cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.qz612jmnf
title: Overcoming the congenitally disadvantageous mutation through adaptation to environmental UV exposure in land snails

description: Congenital fitness-disadvantageous mutations are not maintained in the
 population; they are purged from the population through processes such as
 purifying selection. However, these mutations could persist in the
 population as polymorphisms when it is advantageous for the individuals
 carrying them to adapt to a specific external environment. We tested this
 hypothesis using the dimorphic land snail Euhadra peliomphala simodae in
 Japan; these snails have dark or bright-coloured shells. The survival rate
 of dark snails at hatching was lower than that of the bright ones, as
 observed in the F1 progenies produced through crossing. Dark snails have a
 congenital fitness-disadvantageous mutation; however, they also have
 protection against ultraviolet radiation. They have a higher survival rate
 than the bright snails in a UV environment, as observed using the UV
 exposure experiments and UV transmittance measurements. This is a good
 example of a congenitally disadvantageous mutation that is advantageous
 for adapting to the external environment. These results explain the
 maintenance of polymorphism and highlight the genotypic and phenotypic
 diversity in the wild population.
description: # Overcoming the congenitally disadvantageous mutation through adaptation
 to environmental UV exposure in land snails
 [https://doi.org/10.5061/dryad.qz612jmnf](https://doi.org/10.5061/dryad.qz612jmnf) ## File list 1. 2.1.Crossing.csv – The data file of the crossing experiment in section 2.1. * Categories of mating pair (Dark-Dark(BB) or Bright-Bright(ww) * Individual status (alive (1) or dead (2)) * Passed days 2. 2.2.Thermal\_exposure.csv – The data file of the thermal exposure experiment is in section 2.2. * Individual ID * Increasing rate of inner temperature * Decreasing rate of inner temperature * Shell colour values (standardised luminance) 3. 2.3.UV\_exposure.csv – The data file of the ultraviolet exposure experiment in section 2.3. * Individual ID * Experiment conditions (UV condition or control) * Individual status (alive (0) or dead (1)) * Passed days * Shell colour values (standardised luminance) 4. 2.4.UV\_transmittance.csv – The data file of the measurements of ultraviolet transmittance in section 2.4. * Individual ID * Shell colour values (standardised luminance) * UV transmittance value (value is from 0 to 1.0) The way to calculate the shell colour values is described in Ito & Konuma 2020 *Biol. J. Linn. Soc.* (DOI: 10.1093/biolinnean/blz168).
DOI: 10.5061/dryad.cfxpnvxcj
title: Monitoring wildlife population trends with sample counts: A case study on the Alpine ibex (Capra ibex)

description: Monitoring population dynamics is of fundamental importance in
 conservation but assessing trends in abundance can be costly, especially
 in large and rough areas. Obtaining trend estimations from counts
 performed in only a portion of the total area (sample counts) can be a
 cost-effective method to improve the monitoring and conservation of
 species difficult to count. We tested the effectiveness of sample counts
 in monitoring population trends of wild animals, using as a model
 population the Alpine ibex (Capra ibex) in the Gran Paradiso National Park
 (Italy), both with computer simulations and using historical count data
 collected over the last 65 years. Despite sample counts failed to
 correctly estimate the true population abundance, sampling half of the
 target area could reliably monitor the trend of the target population. In
 case of strong changes in abundance, an even lower proportion of the total
 area could be sufficient to identify the direction of the population
 trend. However, when there is a high yearly trend variability, the
 required number of samples increases and even counting in the entire area
 can be ineffective to detect population trends. The effect of other
 parameters, such as which portion of the area is sampled and
 detectability, was lower, but these should be tested case by case. Sample
 counts could therefore constitute a viable alternative to assess
 population trends, allowing for important, cost-effective improvements in
 the monitoring of wild animals of conservation interest.
description: We here provide the R script to run all the simulations in the
 paper. See Methods and Supplementary materials S1 and S2 for more
 info
description: # Monitoring wildlife population trends with sample counts: A case study
 on the Alpine ibex (Capra ibex)
 [https://doi.org/10.5061/dryad.cfxpnvxcj](https://doi.org/10.5061/dryad.cfxpnvxcj) This dataset contain the R script necessary to run all the simulations presented in the paper. More details can be found in Methods section and Supplementary materials S1 and S2 ## Description of the data and file structure samplecounts\_ibex.R is the R script to run the simulations GPNP sectors area contain the extent of each of the 38 sectors in Gran Paradiso National Park, used to run our simulations (see methods) ## Sharing/Access information Data was derived from the following sources: \- script used to run the simulations \(provided\) \- area of each GPNP sectior \(provided\) \- census data \(provided in the script as an example\) ## Code/Software Simulations presented here were run on R, version 4.2.3 Packages required are "lme4", "purrr" ,"doParallel", "dplyr", "scales", "truncnorm"
DOI: 10.5061/dryad.1rn8pk10w
title: Data from: Determinants of hyena participation in risky collective action

description: Collective action problems arise when cooperating individuals suffer costs
 of cooperation, while the benefits of cooperation are received by both
 cooperators and defectors. We address this problem using data from spotted
 hyenas fighting with lions. Lions are much larger, and kill many hyenas,
 so these fights require cooperative mobbing by hyenas for them to succeed.
 We identify factors that predict: when hyena groups engage in cooperative
 fights with lions, which individuals choose to participate, and how the
 benefits of victory are distributed among cooperators and non-cooperators.
 We find that cooperative mobbing is better predicted by lower costs (no
 male lions, more hyenas) than higher benefits (need for food). Individual
 participation is facilitated by social factors, both over the long term
 (close kin, social bond strength) and the short term (greeting
 interactions prior to cooperation). Finally, we find some direct benefits
 of participation: after cooperation, participants were more likely to feed
 at contested carcasses than non-participants. Overall, these results are
 consistent with the hypothesis that, when animals face dangerous
 cooperative dilemmas, selection favors flexible strategies that are
 sensitive to dynamic factors emerging over multiple time-scales.
description: # Determinants of hyena participation in risky collective action Data and
 code associated with Proc B manuscript *Determinants of hyena
 participation in risky collective action. doi: 10.1098/rspb.2023.1390*
 Here we study cooperative mobbing behavior by spotted hyenas during fights
 with lions. We use long-term observational data from the Mara Hyena
 Project to identify factors that predict: (1) when hyena groups engage in
 cooperative fights with lions, (2) which individuals choose to
 participate, and (3) how the benefits of victory are distributed among
 cooperators and non-cooperators. Dryad doi:
 [https://doi.org/10.5061/dryad.1rn8pk10w](https://doi.org/10.5061/dryad.1rn8pk10w) ## Description of the data and file structure All data and code are numbered for ease of use, and users should sort by file name to achieve the correct ordering. Raw data are loaded into R and preliminarily cleaned in R script 01. Data are then reshaped in R scripts 03-07, and output is saved in Rdata files 04-08. Models in R script 09 address question (1) when hyena groups engage in cooperative fights with lions. Data are again reshaped in R script 11, and output is saved in Rdata file 12. Models in R scripts 13-16 address question (2) which individuals choose to participate in cooperative fights with lions. Data are again reshaped in R scripts 21 and 24, and output is saved in Rdata files 22 and 25. Models in R scripts 23 and 26 address question (3) how the benefits of victory are distributed among cooperators and non-cooperators. ## Sharing/Access information Data and code can also be found at GitHub: https://github.com/tracymont/hyena\_mobbing/ ## Code/Software All coding was done in R.
DOI: 10.15468/dl.2tswym
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Algeria, Tunisia, Morocco)",
 "TaxonKey is Lemniscomys barbarus",
 "Year 1970-2026"
 ]
}

 The dataset includes 101 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003720-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.73n5tb33h
title: Knime workflow and data files to calculate a lead-likeness evaluation function 'Λ' for small molecule compounds

description: A simple and rational method to rank molecules’ lead-likeness using
 continuous evaluation functions was developed to support epigenetic drug
 discovery. This strategy proved to be highly effective on model chemical
 libraries and finally helped drive synthetic efforts towards candidates of
 interest for epigenetic applications towards HDAC6, BRD4, and EZH2. Here
 we provide sample data and the knime workflow for generating lead-like
 chemical probes for epigenetics. This tool and example data set will
 enable researchers to establish this workflow in their own laboratories
 and apply it to new synthetic scaffolds for medicinal chemistry.
description: # Knime workflow and data files Contains: Chemical Structures in Chemdraw
 Format (.cdxml)and SDF format for: Amino Acids HDAC Fragments Isoxazole
 Fragment Pyridone Fragment Docking Score Files for: DKP Library against
 BRD4 (Fred and hybrid) DKP Library against EZH2 (Fred and hybrid) DKP
 Library against HDAC6 (Fred and hybrid) 3D structures of DKP Library for
 PMI Plot generation Knime workflow for scoring functions. Usage: Install
 Knime and open workflow Install any additional plugins required Direct
 workflow to files provided above.
DOI: 10.15468/dl.2gks92
title: Occurrence Download

description: A dataset containing 9146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dryobates minor",
 "HasCoordinate is true",
 "Country is Sweden",
 "Year is 2025",
 {
 "or" : [
 "Month is March",
 "Month is April",
 "Month is May"
 ]
 }
 ]
}

 The dataset includes 9146 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003721-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.82294/dvnk2osmo
title: Triangulación y unidades de análisis. Caminos para construir las narrativas en las prácticas educativas

description: Esta es una estrategia para la lectura de los testimonios de los educadores artísticos involucrados en el proyecto de formación, como para la construcción de nuevas rutas de análisis de los futuros proyectos. Pues, al ser tan amplios los datos testimoniales, se construyó una propuesta donde ser agruparan códigos basados en una narrativa fenomenológica particular, brindando una interrelación de testimonios y permitiendo una lectura precisa sobre los mismos. En esta herramienta encontrarán algunas citas que constituyen las categorías y los códigos, con la selección de las mismas, para luego identificar su ruta en los códigos Sankey que se encuentran en el Dataverse de Visualizaciones, y luego identificar los testimonios en el Administrador de citas testimoniales (entrevistas procesadas).
DOI: 10.5281/zenodo.17965436
title: Data Associated with "Physical and Chemical Characterization of GY 91's Multi-ringed Protostellar Disk with ALMA"

description: Continuum 

GY91_combined_cont_selfcal_7a_pbcor.fits - Spectral Setting 7a continuum image 

GY91_combined_cont_selfcal_7b_pbcor.fits - Spectral Setting 7b continuum image 

 GY91_hirescontinuum.image.fits - high resolution B6 continuum image from observations originally published in Cieza et al. 2021 

Image cubes - 

CS_channelmap_pbcor.fits - pb-corrected CS image cubes

N2Hp_channelmap_taper1_pbcor.fits - tapered pb-corrected N2H+ image cubes

H2CO_290_channelmap_pbcor.fits - pb-corrected H2CO 4(0,4)-3(0,3) image cubes

H2CO_291_channelmap_taper1_pbcor.fits - tapered pb-corrected H2CO 4(2,3)-3(2,2) image cubes

H2CS_channelmap_taper1_pbcor.fits - tapered pb-corrected H2CS image cubes

C18O_channelmap_pbcor.fits - pb-corrected C18O image cubes

13C18O_channelmap_taper1_pbcor.fits - tapered pb-corrected 13C18O image cubes

Moment Maps

CS_moment0.fits - CS moment 0 map

N2Hp_moment0_taper1.fits - tapered N2H+ moment 0 map

H2CO_290_masked_moment0.fits - masked H2CO 4(0,4)-3(0,3) moment 0 map

H2CO_291_moment0_taper1.fits - tapered H2CO 4(2,3)-3(2,2) moment 0 map

H2CS_moment0_taper1.fits - tapered H2CS moment 0 map

C18O_masked_moment0.fits - masked C18O moment 0 map

13C18O_moment0_taper1.fits - tapered 13C18O moment 0 map

--------------------

CS_moment1.fits - CS moment 1 map

N2Hp_moment1_taper1.fits - tapered N2H+ moment 1 map

H2CO_290_moment1_taper1.fits - tapered H2CO 4(0,4)-3(0,3) moment 1 map

H2CS_moment1_taper1.fits - tapered H2CS moment 0 map

C18O_moment1.fits - C18O moment 1 map

Measurement sets

GY91_7a_combined_obs_LINE.ms.tar - measurement sets for each LINE in spectral setting 7a (lines are all binned by 30s) 

GY91_7b_combined_obs_LINE.ms.tar - measurement sets for each LINE in spectral setting 7b (C18O and 13C18O binned by 30s; only a subset of channels from the SPWs are included due to large data volume)

GY91_7a_combined_cont_obs_datacorr_a1.ms.tar - self-calibrated continuum measurement set for the 7a spectral setting continuum 

GY91_7b_combined_cont_obs_datacorr_a1.ms.tar - self-calibrated continuum measurement set for the 7b spectral setting continuum 

GY91_hirescontinuum.ms.tar - high resolution B6 continuum measurement set from from observations originally published in Cieza et al. 2021 

Keplerian Masks

H2CO_290_KeplerianMask.fits - Keplerian mask used to generate moment 0 map for H2CO 4(0,4)-3(0,3) 

C18O_KeplerianMask.fits - Keplerian mask used to generate moment 0 map for C18O

MCFOST Parameter Files/Outputs 

OpticalConstants.tar - optical constants used in MCFOST models 

M_mass.tar - input and output files for MCFOST model with mass (39, 44, 49 earth masses) 

Scripts

Band7a_reduction.py - reduction scripts for continuum and line in spectral setting 7a

Band7b_reduction.py - reduction scripts for continuum and line in spectral setting 7b

GY91hirescontinuumselfcal.py - CASA script for generating high-resolution B6 continuum image from observations originally published in Cieza et al. 2021
DOI: 10.5281/zenodo.7991429
title: Interplanetary shock data base

description: This interplanetary shock data base was compiled with Wind, Advanced Composition Explorer (ACE), and Deep Space Climate Observatory (DSCOVR) observations collected at the Lagrangian point L1. The list ranges from January 1995 to December 2024 with 650 events. Many shock parameters are included, such as shock impact angle, shock speed, Mach numbers, compression ratios, and IMF (interplanetary magnetic field) Bz in the upstream and downstream regions. The list also brings geomagnetic activity information such as minimum SMR values in a time interval of 2 hours after shock impact. The author intends to update this list annually.

There are three files: (i) full_shock_list_2024.txt, a text file with  650 events; (ii) full_shock_params.cdf, a cdf file with detailed information about each specific shock events; and (iii) read_shock.py, a file that contains a short python routine to read information about a specific shock event. The SpacePy package (https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.) is required to extract shock information from the cdf file.

Example (shock number 142, 26 June 2000):

from read_shock import read_shock_cdf

read_shock_cdf(142)

------------------------------------------------------------------------------------sn     date     UTS  UTM142 2000 06 23 1226  1226Spacecraft is acPosition: X = 239.9 Re; Y =  36.7 Re; Z =  -0.7 Re

Time windowsUpstream:    5 to 10 minutes before shockDownstream:  5 to 10 minutes after shock

Solar wind plasma and IMF                             Bx        By      Bz        Vx              Vy       Vz          N           TUpstream        5.448  -3.946  -4.568   -399.958  18.948 -14.969     6.941    99731.6Downstream  14.317  -1.766 -16.125  -508.734  37.628 -117.578 17.429  224496.1

Computed parameters dp1   dp2   Xdp   Xb    Xn     vs_rh    vA      cs1.864 7.989 4.286 2.661 2.511 604.778  67.317  52.384

Minimum SMR index in the 2-hour window following shock impact:  -9.60 nT

        nx      ny     nz    thxn    phiyn    thbn     vs       vfms    Ma     MsMC    -0.323 -0.944  0.070 108.831  175.779  78.309  127.332   85.704  0.255  0.200MX1   -0.796  0.191 -0.575 142.729  -71.590  72.348  578.268   86.195  3.681  2.874MX2   -0.798  0.133 -0.587 142.962  -77.223  74.365  579.175   86.010  3.693  2.890MX3   -0.798  0.107 -0.592 142.980  -79.739  75.273  578.920   85.933  3.694  2.894VC    -0.722  0.124 -0.681 136.205  -79.682  80.717  551.670   85.556  3.720  2.927

Type cdf['SHOCK'].attrs for a description of all shock variables and parameters.

More details about this list and methods for shock normal calculations can be found in:

Oliveira, D. M. (2023). Interplanetary Shock Data Base. Frontiers in    Astronomy and Space Science. (Under review)
DOI: 10.5281/zenodo.7991430
title: Interplanetary shock data base

description: Please click on the link "version 2" on the right to obtain the data.

This interplanetary shock data base was compiled with Wind, Advanced Composition Explorer (ACE), and Deep Space Climate Observatory (DSCOVR) observations collected at the Lagrangian point L1. The list ranges from January 1995 to December 2024 with 650 events. Many shock parameters are included, such as shock impact angle, shock speed, Mach numbers, compression ratios, and IMF (interplanetary magnetic field) Bz in the upstream and downstream regions. The list also brings geomagnetic activity information such as minimum SMR values in a time interval of 2 hours after shock impact. The author intends to update this list annually.

There are three files: (i) full_shock_list_2024.txt, a text file with  650 events; (ii) full_shock_params.cdf, a cdf file with detailed information about each specific shock events; and (iii) read_shock.py, a file that contains a short python routine to read information about a specific shock event. The SpacePy package (https://spacepy.github.io/spacepy.html#:~:text=SpacePy%3A%20Space%20Science%20Tools%20for,at%20the%20space%20science%20community.) is required to extract shock information from the cdf file.

Example (shock number 142, 26 June 2000):

from read_shock import read_shock_cdf

read_shock_cdf(142)

------------------------------------------------------------------------------------sn     date     UTS  UTM142 2000 06 23 1226  1226Spacecraft is acPosition: X = 239.9 Re; Y =  36.7 Re; Z =  -0.7 Re

Time windowsUpstream:    5 to 10 minutes before shockDownstream:  5 to 10 minutes after shock

Solar wind plasma and IMF                             Bx        By      Bz        Vx              Vy       Vz          N           TUpstream        5.448  -3.946  -4.568   -399.958  18.948 -14.969     6.941    99731.6Downstream  14.317  -1.766 -16.125  -508.734  37.628 -117.578 17.429  224496.1

Computed parameters dp1   dp2   Xdp   Xb    Xn     vs_rh    vA      cs1.864 7.989 4.286 2.661 2.511 604.778  67.317  52.384

Minimum SMR index in the 2-hour window following shock impact:  -9.60 nT

        nx      ny     nz    thxn    phiyn    thbn     vs       vfms    Ma     MsMC    -0.323 -0.944  0.070 108.831  175.779  78.309  127.332   85.704  0.255  0.200MX1   -0.796  0.191 -0.575 142.729  -71.590  72.348  578.268   86.195  3.681  2.874MX2   -0.798  0.133 -0.587 142.962  -77.223  74.365  579.175   86.010  3.693  2.890MX3   -0.798  0.107 -0.592 142.980  -79.739  75.273  578.920   85.933  3.694  2.894VC    -0.722  0.124 -0.681 136.205  -79.682  80.717  551.670   85.556  3.720  2.927

Type cdf['SHOCK'].attrs for a description of all shock variables and parameters.

More details about this list and methods for shock normal calculations can be found in:

Oliveira, D. M. (2023). Interplanetary Shock Data Base. Frontiers in    Astronomy and Space Science. (Under review)
DOI: 10.5281/zenodo.17965437
title: Data Associated with "Physical and Chemical Characterization of GY 91's Multi-ringed Protostellar Disk with ALMA"

description: Continuum 

GY91_combined_cont_selfcal_7a_pbcor.fits - Spectral Setting 7a continuum image 

GY91_combined_cont_selfcal_7b_pbcor.fits - Spectral Setting 7b continuum image 

 GY91_hirescontinuum.image.fits - high resolution B6 continuum image from observations originally published in Cieza et al. 2021 

Image cubes - 

CS_channelmap_pbcor.fits - pb-corrected CS image cubes

N2Hp_channelmap_taper1_pbcor.fits - tapered pb-corrected N2H+ image cubes

H2CO_290_channelmap_pbcor.fits - pb-corrected H2CO 4(0,4)-3(0,3) image cubes

H2CO_291_channelmap_taper1_pbcor.fits - tapered pb-corrected H2CO 4(2,3)-3(2,2) image cubes

H2CS_channelmap_taper1_pbcor.fits - tapered pb-corrected H2CS image cubes

C18O_channelmap_pbcor.fits - pb-corrected C18O image cubes

13C18O_channelmap_taper1_pbcor.fits - tapered pb-corrected 13C18O image cubes

Moment Maps

CS_moment0.fits - CS moment 0 map

N2Hp_moment0_taper1.fits - tapered N2H+ moment 0 map

H2CO_290_masked_moment0.fits - masked H2CO 4(0,4)-3(0,3) moment 0 map

H2CO_291_moment0_taper1.fits - tapered H2CO 4(2,3)-3(2,2) moment 0 map

H2CS_moment0_taper1.fits - tapered H2CS moment 0 map

C18O_masked_moment0.fits - masked C18O moment 0 map

13C18O_moment0_taper1.fits - tapered 13C18O moment 0 map

--------------------

CS_moment1.fits - CS moment 1 map

N2Hp_moment1_taper1.fits - tapered N2H+ moment 1 map

H2CO_290_moment1_taper1.fits - tapered H2CO 4(0,4)-3(0,3) moment 1 map

H2CS_moment1_taper1.fits - tapered H2CS moment 0 map

C18O_moment1.fits - C18O moment 1 map

Measurement sets

GY91_7a_combined_obs_LINE.ms.tar - measurement sets for each LINE in spectral setting 7a (lines are all binned by 30s) 

GY91_7b_combined_obs_LINE.ms.tar - measurement sets for each LINE in spectral setting 7b (C18O and 13C18O binned by 30s; only a subset of channels from the SPWs are included due to large data volume)

GY91_7a_combined_cont_obs_datacorr_a1.ms.tar - self-calibrated continuum measurement set for the 7a spectral setting continuum 

GY91_7b_combined_cont_obs_datacorr_a1.ms.tar - self-calibrated continuum measurement set for the 7b spectral setting continuum 

GY91_hirescontinuum.ms.tar - high resolution B6 continuum measurement set from from observations originally published in Cieza et al. 2021 

Keplerian Masks

H2CO_290_KeplerianMask.fits - Keplerian mask used to generate moment 0 map for H2CO 4(0,4)-3(0,3) 

C18O_KeplerianMask.fits - Keplerian mask used to generate moment 0 map for C18O

MCFOST Parameter Files/Outputs 

OpticalConstants.tar - optical constants used in MCFOST models 

M_mass.tar - input and output files for MCFOST model with mass (39, 44, 49 earth masses) 

Scripts

Band7a_reduction.py - reduction scripts for continuum and line in spectral setting 7a

Band7b_reduction.py - reduction scripts for continuum and line in spectral setting 7b

GY91hirescontinuumselfcal.py - CASA script for generating high-resolution B6 continuum image from observations originally published in Cieza et al. 2021