cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14238350
title: 2024 HamSCI Gladstone Signal Spotting Challenge (GSSC) Data and Scoring Calculations

description: The GSSC 2024 Methodology and Scoring ZIP file contains data from the sources used in scoring the October 13, 2023 running of the HamSCI GSSC competition:  ReverseBeacon.net, WSPRnet.org.  The raw PSKReporter.info files are here as individual ZIP files due to their size.

There is a ZIP file containing each entrant's data as extracted from the WSPRnet data.  Further, it contains the participant entries (in an Apple Mac OS Numbers formatted spreadsheet) along with the scoring calculation file (Numbers format).  Included is a description of the scoring methodology (Pages format) and the shell scripts used to extract the WSPRnet data.  The GSSC 2024 Writeup for Website v1.0.PDF, as published on the HamSCI.org website, summarizes the entire event.  

Additional ZIP files, eg gssc2024bonusXX.zip, contain emails and attachments (PDF, jpg, etc) submitted by the entrants for bonus points.

The purpose of the GSSC competition was to generate ham radio 'spots' (records of 1- and 2-way amateur radio contacts on the shortware bands, aggregated in the form of transmission and reception reports stored in the data sources referenced above).  The 'spot' data is intended for use in studies of radio wave propagation via the ionosphere.
DOI: 10.5281/zenodo.14239594
title: 2024 HamSCI Solar Eclipse QSO Party (SEQP) Data and Scoring Calculations

description: This dataset contains the raw data sources (.tsv and .csv formats) used in scoring the 2024 running of the SEQP (PSKReporter, ReverseBeaconNetwork).  Further, it contains intermediate files (PSKR Spots for Participants Only, PSKR Spots for Particiapnt Callsigns by Hour) created to help digest the 101 million lines of PSKR data.  Included are the shell scripts used to create the intermediate files.  480 participant log files and 6 checklog files are present. Where used, spreasdsheets are in the Apple Mac OS Numbers format. 

Additional ZIP files, eg ‘seqp2024bonusXX.zip’, contain emails and attachments (PDF, jpg, etc) submitted by the SEQP entrants for bonus points.

'SEQP Scoring and Writeup.zip' contains a file describing the scoring methodology (in an Apple Pages format) and the Master Scoring spreadhsset (Numbers).  The ‘SEQP 2024 Writeup for Website.key’ (Apple Keynote format), as published to the HamSCI.org website, summarizes the entire event.
DOI: 10.5281/zenodo.14239595
title: 2024 HamSCI Solar Eclipse QSO Party (SEQP) Data and Scoring Calculations

description: This dataset contains the raw data sources (.tsv and .csv formats) used in scoring the 2024 running of the SEQP (PSKReporter, ReverseBeaconNetwork).  Further, it contains intermediate files (PSKR Spots for Participants Only, PSKR Spots for Particiapnt Callsigns by Hour) created to help digest the 101 million lines of PSKR data.  Included are the shell scripts used to create the intermediate files.  480 participant log files and 6 checklog files are present. Where used, spreasdsheets are in the Apple Mac OS Numbers format. 

Additional ZIP files, eg ‘seqp2024bonusXX.zip’, contain emails and attachments (PDF, jpg, etc) submitted by the SEQP entrants for bonus points.

'SEQP Scoring and Writeup.zip' contains a file describing the scoring methodology (in an Apple Pages format) and the Master Scoring spreadhsset (Numbers).  The ‘SEQP 2024 Writeup for Website.key’ (Apple Keynote format), as published to the HamSCI.org website, summarizes the entire event.
DOI: 10.5281/zenodo.14238873
title: 2023 HamSCI Solar Eclipse QSO Party (SEQP) Data and Scoring Calculations

description: The ‘SEQP 2023 All Data and Files’ ZIP file contains the data sources used in scoring the October 13, 2023 running of the HamSCI SEQP competition:  222 individual participants’ logs plus ReverseBeacon.net and PSKReporter.info downloads.   Included are various intermediate spreadsheets from the scoring process (Apple Mac OS Numbers format) along with the Master Scoring spreadsheet.  One file describes the scoring methodology (in an Apple Pages format).  The ‘SEQP 2023 Writeup for Website.key’ (Apple Keynote format), as published to the HamSCI.org website, summarizes the entire event.  Checklogs (from the SEQP and various state QSO Parties held the same day) are also in the above file.

Additional ZIP files, eg ‘seqp2023bonusXX.zip’, contain emails and attachments (PDF, jpg, etc) submitted by the SEQP entrants for bonus points.

The purpose of the SEQP competition was to generate ham radio logs and 'spots' (records of 2-way amateur radio contacts on the shortwave bands, aggregated in the form of transmission and reception reports stored in the data sources referenced above).  The 'spot' data is intended for use in studies of radio wave propagation via the ionosphere.
DOI: 10.5281/zenodo.14238874
title: 2023 HamSCI Solar Eclipse QSO Party (SEQP) Data and Scoring Calculations

description: The ‘SEQP 2023 All Data and Files’ ZIP file contains the data sources used in scoring the October 13, 2023 running of the HamSCI SEQP competition:  222 individual participants’ logs plus ReverseBeacon.net and PSKReporter.info downloads.   Included are various intermediate spreadsheets from the scoring process (Apple Mac OS Numbers format) along with the Master Scoring spreadsheet.  One file describes the scoring methodology (in an Apple Pages format).  The ‘SEQP 2023 Writeup for Website.key’ (Apple Keynote format), as published to the HamSCI.org website, summarizes the entire event.  Checklogs (from the SEQP and various state QSO Parties held the same day) are also in the above file.

Additional ZIP files, eg ‘seqp2023bonusXX.zip’, contain emails and attachments (PDF, jpg, etc) submitted by the SEQP entrants for bonus points.

The purpose of the SEQP competition was to generate ham radio logs and 'spots' (records of 2-way amateur radio contacts on the shortwave bands, aggregated in the form of transmission and reception reports stored in the data sources referenced above).  The 'spot' data is intended for use in studies of radio wave propagation via the ionosphere.
DOI: 10.5281/zenodo.14164848
title: Annular Solar Eclipse 2023: recordings of the entire medium-wave AM broadcast band from near Masset, BC, Canada,  Part 2

description: recorded by Nick Hall-Patch, VE7DXR

Grid square of location recorded:  CO44aa 

These data files are recordings of the entire medium wave broadcast band (535-1705kHz) made during the annular solar eclipse of 14 October 2024 from 1518UT to 1732UT from near Masset, BC, Canada.  They are part of a set of such recordings made from 13 Oct 1330UT to 15 Oct 1715UT.  Maximum obscuration of the sun was 69% at 1619UT.  

Data files are .wav format, but the archived files are .zip, with about ten .wav files packaged together in each .zip file, using the prefix "VE7DXR-Masset-wideband_125_2125kHz".  As the total files recorded during the solar eclipse exceeded the Zenodo 50GB limit, these files are the remainder of the data files found in "Annular Solar Eclipse 2023:  recordings of the entire medium-wave AM broadcast band from near Masset, BC, Canada,  Part 1”.  Please see that record for software and for supporting documentation.

The recordings not included here or in Part 1 are available to provide reference data for the eclipse recordings.  Please contact HamSCI at am-eclipse@hamsci.org if you require this data. 

Antenna:   North oriented Beverage, 450' length, unterminated; amplified by DX Engineering RPA-1 at receiver.  

Receiver:  SDRplay RSPdx with RF gain = 20dB and using HDR mode, with center frequency 1125kHz, recording 2000kHz wide passband, using SDR Console v 3.2 build 2643.  Although the RSPdx was not frequency locked, a Bodnar Mini Precision GPS Reference Clock outputting 1705.0000kHz was on site, and its accurate signal was recorded in the data.   Computer time accuracy was determined by Windows NTP server.
DOI: 10.5281/zenodo.14164849
title: Annular Solar Eclipse 2023: recordings of the entire medium-wave AM broadcast band from near Masset, BC, Canada,  Part 2

description: recorded by Nick Hall-Patch, VE7DXR

Grid square of location recorded:  CO44aa 

These data files are recordings of the entire medium wave broadcast band (535-1705kHz) made during the annular solar eclipse of 14 October 2024 from 1518UT to 1732UT from near Masset, BC, Canada.  They are part of a set of such recordings made from 13 Oct 1330UT to 15 Oct 1715UT.  Maximum obscuration of the sun was 69% at 1619UT.  

Data files are .wav format, but the archived files are .zip, with about ten .wav files packaged together in each .zip file, using the prefix "VE7DXR-Masset-wideband_125_2125kHz".  As the total files recorded during the solar eclipse exceeded the Zenodo 50GB limit, these files are the remainder of the data files found in "Annular Solar Eclipse 2023:  recordings of the entire medium-wave AM broadcast band from near Masset, BC, Canada,  Part 1”.  Please see that record for software and for supporting documentation.

The recordings not included here or in Part 1 are available to provide reference data for the eclipse recordings.  Please contact HamSCI at am-eclipse@hamsci.org if you require this data. 

Antenna:   North oriented Beverage, 450' length, unterminated; amplified by DX Engineering RPA-1 at receiver.  

Receiver:  SDRplay RSPdx with RF gain = 20dB and using HDR mode, with center frequency 1125kHz, recording 2000kHz wide passband, using SDR Console v 3.2 build 2643.  Although the RSPdx was not frequency locked, a Bodnar Mini Precision GPS Reference Clock outputting 1705.0000kHz was on site, and its accurate signal was recorded in the data.   Computer time accuracy was determined by Windows NTP server.
DOI: 10.15468/dl.rwcvsm
title: Occurrence Download

description: A dataset containing 1348 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Diplomitoporus flavescens (Bres.) Domański",
 "Year 1900-2024"
 ]
}

 The dataset includes 1348 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003468-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14743235
title: Ressourcen zur Bachlorarbeit
DOI: 10.5281/zenodo.14743236
title: Ressourcen zur Bachlorarbeit
DOI: 10.15468/dl.8uhs2m
title: Occurrence Download

description: A dataset containing 918 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (April, May, June, July)",
 "TaxonKey is Lagopus muta (Montin, 1781)",
 "Year 2000-2025"
 ]
}

 The dataset includes 918 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003470-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjcp2e
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Coendou bicolor (Tschudi, 1844)"
 ]
}

 The dataset includes 39 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003469-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f7nc4j
title: Occurrence Download

description: A dataset containing 60227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "StateProvince is one of (Vermont, New York, New Hampshire, Connecticut, Maine, Rhode Island, Massachusetts)",
 "TaxonKey is Megascops asio (Linnaeus, 1758)"
 ]
}

 The dataset includes 60227 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003471-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14743256
title: NDVI_TimeSeries_For_MATLAB
DOI: 10.5281/zenodo.14743257
title: Forecasted_NDVI_results
DOI: 10.5281/zenodo.14743267
title: NDVI_TimeSeries_For_MATLAB
DOI: 10.5281/zenodo.14254043
title: IR Lab Cologne/Jena/Kassel Winter Term 2024/2025

description: The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025

This repository contains resources coupled to ir_datasets and TIREx for IR courses that focus their hands-on labs on shared tasks. During the IR exercises in winter term 2023/2024, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at https://tira.io/task-overview/ir-lab-wise-2024. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.

 

The artifact in this dataset include the following files:



subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.

subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.


Accessing the Data with ir_datasets

We provide wrapper code to easily access the resources with ir_datasets:

# this loads a patched version of ir_datasets that can load resources from TIRA
from tira.third_party_integrations import ir_datasets

training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')

Similarly, the same is possible with the ir_datasets integration to PyTerrier:

from tira.third_party_integrations import ensure_pyterrier_is_loaded
import pyterrier as pt

# this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox
ensure_pyterrier_is_loaded()

training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')
DOI: 10.5281/zenodo.14254044
title: IR Lab Cologne/Jena/Kassel Winter Term 2024/2025

description: The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025

This repository contains resources coupled to ir_datasets and TIREx for IR courses that focus their hands-on labs on shared tasks. During the IR exercises in winter term 2023/2024, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at https://tira.io/task-overview/ir-lab-wise-2024. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.

 

The artifact in this dataset include the following files:



subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.

subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.


Accessing the Data with ir_datasets

We provide wrapper code to easily access the resources with ir_datasets:

# this loads a patched version of ir_datasets that can load resources from TIRA
from tira.third_party_integrations import ir_datasets

training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')

Similarly, the same is possible with the ir_datasets integration to PyTerrier:

from tira.third_party_integrations import ensure_pyterrier_is_loaded
import pyterrier as pt

# this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox
ensure_pyterrier_is_loaded()

training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')
DOI: 10.5281/zenodo.14600777
title: IR Lab Cologne/Jena/Kassel Winter Term 2024/2025

description: The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025

This repository contains resources coupled to ir_datasets and TIREx for IR courses that focus their hands-on labs on shared tasks. During the IR exercises in winter term 2023/2024, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at https://tira.io/task-overview/ir-lab-wise-2024. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.

 

The artifact in this dataset include the following files:



subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.

subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.


Accessing the Data with ir_datasets

We provide wrapper code to easily access the resources with ir_datasets:

# this loads a patched version of ir_datasets that can load resources from TIRA
from tira.third_party_integrations import ir_datasets

training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')

Similarly, the same is possible with the ir_datasets integration to PyTerrier:

from tira.third_party_integrations import ensure_pyterrier_is_loaded
import pyterrier as pt

# this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox
ensure_pyterrier_is_loaded()

training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')
DOI: 10.5281/zenodo.14602253
title: IR Lab Cologne/Jena/Kassel Winter Term 2024/2025

description: The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025

This repository contains resources coupled to ir_datasets and TIREx for IR courses that focus their hands-on labs on shared tasks. During the IR exercises in winter term 2023/2024, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at https://tira.io/task-overview/ir-lab-wise-2024. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.

 

The artifact in this dataset include the following files:



subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.

subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.


Accessing the Data with ir_datasets

We provide wrapper code to easily access the resources with ir_datasets:

# this loads a patched version of ir_datasets that can load resources from TIRA
from tira.third_party_integrations import ir_datasets

training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')

Similarly, the same is possible with the ir_datasets integration to PyTerrier:

from tira.third_party_integrations import ensure_pyterrier_is_loaded
import pyterrier as pt

# this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox
ensure_pyterrier_is_loaded()

training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')
DOI: 10.5281/zenodo.14743268
title: IR Lab Cologne/Jena/Kassel Winter Term 2024/2025

description: The Datasets for the Information Retrieval Courses in Cologne/Jena/Kassel in Winter Term 2024/2025

This repository contains resources coupled to ir_datasets and TIREx for IR courses that focus their hands-on labs on shared tasks. During the IR exercises in winter term 2023/2024, we collaboratively developed and evaluated IR systems in a shared task style setup, covering corpus creation, system development, and statistical analysis. The resulting artifacts, i.e., the documents, topics, runs, relevance judgments can be browsed at https://tira.io/task-overview/ir-lab-wise-2024. This zenodo artifact contains all of the underlying datasets used and produced during the course together with instructions on how to easily access the data using ir_datasets.

 

The artifact in this dataset include the following files:



subsampled-ms-marco-deep-learning-20241201-training-inputs.zip containing the training inputs, i.e., containing the document corpus and the topics.

subsampled-ms-marco-deep-learning-20241201-training-truths.zip containing the training truth to evaluate and tune systems, i.e., the topics and relevance judgments.


Accessing the Data with ir_datasets

We provide wrapper code to easily access the resources with ir_datasets:

# this loads a patched version of ir_datasets that can load resources from TIRA
from tira.third_party_integrations import ir_datasets

training_dataset = ir_datasets.load('ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')

Similarly, the same is possible with the ir_datasets integration to PyTerrier:

from tira.third_party_integrations import ensure_pyterrier_is_loaded
import pyterrier as pt

# this patches ir_datasets and loads PyTerrier so that it can load resources from TIRA and can run in the TIRA sandbox
ensure_pyterrier_is_loaded()

training_dataset = pt.datasets.get_dataset('irds:ir-lab-wise-2024/subsampled-ms-marco-deep-learning-20241201-training')
DOI: 10.48758/ufz.15496
title: FertilizationMetalStress_SoilProkaryotes_FASTA_Abundance

description: This dataset includes FASTA sequences and abundance data from metagenomic sequencing for prokaryotic communities from agricultural soils subjected to 119 years of fertilization under four treatments: unfertilized, mineral fertilization, organic fertilization, and combined mineral + organic fertilization. The soils either retained their native metal contents or were amended with elevated metal levels of +0.2 ppmw Cd, +33.3 ppmw Zn, and +4.8 ppmw Pb above background values. Soils were incubated for 24 days, with samples collected on days 0, 7, and 24. Please refer to the SampleList.xlsx file to decode sample names.
DOI: 10.15468/dl.tsmnsr
title: Occurrence Download

description: A dataset containing 94384 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "StateProvince is one of (Vermont, New York, New Hampshire, Connecticut, Maine, Rhode Island, Massachusetts)",
 "TaxonKey is Bubo virginianus (J.F.Gmelin, 1788)"
 ]
}

 The dataset includes 94384 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003472-250123221155621/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.