cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15025913
title: Physically-based dimensionless features for pluvial flood mapping with machine learning

description: Project overview

This is a companion submission for code and data used in the manuscript titled "Physically-based dimensionless features for pluvial flood mapping with machine learning." It includes the data used to develop the conclusions of the aforementioned work as well as a jupyter notebook to calculate the features, traing the logistic regression models, and produce most of the figures. It does not include the GRASS GIS code used to transform the raw data into the feature format we use here. We are happy to assist our readers in the development of these GIS operations by reaching out to us directly.

Code

- `dimensional_vs_dimensionless.ipynb`: Compute dimensionless features, trains logistic regression models for dimensional and nondimensional cases, produces figures and writes them.- `utils.py`: Utility functions used in the jupyter notebook.  

Data

Feather files are a binary columnar data format optimized for use with data frames, particularly in the Python and R programming languages. They are part of the Apache Arrow project and are designed to be fast and efficient for both reading and writing operations.

In this project, a file like huc12_020301030503_features_derivs_local.ftr contains tabular data related to hydrologic unit codes (HUCs). These files store various features and derived metrics for a specific hydrologic unit at a particular resolution (e.g., 10 meters). In this project, the resolution used is 1/3 arc-second which is approximately 10 meters, based on the Digital Elevation Models sourced from USGS 3DEP. The files are organized by HUC12 code and the scale used to calculate the features as explained in the corresponding publication. 

These feather files are read using the pandas library and contain tabular data with the following columns, which correspond to the definitions in Table 1 of the corresponding manuscript:- `x_pix` and `y_pix`: The integer pixel coordinates of the feature within the watershed raster grid. These are used to plot the results but are insufficient to place the results on a map.- `z_d`: The height above nearest drainage (HAND) value at the pixel location in meters.- `y_d`: The distance to the nearest drainage in meters.- `So`: The channel bed slope in meters per meter.- `no`: The Manning's roughness coefficient for the channel bed.- `ac`: The contributing area in square meters.- `A`: The channel cross-section in square meters.- `rh`: The hydraulic radius in meters.- `Ks`: The saturated hydraulic conductivity in meters per second.- `K`: The conveyance in cubic meters per second.- `Kvel`: The conveyance velocity in meters per second.- `Q_runoff_iuh_{interval}`: The runoff volume in cubic meters per second for the specified interval.- `heatmap_pluvial`: The exceedance probability of pluvial flooding. These are derived from two-dimensional HEC-RAS modeling of the watersheds of interest.
DOI: 10.5281/zenodo.15045598
title: Physically-based dimensionless features for pluvial flood mapping with machine learning

description: Project overview

This is a companion submission for code and data used in the manuscript titled "Physically-based dimensionless features for pluvial flood mapping with machine learning." It includes the data used to develop the conclusions of the aforementioned work as well as a jupyter notebook to calculate the features, traing the logistic regression models, and produce most of the figures. It does not include the GRASS GIS code used to transform the raw data into the feature format we use here. We are happy to assist our readers in the development of these GIS operations by reaching out to us directly.

Code

- `dimensional_vs_dimensionless.ipynb`: Compute dimensionless features, trains logistic regression models for dimensional and nondimensional cases, produces figures and writes them.- `utils.py`: Utility functions used in the jupyter notebook.  

Data

Feather files are a binary columnar data format optimized for use with data frames, particularly in the Python and R programming languages. They are part of the Apache Arrow project and are designed to be fast and efficient for both reading and writing operations.

In this project, a file like huc12_020301030503_features_derivs_local.ftr contains tabular data related to hydrologic unit codes (HUCs). These files store various features and derived metrics for a specific hydrologic unit at a particular resolution (e.g., 10 meters). In this project, the resolution used is 1/3 arc-second which is approximately 10 meters, based on the Digital Elevation Models sourced from USGS 3DEP. The files are organized by HUC12 code and the scale used to calculate the features as explained in the corresponding publication. 

These feather files are read using the pandas library and contain tabular data with the following columns, which correspond to the definitions in Table 1 of the corresponding manuscript:- `x_pix` and `y_pix`: The integer pixel coordinates of the feature within the watershed raster grid. These are used to plot the results but are insufficient to place the results on a map.- `z_d`: The height above nearest drainage (HAND) value at the pixel location in meters.- `y_d`: The distance to the nearest drainage in meters.- `So`: The channel bed slope in meters per meter.- `no`: The Manning's roughness coefficient for the channel bed.- `ac`: The contributing area in square meters.- `A`: The channel cross-section in square meters.- `rh`: The hydraulic radius in meters.- `Ks`: The saturated hydraulic conductivity in meters per second.- `K`: The conveyance in cubic meters per second.- `Kvel`: The conveyance velocity in meters per second.- `Q_runoff_iuh_{interval}`: The runoff volume in cubic meters per second for the specified interval.- `heatmap_pluvial`: The exceedance probability of pluvial flooding. These are derived from two-dimensional HEC-RAS modeling of the watersheds of interest.
DOI: 10.18150/1i5ml7
title: Raw DMA data for "The changes in selected properties of flax fibre-reinforced biocomposites affected by plant modifier concentration"

description: Raw dynamic mechanical analysis (DMA) data for the PLA-based materials containing 20% of flax fibres modified with 1%, 5% and 10% of tannic acid. Data also includes DMA files for the neat PLA and PLA containing 20% of non-modified flax fibres.
DOI: 10.15468/dl.aeuph3
title: Occurrence Download

description: A dataset containing 111259 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amaranthus retroflexus L."
 ]
}

 The dataset includes 111259 records from 1326 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014934-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28616189
title: <b>The effects, patterns, and predictors of phosphorus addition on terrestrial litter decomposition</b> raw data.xlsx

description: Anthropogenic phosphorus (P) input profoundly affects carbon (C) and nutrient dynamics in terrestrial ecosystems, which poses a threat to soil health and nutrient sustainability. Litter decomposition is crucial for maintaining soil C and nutrient pools, yet there is a significant knowledge gap regarding the effects of anthropogenic P input on terrestrial litter decomposition.To address this, we conducted a global meta-analysis of 731 observations from 261 field studies to investigate the impact of P addition on litter decomposition. Our analysis revealed that P addition increased the activities of β-1,4-glucosidase (BG) and β-1,4-N-acetylglucosaminidase (NAG) during decomposition.This increase in enzyme activity accelerated the degradation of lignin and cellulose and the release of litter C and nitrogen (N). Ultimately, this resulted in a 6.42% increase in litter mass loss. The positive effect of P addition on litter mass loss was further amplified when combined with N addition. The effects of P addition on litter decomposition varied depending on P fertilizer type (e.g., Ca(H2PO4)2, NaH2PO4, and KH2PO4), P addition level (e.g., &lt;5, 5-10, and &gt;10 g P m-2 year-1), experimental duration (e.g., &lt;12, 12-24, and &gt;24 months), and ecosystem type. Model selection analysis indicated that soil pH was the primary predictor driving global-scale litter decomposition in response to P addition. In conclusion, P addition overall accelerated litter decomposition due to increased activities of BG and NAG. However, the patterns varied with N availability, P fertilizer type, P addition level, experimental duration, and ecosystem type. Future studies should thus consider the effects, patterns, and drivers of P addition on litter decomposition when guiding sustainable practices in nutrient management.
DOI: 10.5281/zenodo.15045516
title: Food webs: edge lists and species names

description: Data consists of six food web networks. Four of them are in the form of edge lists (St. Marks, Florida Bay, Cypress dry season, Mangrove wet season), while the other two are in the form of Python scripts where we write the adjacency matrix link by link (Benguela, Coachella). We also provide the species names. In the case of Cypress dry season, we also provide the species biomasses.

 

References: 

Coachella: G. A. Polis, Complex trophic interactions in deserts: an empirical critique of food-web theory, The American Naturalist 138, 123 (1991).

Benguela: P. Yodzis, Local trophodynamics and the interaction of marine mammals and fisheries in the Benguela ecosystem, Journal of Animal Ecology 67, 635 (1998).

St. Marks: R. R. Christian and J. J. Luczkovich, Organizing and understanding a winter’s seagrass foodweb network through effective trophic levels, Ecological modelling 117, 99 (1999).

Florida Bay, Cypress Dry Season, Mangrove Wet Season: R. Ulanowicz, C. Bondavalli, and M. Egnotovich, Network Analysis of Trophic Dynamics in South Florida Ecosystem, fy 97: The Florida Bay Ecosystem, Annual Report to the United States Geological Service Biological Resources Division. Ref. No. [UMCES]CBL , 98 (1998).

The data of Florida Bay, Cypress Dry Season, Mangrove Wet Season were available at C. L. DuBois, E. S. Spiro, Z. Almquist, M. S. Handcock, D. Hunter, C. T. Butts, S. M. Goodreau, and M. Morris, netdata: A collection of network data (2003)., sourced from V. Batagelj and A. Mrvar, Pajek datasets (2006).
DOI: 10.5281/zenodo.15045517
title: Food webs: edge lists and species names

description: Data consists of six food web networks. Four of them are in the form of edge lists (St. Marks, Florida Bay, Cypress dry season, Mangrove wet season), while the other two are in the form of Python scripts where we write the adjacency matrix link by link (Benguela, Coachella). We also provide the species names. In the case of Cypress dry season, we also provide the species biomasses.

 

References: 

Coachella: G. A. Polis, Complex trophic interactions in deserts: an empirical critique of food-web theory, The American Naturalist 138, 123 (1991).

Benguela: P. Yodzis, Local trophodynamics and the interaction of marine mammals and fisheries in the Benguela ecosystem, Journal of Animal Ecology 67, 635 (1998).

St. Marks: R. R. Christian and J. J. Luczkovich, Organizing and understanding a winter’s seagrass foodweb network through effective trophic levels, Ecological modelling 117, 99 (1999).

Florida Bay, Cypress Dry Season, Mangrove Wet Season: R. Ulanowicz, C. Bondavalli, and M. Egnotovich, Network Analysis of Trophic Dynamics in South Florida Ecosystem, fy 97: The Florida Bay Ecosystem, Annual Report to the United States Geological Service Biological Resources Division. Ref. No. [UMCES]CBL , 98 (1998).

The data of Florida Bay, Cypress Dry Season, Mangrove Wet Season were available at C. L. DuBois, E. S. Spiro, Z. Almquist, M. S. Handcock, D. Hunter, C. T. Butts, S. M. Goodreau, and M. Morris, netdata: A collection of network data (2003)., sourced from V. Batagelj and A. Mrvar, Pajek datasets (2006).
DOI: 10.15468/dl.jev84a
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Baccharis gaucha G.Heiden"
 ]
}

 The dataset includes 14 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014948-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xtwz3p
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.18797 53.43974,6.00237 53.30854,6.18157 53.27814,6.36077 53.27494,6.33517 53.39014,6.22317 53.41254,6.18797 53.43974))",
 "TaxonKey is Hadena bicruris Hufnagel, 1766"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014949-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z5a8md
title: Occurrence Download

description: A dataset containing 1892 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carpophilus hemipterus (Linnaeus, 1758)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1892 records from 145 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014951-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15319205
title: Leveraging large language models for ecological interpretation using an eBird chatbot case study.

description: 1. The anthropocene presents significant challenges for global biodiversity, public health, and long-term ecosystem stability. The wealth of publicly available near-real-time ecology and climate data can be used to monitor these challenges and allow practitioners to develop mitigation strategies.2. There is untapped potential to apply Large Language Models (LLMs) to quantitative ecological and environmental datasets, enabling researchers and practitioners to use natural language queries to transform ecological observations into actionable insights for both conservation action and external communication of results to diverse audiences. Advances in artificial intelligence (AI), and particularly in LLMS, offer emerging opportunities to address these challenges. LLMs are increasingly proficient at identifying patterns and semantic relationships within textual data, and are highly customisable. Accessible AI tools can also facilitate communication across research and policy sectors.3. Here, we present a roadmap for designing and implementing multi-modal LLMs to answer ecological research questions. In order to build ‘virtual statistician’ systems capable of fast-tracking data interpretation, we advocate for strategic planning, data stewardship practices, careful prompt-engineering, and model evaluation as key steps in the LLM development process.4. We showcase a case study that applies the open-source LangChain framework to analyse citizen science data using the eBird database to produce a chatbot allowing the user to ask quantitative questions about near-real-time bird observations. Using our LLM roadmap, we highlight the importance of iterative and strategic prompt engineering and agent selection, in addition to iteratively evaluating model output. As LLM software continues to evolve, their integration into ecological and environmental research can empower ecologists with purpose-built tools that bridge the gap between data collection and actionable solutions.
DOI: 10.5281/zenodo.15319206
title: Leveraging large language models for ecological interpretation using an eBird chatbot case study.

description: 1. The anthropocene presents significant challenges for global biodiversity, public health, and long-term ecosystem stability. The wealth of publicly available near-real-time ecology and climate data can be used to monitor these challenges and allow practitioners to develop mitigation strategies.2. There is untapped potential to apply Large Language Models (LLMs) to quantitative ecological and environmental datasets, enabling researchers and practitioners to use natural language queries to transform ecological observations into actionable insights for both conservation action and external communication of results to diverse audiences. Advances in artificial intelligence (AI), and particularly in LLMS, offer emerging opportunities to address these challenges. LLMs are increasingly proficient at identifying patterns and semantic relationships within textual data, and are highly customisable. Accessible AI tools can also facilitate communication across research and policy sectors.3. Here, we present a roadmap for designing and implementing multi-modal LLMs to answer ecological research questions. In order to build ‘virtual statistician’ systems capable of fast-tracking data interpretation, we advocate for strategic planning, data stewardship practices, careful prompt-engineering, and model evaluation as key steps in the LLM development process.4. We showcase a case study that applies the open-source LangChain framework to analyse citizen science data using the eBird database to produce a chatbot allowing the user to ask quantitative questions about near-real-time bird observations. Using our LLM roadmap, we highlight the importance of iterative and strategic prompt engineering and agent selection, in addition to iteratively evaluating model output. As LLM software continues to evolve, their integration into ecological and environmental research can empower ecologists with purpose-built tools that bridge the gap between data collection and actionable solutions.
DOI: 10.15468/dl.8v472x
title: Occurrence Download

description: A dataset containing 98 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.basisofrecord IN ('OBSERVATION', 'PRESERVED_SPECIMEN', 'OCCURRENCE', 'HUMAN_OBSERVATION')
 AND occurrence.countrycode IN ('US', 'CA')
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 5156102 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 5156102 OR CAST(occurrence.kingdomkey AS INTEGER) = 5156102 OR CAST(occurrence.phylumkey AS INTEGER) = 5156102 OR CAST(occurrence.classkey AS INTEGER) = 5156102 OR CAST(occurrence.orderkey AS INTEGER) = 5156102 OR CAST(occurrence.familykey AS INTEGER) = 5156102 OR CAST(occurrence.genuskey AS INTEGER) = 5156102 OR CAST(occurrence.specieskey AS INTEGER) = 5156102)
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 98 records were derived from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004463-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vttnjm
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Silybum marianum",
 "Country is Pakistan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 76 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004498-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnh6g3
title: Occurrence Download

description: A dataset containing 2839 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Robinia pseudoacacia",
 "HasCoordinate is true",
 "Country is Portugal"
 ]
}

 The dataset includes 2839 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004496-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vckk9x
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Silybum marianum",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 275 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004500-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11588/portal.gsi.68e8c04cc174f
title: Kunsthandlung Karoline Anny Lang
DOI: 10.15468/dl.23s6q7
title: Occurrence Download

description: A dataset containing 13074244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 13074244 records from 5572 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004341-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/independent-c69-24241
title: Case summaries of the 83 selected cases (1986-1990) (1 & 2 of 4), 1991 May 23-31

description: Packet - Case summaries of the 83 selected cases by the Independent Commission - cases against LAPD (1 & 2 of 4), 1991 May 23-31. PART OF A SERIES: Latham & Watkins researched a total of 83 civil litigation cases brought against the LAPD pertaining to excessive force and/or improper tactics to ascertain whether the Rodney King incident was in fact the aberration that Chief Daryl Gates described. Included in the series are several materials related to the firm’s investigation and final work product. Specifically, the series contains source materials related to the 83 cases that were studied. These materials include the firm’s own memoranda and notes, statistics on misconduct and police brutality, summaries of the selected cases, recommendations for settlement drafted by the Office of the City Attorney, docket cards, copies of settlement agreements, a master roster of officers named in the civil suits, and excerpts from officers’ personnel files. Also included are summaries of interviews held with police personnel and aggrieved citizens about their perception of problems within the LAPD.