cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14866784
title: Dataset for "Net Primary Productivity Orchestrates Uncertainty Sources Driving Global Soil Organic Carbon Under Land Use Change"

description: Data

The post-processed LULCC-induced SOC change data spans different periods across the three MIPs: 1901–2010 for MsTMIP, 1901–2014 for LUMIP, and 1901–2019 for TRENDYv9. Each file contains the slope of LULCC-induced SOC changes for each model within these MIPs..

MsTMIP DGVMs:  CLASS-CTEM-N, CLM4, DLEM, GTEC, JPL-HYLAND, ISAM, LPJ-wsl, JPL-CENTURY, ORCHIDEE-LSCE, SiBCASA, VEGAS, VISIT.

LUMIP ESMs: BCC-CSM2-MR, CMCC-ESM2, CNRM-ESM2-1, IPSL-CM6A-LR, MPI-ESM1-2-LR, UKESM1-0-LL.

TRENDYv9 DGVMs: CABLE, CLASSIC, CLM5, DLEM, IBIS, ISAM, JSBACH, JULES, LPJ-GUESS, LPJ-wsl, LPX-Bern, OCN, ORCHIDEE-CNP, ORCHIDEEv3, SDGVM, VISIT, YIBS. 

Code

The .tif files for each model were primarily processed using the "Raster Calculator" tool in ArcGIS 10.8. All figures presented in the main text and Supplementary Information were generated using ArcGIS 10.8 and/or Origin 2024b.
DOI: 10.15468/dl.tfphsf
title: Occurrence Download

description: A dataset containing 626663 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Delphinidae"
 ]
}

 The dataset includes 626663 records from 1320 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002904-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52dcu8
title: Occurrence Download

description: A dataset containing 5593 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Caiman crocodilus (Linnaeus, 1758)"
 ]
}

 The dataset includes 5593 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002908-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15054612
title: 8,163 High-Quality metagenome-assembled genomes from the global soil microbiome

description: Metagenome-assembled genome (MAG) sequences from the global soil microbiome were obtained from the Soil Microbial Dark Matter MAG and Microflora Danica long-read MAG catalogues, and filtered to retain only high-quality (HQ) MAGs based on MIMAG standards. That is, genomes that had >90% CheckM2 completeness and <5% CheckM2 contamination, and had the 23S, 16S, and 5S rRNA genes, and at least 18 different tRNA genes. MAGs were dereplicated at approximately species-level, using clustering thresholds of 95% average nucleotide identity (ANI) and 50% aligned fraction (AF).MAG sequences are within the compressed directory, 'MAGs.tar.gz'.Pooled predicted protein sequences are in 'HQ_MAGs.prots.faa.gz'.
DOI: 10.5281/zenodo.15054613
title: 8,163 High-Quality metagenome-assembled genomes from the global soil microbiome

description: Metagenome-assembled genome (MAG) sequences from the global soil microbiome were obtained from the Soil Microbial Dark Matter MAG and Microflora Danica long-read MAG catalogues, and filtered to retain only high-quality (HQ) MAGs based on MIMAG standards. That is, genomes that had >90% CheckM2 completeness and <5% CheckM2 contamination, and had the 23S, 16S, and 5S rRNA genes, and at least 18 different tRNA genes. MAGs were dereplicated at approximately species-level, using clustering thresholds of 95% average nucleotide identity (ANI) and 50% aligned fraction (AF).MAG sequences are within the compressed directory, 'MAGs.tar.gz'.Pooled predicted protein sequences are in 'HQ_MAGs.prots.faa.gz'.
DOI: 10.5281/zenodo.14934090
title: Dataset for "Playing Nonlocal Games across a Topological Phase Transition on a Quantum Computer"

description: Dataset used to create Fig. 2 of the paper titled "Playing Nonlocal Games across a Topological Phase Transition on a Quantum Computer."
DOI: 10.5281/zenodo.15042232
title: Stordalen Mire habitat classifications map (2014)

description: Stordalen Mire landcover classifications (including 3 permafrost thaw stages: Palsa, Bog, and Fen), based on WorldView-2 satellite imagery acquired on August 8, 2014. 

This image is a GeoTIFF with embedded georeferencing information.

 

FUNDING:



National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G)

National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070)

United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440)

National Science Foundation, MacroSystems program (grant # EF-1241037)
DOI: 10.5281/zenodo.15042233
title: Stordalen Mire habitat classifications map (2014)

description: Stordalen Mire landcover classifications (including 3 permafrost thaw stages: Palsa, Bog, and Fen), based on WorldView-2 satellite imagery acquired on August 8, 2014. 

This image is a GeoTIFF with embedded georeferencing information.

 

FUNDING:



National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G)

National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070)

United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440)

National Science Foundation, MacroSystems program (grant # EF-1241037)
DOI: 10.3886/e224383
title: National Board Certification as a Signal of Cooperating Teacher Quality

description: Prior research suggests that more effective teachers may also be better mentors for preservice student teachers, but the specific measures of cooperating teacher effectiveness considered in the prior quantitative literature (value added and performance evaluations) are infrequently observable to individuals responsible for student teaching placements. In this paper, we consider a more easily observed proxy for mentor effectiveness: National Board (NB) certification. We find that NB teachers are considerably more likely to host student teachers, and student teachers supervised by NB teachers are slightly more likely to be hired within three years, all else equal, but we find no consistent significant relationship between cooperating teacher NB certification and the later attrition or value added of the teachers they supervised.
description: We procured data on individual teaching candidates and their student-teaching placements from our partner institutions in the Teacher Education Learning Collaborative and they are protected from publication according to our data sharing agreement. Other researchers interested in accessing these data would need to reach out to each teacher education program partner independently to collect information on their students. The partnering institutions are as follows: Central Washington University, City University, Evergreen State University, Gonzaga University, Northwest Educational Development, Northwest University, Pacific Lutheran University, Seattle Pacific University, Seattle University, St. Martin’s University, University of Washington Bothell, University of Washington Seattle, University of Washington Tacoma, Washington State University, Western Governors University, Western Washington University, Yakima Valley College
DOI: 10.3886/e224383v1
title: National Board Certification as a Signal of Cooperating Teacher Quality

description: Prior research suggests that more effective teachers may also be better mentors for preservice student teachers, but the specific measures of cooperating teacher effectiveness considered in the prior quantitative literature (value added and performance evaluations) are infrequently observable to individuals responsible for student teaching placements. In this paper, we consider a more easily observed proxy for mentor effectiveness: National Board (NB) certification. We find that NB teachers are considerably more likely to host student teachers, and student teachers supervised by NB teachers are slightly more likely to be hired within three years, all else equal, but we find no consistent significant relationship between cooperating teacher NB certification and the later attrition or value added of the teachers they supervised.
description: We procured data on individual teaching candidates and their student-teaching placements from our partner institutions in the Teacher Education Learning Collaborative and they are protected from publication according to our data sharing agreement. Other researchers interested in accessing these data would need to reach out to each teacher education program partner independently to collect information on their students. The partnering institutions are as follows: Central Washington University, City University, Evergreen State University, Gonzaga University, Northwest Educational Development, Northwest University, Pacific Lutheran University, Seattle Pacific University, Seattle University, St. Martin’s University, University of Washington Bothell, University of Washington Seattle, University of Washington Tacoma, Washington State University, Western Governors University, Western Washington University, Yakima Valley College
DOI: 10.5281/zenodo.15085178
title: Uncertainty-aware Machine Learning Bias Correction and Filtering for OCO-2 | 2014-2024

description: Overview

This is a dataset to explore the effect of applying a new bias correction and quality filtering approach to increase the accuracy of atmospheric CO2 measurements derived from the Orbiting Carbon Observatory-2 (OCO-2) satellite. This is not an official OCO-2 data product.

 

Data Description

The dataset contains OCO-2 retrieved XCO2 B11.2 that have been corrected and filtered with a new machine learning approach from 2014 to the end of 2024. There is one file for each year. The following variables are contained in the files:

sounding_id, xco2_ML*, xco2, xco2_x2019, xco2_quality_flag_ML*, xco2_quality_flag, bias_correction_uncert_ML*, xco2_uncertainty, latitude, longitude, time, land_water_indicator, operation_mode

* new variables that contain the new bias coorected xco2, quality flag, and uncertainty and are not contained in the official OCO-2 Lite Files.

 

xco2_ML: XCO2 Machine Learning corrected XCO2 on x2019 scale

xco2_quality_flag_ML: XCO2 ternary quality flag:  0 = best quality data, 1 = good quality data for increasing sounding throughput if needed, 2 = poor quality data (do not use)

bias_correction_uncert_ML: XCO2 bias correction uncertainty

 

For the full set of variables contained in the LiteFiles and description of each variable please refere to the data user guide of the official OCO-2 lite files: https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.2r/summary?keywords=oco2

 

Current Bias Correction Approach

The operational bias correction method uses a multiple linear regression-like approach to adjust errors in XCO2 relative to elements of the state vector derived from the ACOS retrieval. Adjustments are currently made manually, guided by various success metrics like TCCON observation agreement, retrieval variability reduction, and flux model coherence.

 

New Bias Correction Approach

1. Computational Optimization: Replaces manual tuning with computational methods, enhancing transparency, traceability, and reproducibility.2. Non-linear Error Modeling: Allows more flexibility in modeling retrieval errors, reducing biases, particularly in previously unusable data.3. Independent Flux Inversion Models: Excludes flux inversion models from bias correction development, maintaining OCO-2 measurement independence.4. Quantified Correction Uncertainties: Provides uncertainty quantification for each bias correction at the per sounding level.

 

Data Usage

If you find anything unexpected in the data please report your findings to Steffen.mauceri@jpl.nasa.gov and william.r.keely@jpl.nasa.gov so we can resolve any issues.

Additional Resources and Citation

Two preprints are currently available that describe the approach in detail and should be cited if the data is used. We will update the citations as soon as the papers are published:

https://doi.org/10.22541/essoar.174164198.80749970/v1https://doi.org/10.22541/essoar.174164203.37422284/v1

 

Copyright statement: © 2023 California Institute of Technology. Government sponsorship acknowledged.
DOI: 10.5281/zenodo.15085179
title: Uncertainty-aware Machine Learning Bias Correction and Filtering for OCO-2 | 2014-2024

description: Overview

This is a dataset to explore the effect of applying a new bias correction and quality filtering approach to increase the accuracy of atmospheric CO2 measurements derived from the Orbiting Carbon Observatory-2 (OCO-2) satellite. This is not an official OCO-2 data product.

 

Data Description

The dataset contains OCO-2 retrieved XCO2 B11.2 that have been corrected and filtered with a new machine learning approach from 2014 to the end of 2024. There is one file for each year. The following variables are contained in the files:

sounding_id, xco2_ML*, xco2, xco2_x2019, xco2_quality_flag_ML*, xco2_quality_flag, bias_correction_uncert_ML*, xco2_uncertainty, latitude, longitude, time, land_water_indicator, operation_mode

* new variables that contain the new bias coorected xco2, quality flag, and uncertainty and are not contained in the official OCO-2 Lite Files.

 

xco2_ML: XCO2 Machine Learning corrected XCO2 on x2019 scale

xco2_quality_flag_ML: XCO2 ternary quality flag:  0 = best quality data, 1 = good quality data for increasing sounding throughput if needed, 2 = poor quality data (do not use)

bias_correction_uncert_ML: XCO2 bias correction uncertainty

 

For the full set of variables contained in the LiteFiles and description of each variable please refere to the data user guide of the official OCO-2 lite files: https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.2r/summary?keywords=oco2

 

Current Bias Correction Approach

The operational bias correction method uses a multiple linear regression-like approach to adjust errors in XCO2 relative to elements of the state vector derived from the ACOS retrieval. Adjustments are currently made manually, guided by various success metrics like TCCON observation agreement, retrieval variability reduction, and flux model coherence.

 

New Bias Correction Approach

1. Computational Optimization: Replaces manual tuning with computational methods, enhancing transparency, traceability, and reproducibility.2. Non-linear Error Modeling: Allows more flexibility in modeling retrieval errors, reducing biases, particularly in previously unusable data.3. Independent Flux Inversion Models: Excludes flux inversion models from bias correction development, maintaining OCO-2 measurement independence.4. Quantified Correction Uncertainties: Provides uncertainty quantification for each bias correction at the per sounding level.

 

Data Usage

If you find anything unexpected in the data please report your findings to Steffen.mauceri@jpl.nasa.gov and william.r.keely@jpl.nasa.gov so we can resolve any issues.

Additional Resources and Citation

Two preprints are currently available that describe the approach in detail and should be cited if the data is used. We will update the citations as soon as the papers are published:

https://doi.org/10.22541/essoar.174164198.80749970/v1https://doi.org/10.22541/essoar.174164203.37422284/v1

 

Copyright statement: © 2023 California Institute of Technology. Government sponsorship acknowledged.
DOI: 10.15468/dl.rw7n4f
title: Occurrence Download

description: A dataset containing 178 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ashmeadiella difugita Michener, 1939"
 ]
}

 The dataset includes 178 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002910-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xbvxvw
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Accipiter griseiceps (Kaup, 1848)"
 ]
}

 The dataset includes 247 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097424-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gagkpp
title: Occurrence Download

description: A dataset containing 37996 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bromus rubens L."
 ]
}

 The dataset includes 37996 records from 494 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062361-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9mlkky
title: Occurrence Download

description: A dataset containing 12985 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Lactarius rufus (Scop.) Fr.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12985 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038208-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.29wsqm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((173.1871 -41.34548,173.18773 -41.34548,173.18773 -41.34507,173.1871 -41.34507,173.1871 -41.34548))",
 "TaxonKey is Oenanthe javanica (Blume) DC."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0200270-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jfjr72
title: Occurrence Download

description: A dataset containing 797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Maera loveni (Bruzelius, 1859)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is MANAGED"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is not null"
 ]
 }
 }
 ]
 },
 {
 "or" : [
 "OccurrenceStatus is Present",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is not null"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 797 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058978-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.notxmh
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Pinus aristata Engelm.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 142 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038209-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kygw7b
title: Occurrence Download

description: A dataset containing 124 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.62414 36.02606,-5.6444 36.023,-5.65126 36.00328,-5.65195 35.96661,-5.57642 35.96133,-5.55513 35.99134,-5.56921 36.01745,-5.60389 36.01162,-5.62414 36.02606))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "or" : [
 "TaxonKey is Chlorophyta",
 "TaxonKey is Ochrophyta",
 "TaxonKey is Rhodophyta"
 ]
 }
 ]
}

 The dataset includes 124 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0200703-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.55yesf
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magellianira serrata Winkler, 1994",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is MANAGED"
 ]
 }
 },
 {
 "not" : {
 "EstablishmentMeans" : [
 "is not null"
 ]
 }
 }
 ]
 },
 {
 "or" : [
 "OccurrenceStatus is Present",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is not null"
 ]
 }
 }
 ]
 }
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059013-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udoskw
title: Occurrence Download

description: A dataset containing 1853 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Horminum pyrenaicum L.",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 1853 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038210-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tn59yv
title: Occurrence Download

description: A dataset containing 11483 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Citrullus lanatus (Thunb.) Matsum. & Nakai"
 ]
}

 The dataset includes 11483 records from 287 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002286-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7pevya
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eumachia purariensis (Sohmer) Barrabé, C.M.Taylor & Razafim."
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042349-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6yb12s
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is FLOTROP, a massive contribution to plant diversity data for open ecosystems in Tropical Africa.",
 "TaxonKey is Ammannia aspera Guill. & Perr."
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038210-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.em7cbh
title: Occurrence Download

description: A dataset containing 405 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microstegium ciliatum (Trin.) A.Camus"
 ]
}

 The dataset includes 405 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106192-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.