open source at <a href="http://github.com/lbhb/NEMS0">https://github.com/lbhb/NEMS0</a>.</p> <p><em>Quick install</em></p> <p>Create and activate a new conda environment:</p> <blockquote> <p>conda create -n NEMS0 python=3.7<br> conda activate NEMS0</p> </blockquote> <p>Download NEMS0:</p> <blockquote> <p>git clone https://github.com/lbhb/NEMS0</p> </blockquote> <p>Install NEMS0:</p> <blockquote> <p>pip install -e NEMS0</p> </blockquote> <p>Detailed instructions for installing NEMS0 are available in the Github repository (https://github.com/lbhb/NEMS0).</p> <p><em>Demo scripts</em></p> <p>Once NEMS0 is installed and the data are downloaded, move to the directory where the data and demo scripts are stored and run them in a NEMS0 environment.</p> <ul> <li><strong>pop_cnn_load.py </strong>- Load the A1 data and compare predictions for two neurons (Fig 3) by two population models (stage 1 fit complete). Illustrates how to load the data using Python.</li> <li><strong>pop_cnn_fit.py</strong> - Load a pre-fit A1 population model (stage 1) and complete stage 2 fit (refinement) for a single neuron. Illustrates use of NEMS0 for CNN model fitting.</li> <li><strong>single_trial_demo.py</strong> - Script demonstrating how to load the single trial data for a repeated validation stimulus from one A1 neuron. Also how to compute the average population PSTH for a single validation stimulus at 1000 sec-1 sampling. Unzip <strong>A1_single_sites.zip</strong> in the director containing this script first in order for it to run correctly.</li> </ul> <p><strong>Funding</strong></p> <p>Data collection, software development and processing were supported by funding from the NIH (R01DC014950, R01EB028155).</p>
Supporting data for the manuscript: Sound velocity anisotropy and single-crystal elastic moduli of MgO to 43 GPa
<p><span>The uploaded two sets of experimental data contain the raw TDBS signals recorded at each pressure step for the MgO [100] and [111] samples. Please note that each ".txt" file contains two columns of data, the first column is time in picosecond, the second column is the relative change of reflectivity.</span></p> <p><span>The uploaded Tables "Table MgO 100" and "Table MgO 111" summarize our processed experimental data, as well as the recently measured refractive indexes and accordingly the derived longitudinal sound velocities. In which, the pressures are given in GPa, Brillouin frequencies in GHz, longitudinal sound velocities </span><em><span>V</span></em><span>L<100> </span><span>and </span><em><span>V</span></em><span>L<</span><span>111></span><span> in km/s. Refractive indexes are dimensionless.</span></p>
Call Graph Soundness in Android Static Analysis
<pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). - **results/**: Contains the results of our static analysis. Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre>
Figure 12 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 12. Seasonal rhythm of the nine species recorded, and of Herrera ancilla found in the national collections. Boundaries are approximately determined after examination of capture dates of 142 specimens.
Figure 6 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 6. Calling song of Dorisiana sutori. (A) Oscillogram and sonogram of a portion of a call; (B) three successive echemes; (C) detail of one echeme; (D) elementary oscillations of syllable A.
Figure 1 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 1. Calling song of Daza montezuma. (A) Oscillogram and sonogram of an entire call; (B) portion of part A; (C) portion of part B; (D) six successive pulses of part B.
Figure 11 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 11. Overall spectrum of the nine species recorded (window size: 512 pts, frequency resolution: 86 Hz).
Figure 4 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 4. Calling song of Pacarina schumanni. (A) Oscillogram and sonogram of one sequence; (B) train of group of sixteen pulses in the middle of the sequence; (C) detail of a group of six pulses; (D) elementary oscillations of one pulse.
Figure 13 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 13. Nycthemeral rhythm of the nine species during periods of investigation. White, no activity; striped, low activity; grey, high activity. Quesada gigas and Fidicinoides pronoe also call occasionally during the night. All the species have high activity at dawn and dusk except Daza montezuma and Dorisiana sutori. Approximate times of sun rise (SR), day (DA), silent period (SIL), sunset (SS), and night (NI) are indicated.
Figure 3 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 3. One sequence of the calling song of Neocicada sp. (A) Oscillogram and sonogram of an entire call; (B) three successive echemes of part A; (C) portion of part B; (D) seven successive pulses of part B.
Figure 8 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 8. Calling song of Fidicinoides pronoe. (A) Oscillogram and sonogram of the end of a call; (B) detail of one echeme of part A; (C) portion of part B; (D) elementary oscillations of part B.
Figure 10 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 10. Spectra of the nine species recorded (window size: 512 pts, frequency resolution: 86 Hz).
Figure 7 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 7. Calling song of Fidicinoides picea. (A) Oscillogram and sonogram of an entire call; (B) detail of one echeme of part A, (C) height groups of three pulses of part A; (D) elementary oscillations of six pulses of part B.
Figure 9 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 9. Calling song of Species A. (A) Oscillogram and sonogram of a portion of a call; (B) detail of 27 groups of two pulses; (C) detail of one group of two pulses; (D) elementary oscillations of one pulse.
Figure 5 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 5. Calling song of Miranha imbellis. (A) Oscillogram and sonogram of an entire call; (B) four successive echemes of part A; (C) portion of part B; (D) elementary oscillations of part B.
Figure 2 in Cicada acoustic communication: potential sound partitioning in a multispecies community from Mexico (Hemiptera: Cicadomorpha: Cicadidae)
Figure 2. Calling song of Quesada gigas. (A) Oscillogram and sonogram of the end of one call; (B) three successive echemes of part A; (C) portion of end of part B; (D) elementary oscillations of part B.
Fig. 4 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 4. The stress-related increase of the concentration of natural products. In principle, two major effects are responsible for the stress-related increase, i. e., the decrease of the reference value (e.g. dry weight), and an enhancement of biosynthetic activity. The latter one is due either to stress-related up-regulation ("active increase" of enzymatic activity) or a "passive shift" cause by the stress-related overreduction due to stomatal closure. Decreases of factors are displayed in red, the related enhancements in blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 3. Dry weight, alkaloid concentration, and alkaloid content in leaves of Catharanthus roseus plants under salt stress. (a): Dry weight of the entire leaves; (b): Concentration of alkaloids in old leaves; (c): Concentration of alkaloids in young leaves; (d): Total alkaloids content in all aerial plant parts. Differentiation between young and old leaves is mentioned in the Materials and methods section. Different lower-case letters on top of the columns for each period of time (10 and 20 days) indicate significant differences (P ≤ 0.05) as calculated using the least significant difference (LSD) test; n = 8. Every period has 5 bars, and these bars from left to right are control, 100 mM, 200 mM, 300 mM, 400 mM NaCl solution, respectively. The bars display the standard deviation.
Fig. 1 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 1. Evapotranspiration rate of drought-stressed Catharanthus roseus plants. The evapotranspiration rates for mild (20% watering) and severe drought stress (40% watering) were obtained by calculating the amount of water lost. Control plant results were used as a reference to normalize the rate. Arrows indicate the days of sampling.
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.