cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nsrqdd
title: Occurrence Download

description: A dataset containing 99324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.02716 48.80232,-77.94906 48.80232,-77.94906 49.77903,-80.02716 49.77903,-80.02716 48.80232))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 99324 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005360-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5ms3kd12
title: MassIVE MSV000096557 - Mayaro viral protein interactome in infected cells: input
title: HEK293T cells were electroporated with FLAG-tagged viral protein baits, recovered for 48 h, and infected with Mayaro virus (MOI = 1). 10 h.p.i., cells were lysed and subjected to anti-FLAG automated co-immunoprecipitation and on-bead digestion. Peptides were analyzed using data-independent acquisition
DOI: 10.25400/lincolnuninz.27644580
title: LOP allele sequence generated from the MaSuRCA genome assembly of PH70 Pilosella piloselloides var praealta

description: This contains three documents. The first is the clean sequence of the LOP allele sequence generated from the MaSuRCA genome assembly that was used in the Msc thesis: An Investigation into the Genetic Forces Shaping the LOP Locus of Pilosella Piloselloides var praealta.
The second and third are the annotated version of this reference.
One annotated LOP reference sequence is annotated with:

 Gene annotation generated using BRAKER
 Transposon annotation generated using EDTA
 RNA-seq data aligned to the LOP allele sequence
 EMS mutant variants generated from the GatK variant calling pipeline

The second annotated LOP reference is annotated with:

 Gene annotation generated using BRAKER
 Transposon annotation generated using EDTA
 Transcriptome (Trinity assembly) aligned to the LOP allele sequence
 EMS mutant variants generated from the GatK variant calling pipeline
DOI: 10.25400/lincolnuninz.27644580.v1
title: LOP allele sequence generated from the MaSuRCA genome assembly of PH70 Pilosella piloselloides var praealta

description: This contains three documents. The first is the clean sequence of the LOP allele sequence generated from the MaSuRCA genome assembly that was used in the Msc thesis: An Investigation into the Genetic Forces Shaping the LOP Locus of Pilosella Piloselloides var praealta.
The second and third are the annotated version of this reference.
One annotated LOP reference sequence is annotated with:

 Gene annotation generated using BRAKER
 Transposon annotation generated using EDTA
 RNA-seq data aligned to the LOP allele sequence
 EMS mutant variants generated from the GatK variant calling pipeline

The second annotated LOP reference is annotated with:

 Gene annotation generated using BRAKER
 Transposon annotation generated using EDTA
 Transcriptome (Trinity assembly) aligned to the LOP allele sequence
 EMS mutant variants generated from the GatK variant calling pipeline
DOI: 10.15468/dl.ftgr8v
title: Occurrence Download

description: A dataset containing 1385 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Regeneración, crecimiento y dinámica poblacional del pino azul Pinus maximartinezii Rzedowski"
 ]
}

 The dataset includes 1385 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005362-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26180/27886818
title: Metadata, data, and all R code for replicating the analyses in the paper: Butterworth et al. 2025: The sicker sex is plastic: Sex specific plasticity determines sex biases in pathogen transmission

description: Sex differences are predicted to play an important role in the spread and evolution of pathogens. However, attempts to generalize the ‘sicker’ sex are often challenged by intraspecific variability of sex-biases across the infection process. Sex specific plasticity provides a framework to resolve this by elucidating how infection is shaped at the sex, pathogen, environment interface. Using the<i> Daphnia magna</i> and <i>Pasteuria ramosa </i>system, we measure infection outcomes for males and females across three temperatures and seven pathogen densities to quantify how sex specific plasticity shapes susceptibility, pathogen loads, and ultimately transmission. We find unique forms of plasticity at each stage of infection – including equivalent, sex-specific, and divergent plasticity. Integrating these into a single estimate of transmission reveals a clear pattern – male-biased transmission at cold temperatures, and female-biased transmission at warm temperatures. Sex specific thermal plasticity thus determines the ‘sicker’ sex, with implications for pathogen spread and evolution in a warming world.
DOI: 10.26180/27886818.v2
title: Metadata, data, and all R code for replicating the analyses in the paper: Butterworth et al. 2025: The sicker sex is plastic: Sex specific plasticity determines sex biases in pathogen transmission

description: Sex differences are predicted to play an important role in the spread and evolution of pathogens. However, attempts to generalize the ‘sicker’ sex are often challenged by intraspecific variability of sex-biases across the infection process. Sex specific plasticity provides a framework to resolve this by elucidating how infection is shaped at the sex, pathogen, environment interface. Using the<i> Daphnia magna</i> and <i>Pasteuria ramosa </i>system, we measure infection outcomes for males and females across three temperatures and seven pathogen densities to quantify how sex specific plasticity shapes susceptibility, pathogen loads, and ultimately transmission. We find unique forms of plasticity at each stage of infection – including equivalent, sex-specific, and divergent plasticity. Integrating these into a single estimate of transmission reveals a clear pattern – male-biased transmission at cold temperatures, and female-biased transmission at warm temperatures. Sex specific thermal plasticity thus determines the ‘sicker’ sex, with implications for pathogen spread and evolution in a warming world.
DOI: 10.15468/dl.ncfe72
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dreissena rostriformis (Deshayes, 1838)",
 "Country is Canada",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005363-241126133413365/datasets/export for details.
DOI: 10.15468/dl.geqxft
title: Occurrence Download

description: A dataset containing 59 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Drosophila suzukii (Matsumura, 1931)",
 "Country is Italy",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 59 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005364-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/srx5-ft24
title: Parkes observations for project P1238 semester 2024OCTS_01

description: Young and energetic pulsars can power strong pulsar wind nebula (PWN), which can be observed from radio to X-ray and beyond. Leveraging the sensitivity and high-resolution of next-generation radio surveys like ASKAP EMU and SARAO MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS), we are now able to identify pulsar candidates associated with PWNe and SNRs. We can then use these PWN and SNR associations to guide our search of radio pulsars. This strategy was demonstrated successful by our recent discoveries of two high dispersion measure (DM) pulsars powering a bow-shocked PWNe using ASKAP EMU radio continuum images. Recently, we analysed several SMGPS fields and identified seven PWNe candidates potentially associated to three new and four known Galactic SNRs. Here we propose to use the Parkes UWL receiver to carry out the targeted search of radio pulsars powering these PWNe. Since these SNRs are located in Galactic plane and can be distant, we expect them to have large DMs. Such high DMs will lead to strong scattering and smearing and make pulsars undetectable in previous pulsar surveys. The wide frequency coverage of UWL, especially the high frequencies, will allow us to avoid these effects and therefore offer us a better chance to find these pulsars.
DOI: 10.15468/dl.f3y9cj
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gentianella weberbaueri (Gilg) Fabris"
 ]
}

 The dataset includes 61 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005365-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2njybz
title: Occurrence Download

description: A dataset containing 824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Drosophila suzukii (Matsumura, 1931)",
 "Country is United States of America",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 824 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005366-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tjgdyc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plantae)",
 "HasCoordinate is one of (true)",
 "Geometry POLYGON((-74.759656463528 7.33905520418513,-74.6959314020006 7.33905520418513,-74.6959314020006 7.39337906400576,-74.759656463528 7.39337906400576,-74.759656463528 7.33905520418513))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005367-241126133413365/datasets/export for details.
DOI: 10.5281/zenodo.10896060
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-03-29

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.10963132
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-04-11

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.11529406
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-06-08

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.13731044
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-09-07

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.13864260
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-09-30

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.13989040
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-10-24

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.14020492
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-10-31

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.14115161
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-11-12

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.14172061
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-11-15

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.14219362
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-11-25

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.10909990
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-04-02

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.11215242
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-05-18

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.11307468
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-05-25

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.11645730
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-06-13

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.12556905
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-06-26

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.12601862
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-06-30

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.5281/zenodo.12646336
title: 360-info/tracker-seaice: Daily sea ice extent: v2024-07-03

description: Tracks the daily sea ice extent for the Arctic Circle and Antarctica using the NSIDC's Sea Ice Index dataset, as well as pre-calculating several useful measures: historical inter-quartile range across the year, the previous lowest year and the previous year.
DOI: 10.13140/rg.2.1.4430.9926
title: Books. Sergei A. Ostroumov. Ecology, Environmental sciences, protection of environment, conservation, ecotoxicology, environmental safety, with pictures; https://www.researchgate.net/publication/275965296;
DOI: 10.15468/dl.ydw8mu
title: Occurrence Download

description: A dataset containing 1406412 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 1406412 records from 1466 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058195-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fz4s4e
title: Occurrence Download

description: A dataset containing 101743 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Charles Maurer Herbarium at Colorado State University"
 ]
}

 The dataset includes 101743 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006536-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14959634
title: Dataset for "Measurement-Induced Crossover of Quantum Jump Statistics in Postselection-Free Many-Body Dynamics"

description: Dataset for the Python language.

The "np" stands for the abbreviation for "numpy".

The folder "Variance" includes the two-dimensional array for the set [SFQJ, variance in the total system] for the measurement strengths \gamma=0.05, 0.1, ..., 1.

If you load one of them like

$ variance_array_8=np.load("variance_8.npy")

and print it as

$ variance_array_8[:, 0]

you can obtain SFQJ for \gamma=0.05, 0.1,..., 1 for L=8, and the same for the variance in the total system as

$ variance_array_8[:, 1]

In the folder "singlesite" inside "Variance", you can obtain the variance for a single site (e.g., for L=8) as

$ variance_array_8=np.load("variance_8.npy")[:, 0]

The folder "LiouvillianGap" includes the two-dimensional array for the Liouvillian gap.

You can access them by loading the files as



#### gamma = [0.05, 0.1, ..., 1] ####

physical_quantities_2D_8=np.load("physical_quantities_2D_8.npy")[:, 0]

physical_quantities_2D_10=np.load("physical_quantities_2D_10.npy")[:, 0]

physical_quantities_2D_12=np.load("physical_quantities_2D_12.npy")[:, 0]

#### gamma = [0.5, 0.55, ..., 1] (γ is different from the other data due to the convergence problem) #### 

physical_quantities_2D_14=np.load("physical_quantities_2D_14.npy")[:, 0]


for the Heisenberg model.

For the XX model, you should load them as



#### gamma = [0.05, 0.1, ..., 1] ####

physical_quantities_2D_10=np.load("physical_quantities_2D_10.npy")[:, 0]
#### gamma = [0.5, 0.55, ..., 1] (γ is different from the other data due to the convergence problem) ####

physical_quantities_2D_8=np.load("physical_quantities_2D_8.npy")[:, 0]

physical_quantities_2D_12=np.load("physical_quantities_2D_12.npy")[:, 0]

physical_quantities_2D_14=np.load("physical_quantities_2D_14.npy")[:, 0]



instead.

The folder "Autocorrelation" includes the two-dimensional array for the integrated autocorrelation function.

If you load one of them like

$ physical_quantities_2D_4=np.load("physical_quantities_2D_4.npy")

and print it as

$ physical_quantities_2D_4[:, 0]

you can obtain the half-chain integrated autocorrelation functions for \gamma=0.05, 0.1,..., 1 for L=4, and the same for that in the single site as

$ physical_quantities_2D_4[:, 1]
DOI: 10.15468/dl.h57z8q
title: Occurrence Download

description: A dataset containing 1103330477 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is United States of America"
 ]
}

 The dataset includes 1103330477 records from 5172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006440-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/icpsr04199.v1
title: Uniform Crime Reporting Program Data [United States]: Offenses Known and Clearances by Arrest, 1971
title: Version 1

description: Since 1930, the Federal Bureau of Investigation has
 compiled the Uniform Crime Reports (UCR) to serve as periodic
 nationwide assessments of reported crimes not available elsewhere in
 the criminal justice system. Law enforcement agencies contribute
 reports either directly or through their state reporting programs.
 Each year, summary data are reported in four types of files: (1)
 Offenses Known and Clearances by Arrest, (2) Property Stolen and
 Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police
 Employee (LEOKA) Data. The Offenses Known and Clearances by Arrest
 data files include monthly data on the number of Crime Index offenses
 reported and the number of offenses cleared by arrest or other
 means. The counts include all reports of Index crimes (excluding
 arson) received from victims, officers who discovered infractions, or
other sources.
description: inap.
description: Datasets: 
DS1: Uniform Crime Reporting Program Data [United States]: Offenses Known and Clearances by Arrest, 1971
description: Index crimes reported by law enforcement agencies in the
United States.
description: self-enumerated formsOffenses Known and Clearances by Arrest data for the
 years 1960-1974 have been released with a separate study number for
 each year. From 1975-1997 all UCR data can be found in UNIFORM CRIME
 REPORTING PROGRAM DATA: [UNITED STATES] (ICPSR 9028). Starting with
 the year 1998, each of the four types of UCR summary data archived by
ICPSR is released as a separate study under its own study number.
DOI: 10.5281/zenodo.15746379
title: Shared data and scripts to support submitted manuscript "Influences of eastern Pacific and central Pacific El Niño events on the Indian summer monsoon: an assessment using the Met Office AGCM" to JGR-Atmosphere

description: This dataset contains all the data and scripts needed to reproduce the main figures in the manuscript. All data are preprocessed from the MetUM GA7.1 simulations using NCL. Note that these files are to support the article submitted to JGR-A, and may be edited under peer-review processes. Any inquiries go to henryzhenliu@hkust-gz.edu.cn.
DOI: 10.5281/zenodo.15746380
title: Shared data and scripts to support submitted manuscript "Influences of eastern Pacific and central Pacific El Niño events on the Indian summer monsoon: an assessment using the Met Office AGCM" to JGR-Atmosphere

description: This dataset contains all the data and scripts needed to reproduce the main figures in the manuscript. All data are preprocessed from the MetUM GA7.1 simulations using NCL. Note that these files are to support the article submitted to JGR-A, and may be edited under peer-review processes. Any inquiries go to henryzhenliu@hkust-gz.edu.cn.