cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.crjdfn36n
title: Data for: Substantial intraspecific trait variation across a hydrological gradient in northern Australian fishes

description: Trait-based models of ecological communities and ecosystem functioning
 often fail to account for intraspecific variation in functional
 traits, assuming that intraspecific variability is negligible
 compared to interspecific variability. However, this assumption remains
 poorly tested across vertebrate animals where past studies routinely
 describe species according to mean trait values without explicit
 consideration of individual trait variability. We assessed nine
 functional traits for 4,254 individuals belonging to 15 freshwater fish
 species from 11 families in Northern Australia, including: body
 elongation, body shape, caudal peduncle throttle, eye size, eye position,
 gape shape, gape size, mouth position and pectoral fin length. We
 quantified the extent and geography of intraspecific trait variability and
 its relationship with riverine hydrologic regimes using a structured
 sampling design. Using a combination of single- and multi-trait
 analyses, we demonstrate that intraspecific trait variability can
 contribute up to 70% of the total trait variability depending on the
 attribute considered and averaged 31% across all traits. The magnitude of
 intraspecific trait variability also varied across the hydrological
 permanency gradient. Fish assemblages in intermittent streams subjected to
 frequent environmental disturbance had high intraspecific trait
 variability, most likely due to strong abiotic filters limiting
 interspecific divergence. Conversely, assemblages in perennial rivers with
 less harsh environmental filters but with a larger pool of species
 expressed lower intraspecific trait variability: this is most likely due
 to stronger resource competition (biotic filter), which promotes
 specialization of resource use and, consequently, interspecific
 divergence. Our study provides the first evidence of
 intraspecific trait variability driven by a disturbance gradient for an
 animal group and points to the need for additional research into the
 functional importance of intraspecific variability in animal ecology. A
 better understanding of the patterns, drivers, and implications of
 intraspecific trait variability will help guide mechanistic-based
 predictions of the effects of environmental changes on community
 assemblage and ecosystem processes.
description: Headers: Bd - Body depth
 PEL - Pectoral fin length Throt - Caudal
 peduncle throttling ED - Eye diameter
 EP - Eye position GS - Gape size
 GSh - Gape shape MP - Mouth position
 BS - Body shape
DOI: 10.15468/dl.tq69j5
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Données Oiseaux des Jardins, iNaturalist Research-grade Observations, Pl@ntNet automatically identified occurrences, Pl@ntNet observations, Atlas herpétologiques des Pays de la Loire 2016-2018-Préfiguration de l'atlas herpétologique des Pays de la Loire : année 2018, EOD – eBird Observation Dataset)",
 "Geometry POLYGON((-0.85013 46.77618,-0.85005 46.75469,-0.81989 46.75489,-0.82029 46.77582,-0.85013 46.77618))",
 "OccurrenceStatus is Present",
 "Year 2015-2026"
 ]
}

 The dataset includes 175 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003130-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.4j0zpc8f0
title: Data from: Genetic architectures of postmating isolation and morphology of two highly diverged rockfishes (genus Sebastes)

description: Postmating isolation is thought to be an important driver of the late
 stages of speciation. However, relatively little is empirically known
 about the process compared to other isolating mechanisms that drive the
 early stages of speciation, especially in non-model organisms. We
 characterized the genetic architecture of postmating isolation between two
 rockfishes, Sebastes schlegelii and S. trivittatus, whose reproductive
 isolation is complete. We examined transmission ratio distortion (TRD)
 patterns of genetic markers in two reciprocal backcross populations.
 Markers showing either of the two types of TRD was widespread across the
 genome, with some of the distorted markers forming extensive clusters
 around the recombination coldspots. These suggest that the postmating
 isolation effectively prevents gene flow across the genome and the
 recombination landscape contributes to the genetic architecture.
 Comparisons between two backcross families and two developmental stages
 showed little similarity in the distorted markers, suggesting asymmetry
 and stage-specificity of the isolation. This may be due to hybrid
 incompatibility involving maternal factors or extrinsic selection. The
 lack of sex-ratio distortion in the mapping families suggested that
 Haldane's rule in terms of hybrid inviability does not hold.
 Additionally, QTL mapping detected significant QTLs for sex and the
 morphological traits relevant to speciation and convergence of rockfishes,
 including body coloration. Genes in the melanocortin system, including
 agouti-signaling protein 1 (asip1) and melanocortin 1 receptor (mc1r),
 might underlie the horizontal and vertical color patterns on the body,
 respectively. These findings constitute an essential step towards a
 comprehensive understanding of speciation and morphological
 diversification of rockfishes.
description: Linkage maps  were constructed using ddRAD-seq data of two
 backcross families of Sebastes trivittatus and S. schlegelii (femael F1 x
 male S. trivittatus and female F1 x male S. schlegelii). Phenotypic data
 were also obtained from the two families. More details are provided in the
 accompnying readme file and the manuscript.
description: Genotype and phenotype data and linkage maps are provided in a
 zip file.
DOI: 10.5061/dryad.79cnp5hxx
title: Supplementary data for: Maternal testosterone and offspring birth weight: A Mendelian randomization study

description: Evidence showed maternal androgen levels in both healthy/general
 population and populations with hyperandrogenic disorders were inversely
 associated with offspring’s birth weight. We aimed to investigate the
 causal effect of maternal testosterone levels in general population on
 offspring’s birth weight and preterm delivery risk using two-sample
 Mendelian randomization (MR) method. We obtained independent genetic
 instruments from a sex-specific genome wide association study with up to
 230,454 females of European descent from UK biobank. Genetic instruments
 with consistent testosterone effects but no aggregate effect on
 sex-hormone binding globulin were used to perform the main analysis.
 Summary-level data of offspring’s birth weight with offspring's
 genotype adjusted was obtained from a study with 210,406 females of
 European descent. Summary-level data of preterm delivery was obtained from
 the FinnGen study (6,736 cases and 116,219 controls). For outcome of
 offspring's birth weight, the numbers of instruments included in
 final MR analyses were 123, 128 and 91 respectively. For outcome of
 preterm delivery, the numbers were 115, 120 and 83 respectively. The
 present data files show the genetic instruments and their effects on
 exposures and outcomes. Pleiotropic genetic instruments that excluded in
 the MR analysis were also provided in the files.
description: Genetic instruments for
 testosterone were obtained from supplementary Tables of the study by Ruth
 et al (2020) (Nat Med. 2020 Feb;26(2):252-258. doi:
 10.1038/s41591-020-0751-5). Summary data on birth weight has been
 contributed by the EGG Consortium using the UK Biobank Resource and has
 been downloaded from www.egg-consortium.org. The download link for summary
 data of preterm delivery can be obtained from
 https://finngen.gitbook.io/documentation/.
DOI: 10.57707/fmi-b2share.csf0y-6w240
title: Data for the manuscript "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Ritvanen et al, submitted to IEEE JSTARS

description: This repository contains the data for the article "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Jenna Ritvanen, Bent Harnist, Miguel Aldana, Terhi Mäkinen, and Seppo Pulkkinen., submitted to IEEE JSTARS journal.

The model code used for generating the model checkpoints and nowcasts is available for the L-CNN model at https://doi.org/10.5281/zenodo.7118752 and for the RainNet model at https://doi.org/10.5281/zenodo.7118705.

## `composites`

- composites: FMI radar composites used as input data for the article. The zip file contains the composites as gzip-compressed PGM images. The metadata of the composites is written in the first 29 lines of the file. The composites are given for the 100 days used in the article with a time interval of 5 minutes, resulting in 100 * 24 * 12 = 28 800 files, sorted in directories according to year, month and day. Note that no quality control has been performed on the composites. The compressed values can be transformed to radar reflectivity with `dBZ = (data - 64.0) / 2.0`. The projection of the composites is `+proj=stere +a=6371288 +lon_0=25E +lat_0=90N +lat_ts=60 +x_0=380886.310 +y_0=3395677.920 +no_defs`

## `models`

Model checkpoints used to produce the nowcasts in the article.

- `lcnn/epoch=6-step=95480.ckpt`: L-CNN model
- `rainnet/t11-rn-logcosh-lt30.ckpt`: RainNet model

## `nowcasts`

Nowcasts used to compute verification results in the article. The subimages were from the composites with bounding box [604, 1116, 125, 637], written as [x1, x2, y1, x2] that corresponds to image[x1:x2, y1:y2] in NumPy indexing.

- `lcnn_diff_rmse_30lt_20062022_36.h5: L-CNN nowcasts`
- `p15-rn-logcosh-lt30.hdf5`: RainNet nowcasts
- `p25_extrapolation_lcnn_test_swap.hdf5`: Extrapolation nowcasts
- `p25_linda_lcnn_test_swap.hdf5`: LINDA nowcasts
- `test_obs_512.hdf5`: Observations

## `verification_results`

Verification statistic values in CSV files. The first row indicates leadtime index (i.e., `leadtime = 5 min * value`). The first column indicates statistic name and second the model.

- `CONT.csv`: continuous scores.
- `CAT.csv`: categorical scores. The statistic names follow the pattern `_`, e.g. `CSI_10_0` for CSI at 10.0 threshold.
- `FSS.csv`: categorical scores. The statistic names follow the pattern `__`, e.g. `FSS_16_10_0` for FSS at 16km scale at 10.0 threshold.
DOI: 10.5061/dryad.xgxd254jr
title: Variable relationships between trait diversity and avian ecological functions in agroecosystems

description: 1) The diversity of traits within animal assemblages has been shown to
 affect the magnitude of animal-provided ecological functions. However,
 little is known about how consistent trait diversity effects are across
 ecological functions and ecosystems. More importantly, the importance of
 trait diversity in driving ecosystem functioning, relative to other
 components of biodiversity, has rarely been assessed. It also remains
 unclear how environmental gradients filter trait diversity and,
 ultimately, modulate ecological functions.   2) Here we
 test how different biodiversity components (i.e., trait diversity,
 phylogenetic diversity and abundance) affect the magnitude of avian seed
 dispersal and insect predation along large environmental gradients. We
 sampled frugivorous and insectivorous birds and their ecological functions
 across gradients of forest cover and fruit and insect abundances in
 woodland pastures and apple orchards in Northern Spain. We measured 6
 morphological traits and compiled phylogenetic information on 43 bird
 species. We used Structural Equation Models to disentangle the effects of
 environmental gradients and biodiversity components on ecological
 functions. 3) We found that different avian functions in the same
 agroecosystem were controlled by different biodiversity components. While
 seed dispersal was positively driven by bird abundance in woodland
 pastures, insect predation responded positively to trait and phylogenetic
 diversity. The positive effects of trait diversity on insect predation
 were, on the other hand, consistent across woodland pastures and apple
 orchards. 4) Our results also pinpointed forest cover and resource
 availability as filters of the different components of avian diversity,
 suggesting that environmental gradients condition the effects of
 biodiversity on avian ecological functions. 5) Our findings reveal
 variable effects of trait diversity on two different avian ecological
 functions, but consistent effects on the same function across
 agroecosystems. Consolidating the generalities of trait diversity effects
 will require further multi-function studies, as well as a unifying
 framework for animal-driven functions that integrates the causal links
 between environmental gradients, the different biodiversity components,
 and ecological functions.  
description: Please see ReadMe file.
DOI: 10.57707/fmi-b2share.j33z3-h4z84
title: Data for the manuscript "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Ritvanen et al, submitted to IEEE JSTARS

description: This repository contains the data for the article "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Jenna Ritvanen, Bent Harnist, Miguel Aldana, Terhi Mäkinen, and Seppo Pulkkinen., submitted to IEEE JSTARS journal.

The model code used for generating the model checkpoints and nowcasts is available for the L-CNN model at https://doi.org/10.5281/zenodo.7118752 and for the RainNet model at https://doi.org/10.5281/zenodo.7118705.

## `composites`

- composites: FMI radar composites used as input data for the article. The zip file contains the composites as gzip-compressed PGM images. The metadata of the composites is written in the first 29 lines of the file. The composites are given for the 100 days used in the article with a time interval of 5 minutes, resulting in 100 * 24 * 12 = 28 800 files, sorted in directories according to year, month and day. Note that no quality control has been performed on the composites. The compressed values can be transformed to radar reflectivity with `dBZ = (data - 64.0) / 2.0`. The projection of the composites is `+proj=stere +a=6371288 +lon_0=25E +lat_0=90N +lat_ts=60 +x_0=380886.310 +y_0=3395677.920 +no_defs`

## `models`

Model checkpoints used to produce the nowcasts in the article.

- `lcnn/epoch=6-step=95480.ckpt`: L-CNN model
- `rainnet/t11-rn-logcosh-lt30.ckpt`: RainNet model

## `nowcasts`

Nowcasts used to compute verification results in the article. The subimages were from the composites with bounding box [604, 1116, 125, 637], written as [x1, x2, y1, x2] that corresponds to image[x1:x2, y1:y2] in NumPy indexing.

- `lcnn_diff_rmse_30lt_20062022_36.h5: L-CNN nowcasts`
- `p15-rn-logcosh-lt30.hdf5`: RainNet nowcasts
- `p25_extrapolation_lcnn_test_swap.hdf5`: Extrapolation nowcasts
- `p25_linda_lcnn_test_swap.hdf5`: LINDA nowcasts
- `test_obs_512.hdf5`: Observations

## `verification_results`

Verification statistic values in CSV files. The first row indicates leadtime index (i.e., `leadtime = 5 min * value`). The first column indicates statistic name and second the model.

- `CONT.csv`: continuous scores.
- `CAT.csv`: categorical scores. The statistic names follow the pattern `_`, e.g. `CSI_10_0` for CSI at 10.0 threshold.
- `FSS.csv`: categorical scores. The statistic names follow the pattern `__`, e.g. `FSS_16_10_0` for FSS at 16km scale at 10.0 threshold.
DOI: 10.57707/fmi-b2share.v921p-hck33
title: Data for the manuscript "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Ritvanen et al, submitted to IEEE JSTARS

description: This repository contains the data for the article "Advection-free Convolutional Neural Network for Convective Rainfall Nowcasting" by Jenna Ritvanen, Bent Harnist, Miguel Aldana, Terhi Mäkinen, and Seppo Pulkkinen., submitted to IEEE JSTARS journal.

The model code used for generating the model checkpoints and nowcasts is available for the L-CNN model at https://doi.org/10.5281/zenodo.7118752 and for the RainNet model at https://doi.org/10.5281/zenodo.7118705.

## `composites`

- composites: FMI radar composites used as input data for the article. The zip file contains the composites as gzip-compressed PGM images. The metadata of the composites is written in the first 29 lines of the file. The composites are given for the 100 days used in the article with a time interval of 5 minutes, resulting in 100 * 24 * 12 = 28 800 files, sorted in directories according to year, month and day. Note that no quality control has been performed on the composites. The compressed values can be transformed to radar reflectivity with `dBZ = (data - 64.0) / 2.0`. The projection of the composites is `+proj=stere +a=6371288 +lon_0=25E +lat_0=90N +lat_ts=60 +x_0=380886.310 +y_0=3395677.920 +no_defs`

## `models`

Model checkpoints used to produce the nowcasts in the article.

- `lcnn/epoch=6-step=95480.ckpt`: L-CNN model
- `rainnet/t11-rn-logcosh-lt30.ckpt`: RainNet model

## `nowcasts`

Nowcasts used to compute verification results in the article. The subimages were from the composites with bounding box [604, 1116, 125, 637], written as [x1, x2, y1, x2] that corresponds to image[x1:x2, y1:y2] in NumPy indexing.

- `lcnn_diff_rmse_30lt_20062022_36.h5: L-CNN nowcasts`
- `p15-rn-logcosh-lt30.hdf5`: RainNet nowcasts
- `p25_extrapolation_lcnn_test_swap.hdf5`: Extrapolation nowcasts
- `p25_linda_lcnn_test_swap.hdf5`: LINDA nowcasts
- `test_obs_512.hdf5`: Observations

## `verification_results`

Verification statistic values in CSV files. The first row indicates leadtime index (i.e., `leadtime = 5 min * value`). The first column indicates statistic name and second the model.

- `CONT.csv`: continuous scores.
- `CAT.csv`: categorical scores. The statistic names follow the pattern `_`, e.g. `CSI_10_0` for CSI at 10.0 threshold.
- `FSS.csv`: categorical scores. The statistic names follow the pattern `__`, e.g. `FSS_16_10_0` for FSS at 16km scale at 10.0 threshold.
DOI: 10.15468/dl.hh4hec
title: Occurrence Download

description: A dataset containing 174383152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 174383152 records from 12895 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003118-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pkvzvb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.6413 -34.1504,26.9648 -34.0826,26.9648 -25.1504,17.9648 -25.1504,17.9648 -34.1504,26.6413 -34.1504))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011879-250525065834625/datasets/export for details.
DOI: 10.15468/dl.6an2qs
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.1447 -34.2546,26.6413 -34.1504,17.9648 -34.1504,17.9648 -34.5378,18.3444 -34.8391,18.8711 -34.8843,19.4851 -35.2658,19.9376 -35.3352,20.7057 -34.9616,21.9281 -34.8692,22.6401 -34.5158,23.3842 -34.6063,23.712 -34.499,24.8223 -34.7143,25.2443 -34.5061,25.7729 -34.5304,26.1447 -34.2546))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011880-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15509074
title: BigBig Progression Formula — Datasets, Code and Reproducible Environment

description: CSV files for synthetic and real benchmarks, a minimal Jupyter demo, plus Dockerfile and Conda environment to reproduce all results in the paper “BigBig Progression Formula: Formal Axioms, Chaos Boundary, and Cross-Domain Validation.”

For reproducibility scripts, data access, and contact:

- 📧 hello@onestardao.com  

- 🌐 https://onestardao.com  

- 📚 All Papers: https://onestardao.com/papers/
DOI: 10.5281/zenodo.15509877
title: BigBig Progression Formula — Datasets, Code and Reproducible Environment

description: CSV files for synthetic and real benchmarks, a minimal Jupyter demo, plus Dockerfile and Conda environment to reproduce all results in the paper “BigBig Progression Formula: Formal Axioms, Chaos Boundary, and Cross-Domain Validation.”

For reproducibility scripts, data access, and contact:

- 📧 hello@onestardao.com  

- 🌐 https://onestardao.com  

- 📚 All Papers: https://onestardao.com/papers/
DOI: 10.15468/dl.bpqyxv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.9648 -7.1504,17.9648 -7.1504,17.9648 -16.1504,26.9648 -16.1504,26.9648 -7.1504))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011881-250525065834625/datasets/export for details.
DOI: 10.15468/dl.6eavxf
title: Occurrence Download

description: A dataset containing 19489 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Vleermuiskasten.nl - Bats in bat boxes in The Netherlands and Belgium"
 ]
}

 The dataset includes 19489 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011882-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8nmjx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.9648 -16.1504,17.9648 -16.1504,17.9648 -25.1504,26.9648 -25.1504,26.9648 -16.1504))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011883-250525065834625/datasets/export for details.
DOI: 10.15468/dl.uhkga8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Talismania Goode & Bean, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.9648 10.8496,17.9648 10.8496,17.9648 1.8496,26.9648 1.8496,26.9648 10.8496))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011884-250525065834625/datasets/export for details.