cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1200568.09fu
title: SH1200568.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1200569.09fu
title: SH1200569.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.6084/m9.figshare.17332865.v1
title: Time-series_power3

description: The time series for the displacement of a trapped Brownian particle modulated by Ornstein-Uhlenbeck noise is provided. The strengh of the noise correspond to the diffusion coefficient A= 0.3X(0.6X10<sup>-6</sup>)<sup>2 </sup>m<sup>2</sup>/s.
DOI: 10.6084/m9.figshare.17332865
title: Time-series_power3

description: The time series for the displacement of a trapped Brownian particle modulated by Ornstein-Uhlenbeck noise is provided. The strengh of the noise correspond to the diffusion coefficient A= 0.3X(0.6X10<sup>-6</sup>)<sup>2 </sup>m<sup>2</sup>/s.
DOI: 10.7298/f18v-s618
title: Bird Cams Lab Video Data
DOI: 10.6084/m9.figshare.3761562.v2080
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.16652890.v2
title: Documenting Soil Archive Use in Peer-Reviewed Publications: 1915-2021

description: Peer-reviewed publications with documented use of soil archives were identified through a literature search using the USDA National Agricultural Library’s Digitop search engine, supplemented with results from Wageningen University & Research Library and Google Scholar. References in identified publications were further screened for additional publications relating to use of archived soils. Only publications in English or with English abstracts were considered. Identified publications were vetted to ensure archived soils were experimentally used in each study. For purposes of this compilation, archived soils were designated as those that had been stored for five or more years. A total of 245 publications were identified. Each publication was reviewed for key metadata, including publication year, age of oldest sample, and sample collection location. Land use associated with archived soil, along with the subject of each investigation were also recorded. Compiled metadata were transcribed into a single spreadsheet with sortable columns. An abstract for each publication was included in the spreadsheet, and when available, a digital object identifier was recorded to facilitate electronic access. Compiled information in the spreadsheet provides researchers a valuable resource for documenting use of soil archives globally.
DOI: 10.6084/m9.figshare.16620361
title: Femur bone histomorphometry for ID #B21 - Snoddy et al

description: This dataset reports raw and average (in bold and red font) histology data representing posterior midshaft femoral samples from a 19th century individual afflicated with one-sided hip joint destruction. The data derive from the left and right femur and can be used in estimating bone remodelling.
DOI: 10.25345/c5xw17
title: MassIVE MSV000088599 - GLEAMS notebooks supplementary data
title: GLEAMS is a deep neural network to embed spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We have used GLEAMS as the basis for a large-scale spectrum clustering, detecting groups of unidentified, proximal spectra representing the same peptide. This dataset includes intermediate and final data to reproduce all analyses described in the GLEAMS manuscript. The corresponding code notebooks are available as open source at https://github.com/bittremieux/GLEAMS_notebooks.
DOI: 10.14272/dipwhgronxmeeo-uhfffaoysa-n.1
title: 1,3,5-tris(4-(2,6-dimethylpyridin-4-yl)quinolin-2-yl)benzene
title: InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3

description: This dataset contains experimental data from a sample (InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3). The datasets from the following experiments are available: fast-atom bombardment mass spectrometry (FABMS) infrared absorption spectroscopy (IR) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR)
DOI: 10.14272/dipwhgronxmeeo-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 9.14 (s, 3H), 8.34 (d, J = 8.3 Hz, 3H), 8.08 (s, 3H), 7.87 (dd, J = 0.9 Hz, J = 8.4 Hz, 3H), 7.83–7.79 (m, 3H), 7.58–7.54 (m, 3H), 7.23 (s, 6H), 2.68 (s, 18H). Impurities: The spectrum contains residues of triethyl amine at 1.06 and 2.53 ppm and water at 1.80 ppm.
DOI: 10.14272/dipwhgronxmeeo-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3055 (w), 2963 (w), 2917 (w), 1606 (w), 1587 (vs), 1544 (vs), 1507 (m), 1466 (w), 1436 (w), 1415 (m), 1397 (m), 1381 (m), 1347 (s), 1279 (m), 1217 (m), 1137 (w), 1028 (w), 946 (w), 878 (w), 854 (s), 758 (vs), 735 (s), 715 (m), 696 (s), 670 (w), 654 (m), 646 (s), 619 (s), 598 (m), 555 (s), 543 (s), 501 (m), 459 (s), 436 (m), 424 (s), 404 (m), 390 (m), 375 (m) cm–1.
DOI: 10.14272/dipwhgronxmeeo-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 158.3 (Cq, 6C), 156.3 (Cq, 3C), 148.7 (Cq, 3C), 147.2 (Cq, 3C), 146.8 (Cq, 3C), 140.7 (Cq, 3C), 130.3 (CH, 3C), 130.0 (CH, 3C), 127.8 (CH, 3C), 127.0 (CH, 3C), 125.2 (CH, 3C), 125.2 (Cq, 3C), 120.9 (CH, 6C), 119.0 (CH, 3C), 24.7 (CH3, 6C). Impurities: The spectrum contains triethylamine at 46.08 ppm.
DOI: 10.14272/dipwhgronxmeeo-uhfffaoysa-n/chmo0000563
title: fast-atom bombardment mass spectrometry (FABMS) ()
title: InChI=1S/C54H42N6/c1-31-19-37(20-32(2)55-31)46-28-52(58-49-16-10-7-13-43(46)49)40-25-41(53-29-47(38-21-33(3)56-34(4)22-38)44-14-8-11-17-50(44)59-53)27-42(26-40)54-30-48(39-23-35(5)57-36(6)24-39)45-15-9-12-18-51(45)60-54/h7-30H,1-6H3

description: dataset for fast-atom bombardment mass spectrometry (FABMS) MS (FAB m/z, Matrix: 3-NBA): 777 (27), 776 (70) [M+H]+, 775 (100) [M]+, 774 (23), 773 (10), 307 (28), 289 (13), 155 (27), 154 (99), 152 (10), 139 (13), 138 (32), 137 (62), 136 (64), 120 (10), 107 (21), 90 (10), 89 (16). HRMS-FAB (m/z) [M+H]+, (C54H43N6): calcd 775.3549; found 775.3552.
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0001150
title: 1H--1H correlation spectroscopy (1H-1H COSY) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for 1H--1H correlation spectroscopy (1H-1H COSY)
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 195.2, 138.3, 115.6, 27.3.
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0001148
title: 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC)
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0001075
title: elemental analysis (EA) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for elemental analysis (EA)
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 12.44 (s, 1H), 10.41 (s, 1H), 7.32 (d, J = 7.8 Hz, 1H), 6.78 (d, J = 7.8 Hz, 1H), 3.62 (sept, J = 7.0 Hz, 1H), 2.22 (s, 3H), 1.33 (d, J = 6.8 Hz, 6H).
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 195.2, 138.3, 115.6, 27.3, 24.3, 15.0.
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 195.1 (HC=O), 161.6 (Cq), 150.2 (Cq), 138.2 (CHar), 124.3 (Cq), 116.3 (Cq), 115.4 (CHar), 27.2 (CH(CH3)2), 24.2 (CH(CH3)2, 2C), 14.9 (CH3).
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0002337
title: electrospray ionisation quadrupole detection (ESI Q Trap) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for electrospray ionisation quadrupole detection (ESI Q Trap) MS (ESI), m/z (%): 180.11 (12), 179.11 (100) [M+H]+. HRMS (ESI, [M+H]+, C11H15O2) calcd.: 179.1067, found: 179.1065.
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n.1
title: 2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: This dataset contains experimental data from a sample (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde, InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT) 1H--1H correlation spectroscopy (1H-1H COSY) 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC) electrospray ionisation quadrupole detection (ESI Q Trap) infrared absorption spectroscopy (IR) elemental analysis (EA)
DOI: 10.14272/llvqqqqukueoow-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2-hydroxy-3-methyl-6-propan-2-ylbenzaldehyde)
title: InChI=1S/C11H14O2/c1-7(2)9-5-4-8(3)11(13)10(9)6-12/h4-7,13H,1-3H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2963 (m), 2927 (w), 2897 (w), 2874 (w), 2798 (vw), 1629 (vs), 1584 (w), 1520 (w), 1497 (w), 1456 (s), 1424 (s), 1405 (vs), 1385 (m), 1381 (m), 1366 (w), 1347 (w), 1307 (vs), 1264 (s), 1255 (vs), 1225 (vs), 1176 (m), 1154 (m), 1106 (w), 1062 (w), 1031 (vs), 975 (s), 952 (m), 925 (w), 870 (w), 822 (s), 779 (vs), 758 (s), 715 (vs), 691 (vs), 653 (s), 628 (m), 575 (w), 545 (vs), 510 (w), 492 (m), 459 (m), 443 (m), 424 (w), 394 (w), 390 (w), 377 (w) cm–1.
DOI: 10.5281/zenodo.5586103
title: Artifact for paper "Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?" published at IEEE SANER 2022

description: The zip file contains all the source code and data for the experiments.
DOI: 10.5281/zenodo.5586102
title: Artifact for paper "Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?" published at IEEE SANER 2022

description: The zip file contains all the source code and data for the experiments.
DOI: 10.6084/m9.figshare.16840435
title: Multiparametric QPI dataset

description: This dataset is the output from the tracking code that can be found on our lab's github repository 'Multiparametric QPI'. The code in the 'Post Tracking Analysis' folder in the same github repository can be used to further analyze and plot/replicate the results of our study.
DOI: 10.3886/e157942
title: Local Implementation of State-Level Discipline Policy: Administrator Perspectives and Contextual Factors Associated with Compliance

description: This project includes information about data collection and data analysis related to <br>Local Implementation of State-Level Discipline Policy: Administrator Perspectives and Contextual Factors Associated with Compliance, by Kaitlin P. Anderson & Sarah McKenzie, forthcoming in AERA Open.<br>
description: mixed mode; other; web-based survey;
DOI: 10.3886/e157942v1
title: Local Implementation of State-Level Discipline Policy: Administrator Perspectives and Contextual Factors Associated with Compliance

description: This project includes information about data collection and data analysis related to <br>Local Implementation of State-Level Discipline Policy: Administrator Perspectives and Contextual Factors Associated with Compliance, by Kaitlin P. Anderson & Sarah McKenzie, forthcoming in AERA Open.<br>
description: mixed mode; other; web-based survey;
DOI: 10.18739/a2nv99c0r
title: Alaska borough and regional demographics, 1990-2020

description: Provides time series of annual population estimates, 1990-2020, for (1) all Alaska boroughs, census areas or municipalities ("county equivalents); (2) Alaska regions; and (3) the state of Alaska. Also includes 10-year summaries of population growth rates and growth components (natural increase or net migration) for each place, covering the periods 1990-2000, 2000-2010, 2010-2020, and 2019-2020. Data have been integrated in place/year analyzable form, from tables published by the Alaska Department of Labor and Workforce Development. Demographic time series provide key background information relevant to sustainability and challenges of communities in rural Alaska.
DOI: 10.5281/zenodo.5639007
title: Codes and parameter datasets for the "Bacteriophage self-counting in the presence of viral replication"

description: Matlab codes and datasets used for modeling in the paper "Bacteriophage self-counting in the presence of viral replication" PNAS 2021 Vol. 118 No. 51 e2104163118 https://doi.org/10.1073/pnas.2104163118
DOI: 10.5281/zenodo.5639006
title: Codes and parameter datasets for the "Bacteriophage self-counting in the presence of viral replication"

description: Matlab codes and datasets used for modeling in the paper "Bacteriophage self-counting in the presence of viral replication" PNAS 2021 Vol. 118 No. 51 e2104163118 https://doi.org/10.1073/pnas.2104163118
DOI: 10.3886/e158082
title: School district Twitter use

description: While educators’ uses of social media for purposes such as professional learning and networking are now well-established, our understanding of how educational institutions use social media—including to engage key stakeholders during periods of crisis—is limited. In this study, we used a public data mining research approach to examine how K-12 school districts in the United States used Twitter as a communication tool during a critical period of the COVID-19 pandemic, March-April, 2020. Through a three-step grounded theory approach of 1,357 district tweets from 492 school districts, we found that districts worked to build community and share time-sensitive announcements in alignment with social media crisis communication recommendations. Announcements were more common during the early stages of the pandemic (and were engaged more collaboratively), with community-building posts more common later on. This study demonstrates how researchers can use publicly available (social media) data to understand districts’ communication priorities and strategies during and beyond periods of crisis.
DOI: 10.25345/c5tc61
title: MassIVE MSV000088600 - RESIVEG_Classical_Networking_modonegativo_better
title: Classical Networking of plants for test in Classical Networking in GNPS
DOI: 10.24414/rob-euref-c2175
title: EPN Multi-year Position and Velocity Solution C2175

description: EUREF releases, each 15 weeks, an update of multi-year coordinates/velocities of the EPN stations in the latest ITRS/ETRS89 realizations. The Reference Frame Coordinator computes the EPN multi-year position and velocity solutions with the CATREF software (Altamimi et al., 2007).
DOI: 10.5281/zenodo.5797013
title: Supplementary Dataset for Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

description: This dataset is the supplementary dataset for the paper "<strong>Deep learning based <em>k</em><sub>cat</sub> prediction enables improved enzyme constrained model reconstruction</strong>". Protein sequence fasta files, deep learning predicted <em>k</em><sub>cat</sub> values, classcial-ecGEMs, DL-ecGEMs and <em>Posterior</em>-mean-ecGEMs for 343 yeast/fungi species are available in this dataset.This repository also contains the computed results for reproducing the figures as model_build_files . The scripts can be found in Github (https://github.com/SysBioChalmers/DLKcat)
DOI: 10.25919/jpbm-qg49
title: Assessing invasive alien species pressures on biodiversity in New South Wales (impact dimension): Data packages for the Biodiversity Indicator Program, first assessment

description: This data collection contains the data inputs, analytical processes and derived outputs including indicator metrics for the first assessment of the impact dimension of the invasive species (pests, weeds, disease) indicator in the New South Wales Biodiversity Indicator Program. If the same workflows and metrics implemented in this first assessment are repeated in future assessments, this will enable change detection over a five-year timescale. Details on indicator design, implementation methods and findings from the first assessment are provided in a technical implementation report: Froese JG, Gooden B, Hulthen AD, Ponce-Reyes R, Burley AL, Cherry H, Hamilton M, Nipperess DA, Russell B, West P & Williams KJ (2021), Assessing invasive alien species pressures on biodiversity in New South Wales, Biodiversity Indicator Program Implementation Report, Department of Planning, Industry and Environment NSW, Sydney, Australia. The key results and highlights of the first assessment are presented in a supplement report card: DPIE (2021), Report card: Invasive species, Supplement to the NSW Biodiversity Outlook Report, Department of Planning, Industry and Environment, Sydney, Australia.
DOI: 10.25919/pavs-0659
title: Assessing invasive alien species pressures on biodiversity in New South Wales (exposure dimension): Data packages for the Biodiversity Indicator Program, first assessment

description: This data collection contains the data inputs, analytical processes and derived outputs including indicator metrics for the first assessment of the exposure dimension of the invasive species (pests, weeds, disease) indicator in the New South Wales Biodiversity Indicator Program. If the same workflows and metrics implemented in this first assessment are repeated in future assessments, this will enable change detection over a five-year timescale. Details on indicator design, implementation methods and findings from the first assessment are provided in a technical implementation report: Froese JG, Gooden B, Hulthen AD, Ponce-Reyes R, Burley AL, Cherry H, Hamilton M, Nipperess DA, Russell B, West P & Williams KJ (2021), Assessing invasive alien species pressures on biodiversity in New South Wales, Biodiversity Indicator Program Implementation Report, Department of Planning, Industry and Environment NSW, Sydney, Australia. The key results and highlights of the first assessment are presented in a supplement report card: DPIE (2021), Report card: Invasive species, Supplement to the NSW Biodiversity Outlook Report, Department of Planning, Industry and Environment, Sydney, Australia.