cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13963778
title: ALPHA-g Preprocessed Simulation Dataset for Machine Learning

description: Dataset generated for "Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber" 2024 preprint and "AI Meets Antimatter: Unveiling Antihydrogen Annihilations" paper for Machine Learning and the Physical Sciences workshop at NeurIPS'24 by Ferreira et al.

The goal of this project was to create a PointNet-like deep learning model that could learn the relationship between simulated ALPHA-g detector data and the position of an antimatter annihilation on the walls of the ALPHA-g vacuum chamber. If this approach to antimatter annihilation event reconstruction works with real-world data then it can then be used as part of a larger analysis pipeline to better constrain the effect of gravity on antimatter.

See the above papers for a much more detailed description of the experiment and data used but generally, the four important terms to know for interacting with this dataset are:




Term
Meaning


Event
An antimatter annihilation. Each event contains a number of spacepoints and one vertex.


Spacepoints
The many 3D points (x,y,z) that represent the ALPHA-g detector data, if more than 800 this dataset cuts them off at 800, prioritizing the ones with higher amplitudes, and if less than 800 it pads them with zeros such that all events have 800 entries.


Vertex
3D position of the antimatter annihilation. For this initial study, we just use the z coordinate since this is what is most important to measuring the effect of gravity. 


Helix Fit
The state-of-the-art method currently used for antimatter annihilation event reconstruction within the ALPHA-g detector. The method doesn't use machine learning and instead involves fitting 3D helix functions to the spacepoints.




 

Data is split into three separate HDF5 datasets: training (2,160,020 events), validation (261,654 events), and testing (288,919 events). Each dataset contains the following columns with each row representing one event:




Column Index
Value [mm]


0
x coordinates of spacepoints


1
y coordinates of spacepoints


2
z coordinates of spacepoints


3


z coordinate of simulated vertex



4


z coordinate of Helix Fit vertex prediction





 

The code used to generate the raw dataset is available at bitbucket.org/expalpha/alphasoft and the code used to process it into this preprocessed dataset as well as the downstream training and evaluation are available at gitlab.triumf.ca/alpha-ai/rTPC-AI.
DOI: 10.5281/zenodo.13963779
title: ALPHA-g Preprocessed Simulation Dataset for Machine Learning

description: Dataset generated for "Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber" 2024 preprint and "AI Meets Antimatter: Unveiling Antihydrogen Annihilations" paper for Machine Learning and the Physical Sciences workshop at NeurIPS'24 by Ferreira et al.

The goal of this project was to create a PointNet-like deep learning model that could learn the relationship between simulated ALPHA-g detector data and the position of an antimatter annihilation on the walls of the ALPHA-g vacuum chamber. If this approach to antimatter annihilation event reconstruction works with real-world data then it can then be used as part of a larger analysis pipeline to better constrain the effect of gravity on antimatter.

See the above papers for a much more detailed description of the experiment and data used but generally, the four important terms to know for interacting with this dataset are:




Term
Meaning


Event
An antimatter annihilation. Each event contains a number of spacepoints and one vertex.


Spacepoints
The many 3D points (x,y,z) that represent the ALPHA-g detector data, if more than 800 this dataset cuts them off at 800, prioritizing the ones with higher amplitudes, and if less than 800 it pads them with zeros such that all events have 800 entries.


Vertex
3D position of the antimatter annihilation. For this initial study, we just use the z coordinate since this is what is most important to measuring the effect of gravity. 


Helix Fit
The state-of-the-art method currently used for antimatter annihilation event reconstruction within the ALPHA-g detector. The method doesn't use machine learning and instead involves fitting 3D helix functions to the spacepoints.




 

Data is split into three separate HDF5 datasets: training (2,160,020 events), validation (261,654 events), and testing (288,919 events). Each dataset contains the following columns with each row representing one event:




Column Index
Value [mm]


0
x coordinates of spacepoints


1
y coordinates of spacepoints


2
z coordinates of spacepoints


3


z coordinate of simulated vertex



4


z coordinate of Helix Fit vertex prediction





 

The code used to generate the raw dataset is available at bitbucket.org/expalpha/alphasoft and the code used to process it into this preprocessed dataset as well as the downstream training and evaluation are available at gitlab.triumf.ca/alpha-ai/rTPC-AI.
DOI: 10.17909/52vt-2g08
title: Data from "A pilot method to determine the high mass end of the Stellar Initial Mass Function in galaxies using4 UVIT, Hα-MUSE observations and applied to NGC628"

description: We used the F2100 JWST filter to correct the internal extinction of the galaxy.
DOI: 10.25574/cdc.158
title: Chandra X-ray Observatory Chandra Data Collection for request #158

description: Chandra Data Collection for request #158
description: DOI updated to include the publication's DOI on 2024-01-30T09:00:00
DOI: 10.5281/zenodo.14225007
title: Ensembl IDs with associated gene-level metadata

description: A small table of ensembl gene IDs with associated symbol and gene biotype.It's often useful to download this information from biomart (and this table was generated with biomart), so this is a static version of the same data.All versions will be listed here, with the date they were retrieved from BioMart:- V1: 2024-04-08
DOI: 10.5281/zenodo.14225008
title: Ensembl IDs with associated gene-level metadata

description: A small table of ensembl gene IDs with associated symbol and gene biotype.It's often useful to download this information from biomart (and this table was generated with biomart), so this is a static version of the same data.All versions will be listed here, with the date they were retrieved from BioMart:- V1: 2024-04-08
DOI: 10.5066/p9s8722d
title: Stable carbon and oxygen isotope paleoclimate records of U.S. Geological Survey-collected samples from Devils Hole and Devils Hole Cave 2, Nevada

description: In support of paleoclimatology investigations, samples of mammillary calcite, calcitic folia, and flowstone were collected by the U.S. Geological Survey in Devils Hole and Devils Hole Cave 2, Nevada, between 1983 and 1996. These samples came from about 60 m below to 9 m above the modern water table in these caverns. To determine delta18O and delta13C time series spanning the interval 567.7-4.5 ka, more than a thousand samples were milled and analyzed for their delta18O and delta13C values. To determine time-series ages, more than a hundred samples were analyzed using uranium-series dating. Many of these measurement results have not been published. Herein, we provide previously unpublished delta13C and delta18O values, and we provide unpublished background data for selected uranium-series ages.
DOI: 10.5281/zenodo.14176814
title: Rapid multiplexed nanopore amplicon sequencing to distinguish Plasmodium falciparum recrudescence from new infection in antimalarial drug trials
DOI: 10.5281/zenodo.14176815
title: Rapid multiplexed nanopore amplicon sequencing to distinguish Plasmodium falciparum recrudescence from new infection in antimalarial drug trials
DOI: 10.6084/m9.figshare.27226247
title: Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers

description: Clustering is a widely used technique with a long and rich history in a variety of areas. However, most existing algorithms do not scale well to large datasets, or are missing theoretical guarantees of convergence. This article introduces a provably robust clustering algorithm based on loss minimization that performs well on Gaussian mixture models with outliers. It provides theoretical guarantees that the algorithm obtains high accuracy with high probability under certain assumptions. Moreover, it can also be used as an initialization strategy for <i>k</i>-means clustering. Experiments on real-world large-scale datasets demonstrate the effectiveness of the algorithm when clustering a large number of clusters, and a <i>k</i>-means algorithm initialized by the algorithm outperforms many of the classic clustering methods in both speed and accuracy, while scaling well to large datasets such as ImageNet. Supplementary materials for this article are available online.
DOI: 10.5284/1124495
title: Images from Buildings Recording and Investigation at Oxford LMS Swing Bridge, Oxford, 2019-2021

description: This collection comprises of site photographs from buildings recording and investigation carried out by Oxford Archaeology (South) at Oxford LMS Swing Bridge, Oxford between 2019 and 2021.
DOI: 10.5284/1124427
title: Images and Text from a Desk-Based Assessment and an Archaeological Evaluation near Canal Rise/Southfields, Bridgerule, Torridge, Devon, March 2017

description: This collection comprises images and a drawing sheet from a desk-based assessment and an archaeological evaluation of an adjoining land at Canal Rise/Southfields, Bridgerule, Torridge, Devon, by South West Archaeology Ltd. in March 2017.
DOI: 10.15125/bath-01457
title: Dataset for Multi-core anti-resonant hollow core optical fibre

description: The data in this repository contains the characterisation of fibres presented in the publication 'Multi-core anti-resonant hollow core optical fibre'. This includes microscope images of the fibres and the raw data files for the transmission, cutback and coupling measurements presented. These were taken to quantify the optical performance of the fibres, specifically the loss and inter-core coupling of light. Also included is the Matlab code used to process the data into the figure for the paper.
description: For the transmission measurements, a single mode fibre (SMF-28) was used to couple into a single core, with collection from the single core ensured by using a large multi-mode fibre which matches the core size (AFS50/125Y). The data is given in columns of wavelength (in nm) and signal (in dBm, not calibrated). For each length, multiple cleaves were taken with the average used to calculate the transmitted signal.

For the cutback, a similar method is used, but now the length is changed and the process is repeated. With the fibre loss being proportional to the length change.

For the coupling experiment, the near field of the fibre was imaged using a DMK 23U445 monochrome camera with different exposures. The IC Capture camera utility software was used to control the exposure of the camera to generate the two images.
description: An Energetiq EQ-99X laser-driven white light source and ANDO Optical Spectrum Analyser were used, with SMF28-e and AFS50/125Y fibres used for coupling.

A DMK 23U445 monochrome camera was used for the near field images. The scripts were run using MATLAB. Images are in TIFF and PNG formats.
DOI: 10.6084/m9.figshare.27910299
title: Data

description: Flexibility of Intrinsic Neural Timescales During Distinct Behavioral StatesDescription of the data and file structureThe github page https://github.com/duodenum96/acwbehavior contains the MATLAB code to replicate the figures. The numerical data necessary for the figures are presented here. We present the data in two formats, .mat format for MATLAB users and .csv format as well. Non-MATLAB users can access the data in .mat files using conversion software such as scipy.io.loadmat as well. The correspondence between .mat and .csv files are explained in get_figure_data.m. For any questions, you can contact to catalyasir@gmail.com.Files and variablesget_figure_data.m: Correspondence between .csv and .mat filesmice_behavioral_states.csv (822 x 1): Each row corresponds to one behavioral state of mice, in agreement with mice_acw_values.csv.mice_acw_values.csv (822 x 92): Each row corresponds to one ACW value estimated from the 10 second interval. The states are in agreement with mice_behavioral_states.csv. Each row is an interval, each column is a ROI.mousenames.csv (822 x 1): Identifiers of the micelabels.csv (1 x 5): Five labels for behavioral states.\\\\_all_acws.csv (987 x 64): ACW values estimated from 10 second intervals in human EEG data. \\\\ corresponds to rest, self and other conditions. Each row is an interval, each column is an EEG channel.subjcode.csv (987 x 1): Subject identifiers in numeric form for human EEG data.acwdr_ca.mat: MATLAB struct containing mice data.\\\\_acws_acwdr.mat: MATLAB struct containing human data. \\\\ corresponds to rest, self and other conditions.Code/softwarecsv files do not require any specific software. MATLAB files can be opened via MATLAB or software such as scipy.io.loadmat (python) or R.matlab package (R, function readMat in particular) or MAT.jl (Julia).
DOI: 10.6084/m9.figshare.27910299.v1
title: Data

description: Flexibility of Intrinsic Neural Timescales During Distinct Behavioral StatesDescription of the data and file structureThe github page https://github.com/duodenum96/acwbehavior contains the MATLAB code to replicate the figures. The numerical data necessary for the figures are presented here. We present the data in two formats, .mat format for MATLAB users and .csv format as well. Non-MATLAB users can access the data in .mat files using conversion software such as scipy.io.loadmat as well. The correspondence between .mat and .csv files are explained in get_figure_data.m. For any questions, you can contact to catalyasir@gmail.com.Files and variablesget_figure_data.m: Correspondence between .csv and .mat filesmice_behavioral_states.csv (822 x 1): Each row corresponds to one behavioral state of mice, in agreement with mice_acw_values.csv.mice_acw_values.csv (822 x 92): Each row corresponds to one ACW value estimated from the 10 second interval. The states are in agreement with mice_behavioral_states.csv. Each row is an interval, each column is a ROI.mousenames.csv (822 x 1): Identifiers of the micelabels.csv (1 x 5): Five labels for behavioral states.\\\\_all_acws.csv (987 x 64): ACW values estimated from 10 second intervals in human EEG data. \\\\ corresponds to rest, self and other conditions. Each row is an interval, each column is an EEG channel.subjcode.csv (987 x 1): Subject identifiers in numeric form for human EEG data.acwdr_ca.mat: MATLAB struct containing mice data.\\\\_acws_acwdr.mat: MATLAB struct containing human data. \\\\ corresponds to rest, self and other conditions.Code/softwarecsv files do not require any specific software. MATLAB files can be opened via MATLAB or software such as scipy.io.loadmat (python) or R.matlab package (R, function readMat in particular) or MAT.jl (Julia).
DOI: 10.48579/pro/kxsimf/ajtqhz
title: README.txt

description: :unav
DOI: 10.48579/pro/kxsimf/gbncqn
title: data_roedeer_hmm_ex_2011_2022.rds

description: :unav
DOI: 10.48579/pro/kxsimf/labd7r
title: data_roedeer_thermolog_2023.rds

description: :unav
DOI: 10.48579/pro/kxsimf/phus4r
title: data_roedeer_pact_2011_2022.rds

description: :unav
DOI: 10.48579/pro/kxsimf/w5pwp1
title: 3_AgriReproTemperature_SI_tables.R

description: :unav
DOI: 10.48579/pro/kxsimf/yzfqdn
title: 1_AgriReproTemperature_TEXT_figures.R

description: :unav
DOI: 10.48579/pro/kxsimf
title: Data for the article : Agricultural land use and reproductive behaviour constrain responses to summer thermal stress in a large herbivore

description: This projects aims to investigate the influence of local agricultural landscape composition and reproductive behaviour on the response of European roe deer (Capreolus capreolus) to high summer temperatures during the rut (breeding season). The data was collected as part of the long-term research project lead by the CEFS, INRAE on the LTSER ZA PYRénées GARonne in the South Ouest of France. The code and data reproduce the analyses and figures in the Results section of the manuscript, as well as the figures, summary tables, contrast tables and MSE scores in the Supporting Information (SI).
DOI: 10.48579/pro/kxsimf/exdlaj
title: data_roedeer_act_perhabitat_F_day_2011_2022.rds

description: :unav
DOI: 10.48579/pro/kxsimf/hiaiwm
title: data_roedeer_gps_1h_2011_2022.rds

description: :unav
DOI: 10.48579/pro/kxsimf/iaemjj
title: data_roedeer_ddt_2011_2022.rds

description: :unav
DOI: 10.48579/pro/kxsimf/jymshg
title: 2_AgriReproTemperature_SI_figures.R

description: :unav
DOI: 10.5284/1124498
title: Images from Buildings Recording And Investigation at Oxford Osney Power Station, Oxford, February 2018

description: This collection comprises of site photographs from buildings recording and investigation carried out by Oxford Archaeology (South) at Oxford Osney Power Station, Oxford during February 2018, prior to proposed redevelopment.
DOI: 10.15468/dl.srhv6g
title: Occurrence Download

description: A dataset containing 3275 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Trawl-survey data from the “expedition Hvar” in the Adriatic Sea (Mediterranean) collected in 1948-1949"
 ]
}

 The dataset includes 3275 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002373-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hhh9dz
title: Occurrence Download

description: A dataset containing 3444 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "TaxonKey is Acer crataegifolium Siebold & Zucc."
 ]
}

 The dataset includes 3444 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002375-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nhwspb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Pleione ×lagenaria Lindl. & Paxton)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002376-241125135418005/datasets/export for details.
DOI: 10.15468/dl.mkwx9b
title: Occurrence Download

description: A dataset containing 1235803 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Squamata",
 "Year 2015-2024"
 ]
}

 The dataset includes 1235803 records from 4781 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002350-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ukmqq2
title: Occurrence Download

description: A dataset containing 1752 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Kiggelaria L."
 ]
}

 The dataset includes 1752 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0198844-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzpwv7
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is South Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Mastomys coucha (A.Smith, 1834)"
 ]
}

 The dataset includes 123 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0119028-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.psxvia
title: Occurrence Download

description: A dataset containing 42809 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.0108 4.4339,-71.02203 4.95026,-70.95612 5.12604,-70.6897 5.31372,-71.26373 5.80719,-72.44659 5.16266,-72.0108 4.4339))",
 {
 "or" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "DatasetKey is Caracterización del hábitat de poblaciones silvestres de chigüiros (Hydrochoerus hydrochaeris) en Paz de Ariporo, Casanare",
 "DatasetKey is Registros biológicos a partir de actividades de pesca deportiva en los Llanos Orientales",
 "DatasetKey is Aves Acuáticas de las sabanas inundables del Meta y Casanare",
 "DatasetKey is Colección de peces del Museo de Historia Natural de la Pontificia Universidad Javeriana",
 "DatasetKey is Colección Zoológica de la Universidad del Tolima (CZUT)- Ictiología",
 "DatasetKey is Colección de Peces de Agua Dulce del Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-P)",
 "DatasetKey is Base de Datos de Flora de la Cuenca del Río Orinoco en Colombia",
 "DatasetKey is Dieta y preferencias alimenticias de poblaciones silvestres de chigüiros (Hydrochoerus hydrochaeris) en Paz de Ariporo, Casanare",
 "DatasetKey is Aves asociadas a las sabanas inundables de las cuencas de los ríos Bita, Manacacías y Cravo Sur - SULU II"
 ]
 },
 "HasCoordinate is true"
 ]
}

 The dataset includes 42809 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019922-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bpj872
title: Occurrence Download

description: A dataset listing the 19 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Sweden",
 "TaxonKey is Ixodida"
 ]
}

 The dataset's 19 records were derived from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031735-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xw3mya
title: Occurrence Download

description: A dataset containing 314027 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Macropus Shaw, 1790"
 ]
}

 The dataset includes 314027 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085972-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3vl717
title: Occurrence Download

description: A dataset containing 108171 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Picoides pubescens (Linnaeus, 1766)",
 "HasCoordinate is True",
 "Country is United States of America",
 "Year is greater than or equal to 1999",
 "Year is less than or equal to 2020",
 "Month is greater than or equal to January",
 "Month is less than or equal to December",
 "HasGeospatialIssue is False",
 "DecimalLatitude is greater than or equal to 27",
 "DecimalLatitude is less than or equal to 41",
 "DecimalLongitude is greater than or equal to -91",
 "DecimalLongitude is less than or equal to -75"
 ]
}

 The dataset includes 108171 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013976-200221144449610/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.