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opencc-by-4.0May 2022View details →

Nocturnal flight calls dataset: long-term acoustic monitoring of birds migrating at night

General Description: This is a development set used in the experiments in the Ph.D. thesis: "Nowe metody akustycznej identyfikacji ptaków migrujących nocą" ("Novel methods of acoustic identification of birds migrating at night") by Hanna Pamula. The project focuses on the detection (and - partially - classification) of passerine birds' calls from long-term audio recordings collected during bird autumn migration between 2016 and 2019. The dataset consists of >56,5 hours of recordings with annotations of nocturnal flight calls of passerine birds migrating along the Baltic Sea coast, Poland.   Folder Structure Development_Set_3.1.zip |_Development_Set_3.1/     |__Training_Set/             |____*.wav             |____*.txt     |__Validation_Set/             |____*.wav             |____*.txt     |__Testing_Set/             |____*.wav             |____*.txt Training Set: 86 recordings Validation Set: 8 recordings Testing set: 18 recordings (BUT: uploaded 20 recordings, as in the previous version of the dataset - version 3, two additional recordings were used. Then, they were deleted in the final version of development set 3.1. Two additional recordings are: 'BUK5_20161101_002104a and BUK5_20161101_002104b) Names of waveforms and annotations are matching. Waveforms: The whole dataset consists of 114 recordings. One hundred thirteen recordings are about 30 minutes long (29min56s – 29min 59s), one recording is 1min20s. All data were recorded at 44,100 Hz sampling rate, one channel, with SM2 Wildlife Acoustics recorders + SMX-NFC microphone. The recording sessions were performed at night (starting time and date denoted in a file name) on the Baltic Sea coast in Poland (Dąbkowice, near Darłowo). Annotations: Transcriptions were produced using Audacity 2.4.1: https://www.audacityteam.org/ by an experienced birdwatcher, Hanna Pamula. While every effort has been made to ensure the quality and accuracy of the labels, some errors may occur, taking into account the difficulty of nocturnal call recognition and transcription tasks in general. Transcription format: [Starting time (sec)] [Ending time (sec)] [Label] Meaning of the labels: 1. Positive classes – migrating passerine birds: <ul> <li>'s' – song thrush call (Turdus philomelos)</li> <li>'k' – blackbird call (Turdus merula)</li> <li>'d' – redwing call (Turdus iliacus)</li> <li>'r' – robin call (Erithacus rubecula)</li> <li>‘kwiczol’ – fieldfare call (Turdus pilaris)</li> <li>‘skowronek’ – skylark call (Alauda arvensis)</li> <li>Each of the above labels could also have a question mark '?', e.g. 'r?', 'k?' – meaning that it's not a sure label. In a bird call detection task, they are regarded as positive chunks containing bird call(s).</li> <li>'ni' – non identified bird call (distant/quiet/not recognized)</li> </ul> Only the supposed calls of migrating passerine birds were labeled; other sounds of species were ignored (e.g., robin's tik-calling, which can be often heard at dusk, and may be regarded as warning sounds). 2. Negative classes – other marked sound events: <ul> <li>'g' – other bird calls/songs/sounds. Sounds that could confuse the model; for example, sounds of migrating geese, cranes, plovers calls, etc.</li> <li>'gh' – human voices</li> <li>'t' – cracks, clicks, raindrops, other noise</li> <li>‘puszczyk’ – tawny owl voice (Strix aluco)</li> <li>'czapla' – grey heron voice (Ardea cinerea)</li> </ul> Not all occurrences of the negative sounds were labeled – only some chosen examples to represent the possible noises/negative samples. Thus these annotations can't be used for entirely different detection / classification tasks than intended, e.g., detecting migrating cranes or human voices in long-term recordings. 3. Labels to be excluded from analysis: <ul> <li>'???', '??? mysz', '??? high freq' – unknown, not sure if the sound event is a birds' call or not. Uncertainty about belonging to a positive/negative class in the detection task.</li> </ul>

(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0008 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0004 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0001 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0003 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0002 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0007 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0009 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0006 – Ceratitis capitata TREhs43-hidAla5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

(10)-Strobl2022A-DS0005 – Ceratitis capitata TREhs43-hid^Ala5_F1m2 long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy

opencc-by-4.0May 2022View details →

Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter

Data and code for mixed-model analysis for the article:  Mukhacheva S.V. (2022) Long-term dynamics of trace elements concentrations in the organism of the shrews (Sorex) during the periods of high and reduction emissions from the copper smelter" // Russian Journal of Ecology. Vol. 5.  Data provided by S.V. Mukhacheva Code provided by A.N. Sozontov

opencc-zeroJun 2022View details →

Data for: Red foxes enhance long-term tree growth near the Arctic treeline

Recent climate warming is expected to increase tree growth and productivity, substantially altering ecological function and boundaries in northern ecosystems. Temperature and precipitation largely determine the range and growth of trees in any biome, yet variations in microsite conditions can also influence tree growth on a finer scale. By altering essential resources and habitat conditions, terrestrial organisms could modify Subarctic tree growth. Red foxes (Vulpes vulpes) are found in most terrestrial ecosystems and are considered ecosystem engineers by enriching soil nutrients and plant composition through denning. Added soil nutrients from prey remains, feces, and urine could benefit tree growth in Subarctic regions by alleviating soil nutrient limitations. We examined growth in white spruce (Picea glauca) trees growing on eight red fox dens and paired control sites near Churchill, Manitoba, Canada, at the Arctic treeline. Radial growth was 55% higher for trees on dens than on control sites between 1897 and 2017, despite similarities in tree ages, densities, and regional climate across all sites. By promoting tree growth near the treeline, red foxes may influence the position of the Arctic treeline. Although the impacts on tree growth largely depend on the spatial distribution of dens and predator activity in the boreal forest, predators can create distinct microhabitats across the landscape via ecosystem engineering processes, leading to increased vegetation productivity, persisting over many decades.

opencc-zeroJun 2022View details →

Data from: AgMate: an optimal mating software versus other mate pair designing methods on long-term breeding of Pinus taeda L

Breeding objectives aim to optimize two crucial but contrasting goals of maximizing genetic gain while managing genetic diversity. In advanced generations, this becomes a challenge in monoecious conifer tree species breeding programs because they suffer from inbreeding. Developing an algorithm that maximizes genetic gain while maintaining genetic diversity for monoecious species is imperative. While methods and algorithms for animal breeding are well-established, an efficient algorithm suited to monoecious species remains elusive. Towards this goal, we have adopted an evolutionary genetic algorithm, the Differential Evolution algorithm, to optimize mate pair designing in Pinus taeda (loblolly pine), a widely planted pine species in the southern USA. AgMate, an optimal mating for monoecious species software, is a multi-functional, completely automated optimization software. It utilizes genetic relationships and breeding values as input to create an optimal mating list. AgMate maximizes the genetic gain and minimizes the increase in average coancestry and inbreeding in the proposed progeny. AgMate was more effective in optimizing mating lists than positive assortative mating and random mating in short-term and long-term settings. AgMate mating list resulted in an average 93% genetic gain each cycle for ten cycles while simultaneously minimizing the increase in coancestry to 0.086. The framework and methods adapted for Pinus taeda are also relevant to the breeding of other monoecious species.

opencc-by-4.0Apr 2022View details →

Global Long-term River Width (GLOW)

This repository archives the Global LOng term river Width (GLOW) dataset measured from Landsat for 1984-2020. There are three datasets in this repository: Width, Cross_section, and Environmental_parameter_global. Width contains the GLOW dataset, which has three attributes: ID, date (mm/dd/yyyy), and width (unit: meters). Each cross-section is identified by a unique ID in the format of R########XS#######. The dataset is grouped into eight regions based on the geospatial location. Cross-section contains the shapefiles indicating the GLOW cross-section locations, which have three attributes: ID, lat (latitude), and lon (longitude). Environmental_parameter_global contains the environmental parameters generated in this study for the Random Forest analysis, which has eighteen parameters for all global rivers at the river reach level, including the temporal trend slope, interquartile range, 25th percentile, and 75th percentile of air temperature, snowmelt, precipitation, evapotranspiration, and river discharge.    Please refer to the following paper for more details: Feng, D., Gleason C.J., Yang X., Allen G.H., and Pavelsky T.M., How have global river widths changed over time? Water Resources Research

opencc-by-4.0Aug 2021View details →

Long-term observation of the egg and chick size in the nests of Larus ichthyaetus in Lake Chany,West Siberia, Russia

Data on long-term observation and morphological study of the eggs of the great black-headed gull Larus ichthyaetus in the nesting colonies of gulls on islands of Lake Chany. Russia. Data on long-term observation and morphological study of the eggs (Data S1) and chick (Data S2) of the great black-headed gull Larus ichthyaetus in the nesting colonies of gulls on islands of Lake Chany, Russia   Abstract  This data set describes the long-term observation and morphological study of the eggs of the great black-headed gull Larus ichthyaetus in the gull nesting colonies on the islands of Lake Chany. Lake Chany is located in the Baraba forest-steppe of the West Siberian Plain, Russia, between the Ob and Irtish rivers. Lake Chany is protected by the Ramsar Convention on the Wetlands of International Importance, indicating that the lake is an important site for migratory birds, including L. ichthyaetus. This dataset contains the size and fate of all eggs, as well as the size of hatched chicks in 1164 observed L. ichthyaetus nests from 1993 to 2003.

opencc-by-4.0Jul 2022View details →

Data and R code for long-term study of fire and climate effects on water quality in Clear Lake, California

Long-term relationships between water quality, fire and climate for Clear Lake, California. Although the watershed has historically experienced frequent fire, the 2018 Mendocino Complex, which was the largest wildfire complex in state history, burned approximately 40% of the watershed, sparking concerns about drinking water quality and lake ecosystem health. This analysis spans approximately 1968-2021.

opencc-by-4.0Jul 2022View details →

Code and Data associated with "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with Escherichia coli"

Code and data sufficient to reproduce analyses in "Idiosyncratic purifying selection on metabolic enzymes in the long-term evolution experiment with Escherichia coli".

opencc-zeroJul 2022View details →

Long-term experimental drought alters floral scent and pollinator visits in a Mediterranean plant community despite overall limited impacts on plant phenotype and reproduction

Pollinators are declining globally, with climate change implicated as an important driver. Climate change can induce phenological shifts and reduce floral resources for pollinators, but little is known about its effects on floral attractiveness and how this might cascade to affect pollinators, pollination functions and plant fitness. We used an in situ long-term drought experiment to investigate multiple impacts of reduced precipitation in a natural Mediterranean shrubland, a habitat where climate change is predicted to increase the frequency and intensity of droughts. Focusing on three insect-pollinated plant species that provide abundant rewards and support a diversity of pollinators (Cistus albidus, Salvia rosmarinus and Thymus vulgaris), we investigated the effects of drought on a suite of floral traits including nectar production and floral scent. We also measured the impact of reduced rainfall on pollinator visits, fruit set and germination in S. rosmarinus and C. albidus. Drought altered floral emissions of all three plant species qualitatively, and reduced nectar production in T. vulgaris only. Apis mellifera and Bombus gr. terrestris visited more flowers in control plots than drought plots, while small wild bees visited more flowers in drought plots than control plots. Pollinator species richness did not differ significantly between treatments. Fruit set and seed set in S. rosmarinus and C. albidus did not differ significantly between control and drought plots, but seeds from drought plots had slower germination for S. rosmarinus and marginally lower germination success in C. albidus. Synthesis. Overall, we found limited but consistent impacts of a moderate experimental drought on floral phenotype, plant reproduction and pollinator visits. Increased aridity under climate change is predicted to be stronger than the level assessed in the present study. Drought impacts will likely be stronger and this could profoundly affect the structure and functioning of plant-pollinator networks in Mediterranean ecosystems.

opencc-by-4.0Aug 2022View details →

Long-Term Lithium Abundance Signatures following Planetary Engulfment [Dataset]

MESA inlist files and chemical network used for the published article Sevilla et. al (2022). The inlist files given are for a 1 solar mass model.  To generate the other models in the paper with a different stellar mass, the initial mass in the pre-ZAMS inlist was changed. We first used prezams.inlist to generate the model star, with mixing processes besides convection.  This model saved after this run would be used for all other model runs for the same stellar mass. For a model run without planetary engulfment, we used one inlist to evolve the star through the main-sequence, and then obtained the desired data from the resulting profile and history files. For model runs with planetary engulfment, we first ran the model using an accretion inlist to simulate the engulfment process by turning on accretion, and then we used a main-sequence (MS) inlist to evolve the star through the main-sequence to produce the desired data. The corresponding inlists for each model run are as follows: 10 Earth mass engulfment with included all mixing processes considered in our paper, which were convection, overshoot, thermohaline mixing, elemental diffusion, and a min_D_mix coefficient: 10earth_accretion.inlist for accretion, and 10earth_postaccretion.inlist for MS. 10 Earth mass engulfment with all mixing processes except thermohaline mixing: 10earth_accretion_nothermohaline.inlist for accretion, and 10earth_postaccretion_nothermohaline.inlist for MS. 10 Earth mass engulfment with all mixing processes except elemental diffusion: 10earth_accretion_nodiffusion.inlist for accretion, and 10earth_postaccretion_nodiffusion.inlist for MS. 1 Earth mass engulfment with all mixing processes: 1earth_accretion.inlist for accretion, and 1earth_postaccretion.inlist for MS. 100 Earth mass engulfment with all mixing processes: 100earth_accretion.inlist for accretion, and 100earth_postaccretion.inlist for MS. 10 Earth mass engulfment with all mixing processes occurring 1 gyr after ZAMS: We first ran the model with evolve_1gyr.inlist to save a model of the star 1 gyr after ZAMS.  Then, we used 1gyr_accretion.inlist for accretion, and 1gyr_postaccretion.inlist for MS. No planetary engulfment: No accretion inlist was used; we immediately ran no_accrete.inlist as the MS inlist.   We also uploaded the chemical network used for our model as the file accrete.net.