cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zd3crfys9w
title: RESEARCH DATA

description: This dataset was generated to investigate the effects of experimental warming and nitrogen addition, as well as their interactive effects, on key soil nitrogen mineralization processes, using a natural control (CK) as a reference. Specifically, the dataset focuses on the responses of digestion, ammonification, and nitrification processes under nitrogen addition (N), warming (W), and combined warming and nitrogen addition (W×N) treatments. The underlying hypothesis is that warming and nitrogen inputs alter soil physicochemical conditions and microbially mediated enzyme processes, thereby regulating inorganic nitrogen availability and nitrogen mineralization rates.
The data were obtained from a combination of field manipulation experiments and laboratory incubation experiments. Four treatments were established: natural control (CK), nitrogen addition (N), warming (W), and warming × nitrogen addition (W×N). Soil samples were collected periodically under each treatment. Measurements included soil total nitrogen (TN), ammonium nitrogen (NH₄⁺–N), and nitrate nitrogen (NO₃⁻–N). Based on changes in inorganic nitrogen concentrations before and after incubation, digestion rate, ammonification rate, nitrification rate, and net nitrogen mineralization rate were calculated. In addition, soil enzyme activities related to nitrogen transformation were measured to characterize the microbial potential driving soil nitrogen cycling.
The dataset also includes concurrently measured soil temperature and soil moisture, which were used to characterize environmental conditions under different treatments and to assess their regulatory effects on soil nitrogen mineralization processes. All variables were obtained using consistent sampling, analytical, and quality control procedures. Data are reported in internationally accepted units, and missing or abnormal values are clearly identified in the data files.
This dataset can be used to evaluate the mechanisms underlying soil nitrogen mineralization responses to warming and nitrogen addition under global change scenarios, and it provides a valuable data source for nitrogen cycling model parameterization, multi-factor interaction analyses, and cross-site comparative studies.
DOI: 10.6078/d1hh7r
title: Genomic and phenotypic signatures of bacteriophage coevolution with the phytopathogen Pseudomonas syringae

description: The rate and trajectory of evolution in an obligate parasite is critically
 dependent on those of its host(s). Adaptation to a genetically homogeneous
 host population should theoretically result in specialization, while
 adaptation to an evolving host population (i.e., coevolution) can result
 in various outcomes including diversification, range expansion, and/or
 local adaptation. For viruses of bacteria (bacteriophages, or phages), our
 understanding of how evolutionary history of the bacterial host(s) impacts
 viral genotypic and phenotypic evolution is currently limited. In this
 study, we used whole genome sequencing and two different metrics of phage
 impacts to compare the genotypes and phenotypes of lytic phages that had
 either coevolved with or were repeatedly passaged on an unchanging
 (ancestral) strain of the phytopathogen Pseudomonas syringae. Genomes of
 coevolved phages had more mutations than those of phages passaged on a
 constant host, and most mutations were in genes encoding phage
 tail-associated proteins. Phages from both passaging treatments shared
 some phenotypic outcomes, including range expansion and divergence across
 replicate populations, but coevolved phages were more efficient at
 reducing population growth (particularly of sympatric coevolved hosts).
 Genotypic similarity correlated with infectivity profile similarity in
 coevolved phages, but not in phages passaged on the ancestral host.
 Overall, while adaptation to either host type (coevolving or ancestral)
 led to divergence in phage tail proteins and infectivity patterns,
 coevolution led to more rapid molecular changes that increased bacterial
 killing efficiency and had more predictable effects on infectivity range.
 Together, these results underscore the important role of hosts in driving
 viral evolution and in shaping the genotype-phenotype relationship.
DOI: 10.17632/zd3crfys9w.1
title: RESEARCH DATA

description: This dataset was generated to investigate the effects of experimental warming and nitrogen addition, as well as their interactive effects, on key soil nitrogen mineralization processes, using a natural control (CK) as a reference. Specifically, the dataset focuses on the responses of digestion, ammonification, and nitrification processes under nitrogen addition (N), warming (W), and combined warming and nitrogen addition (W×N) treatments. The underlying hypothesis is that warming and nitrogen inputs alter soil physicochemical conditions and microbially mediated enzyme processes, thereby regulating inorganic nitrogen availability and nitrogen mineralization rates.
The data were obtained from a combination of field manipulation experiments and laboratory incubation experiments. Four treatments were established: natural control (CK), nitrogen addition (N), warming (W), and warming × nitrogen addition (W×N). Soil samples were collected periodically under each treatment. Measurements included soil total nitrogen (TN), ammonium nitrogen (NH₄⁺–N), and nitrate nitrogen (NO₃⁻–N). Based on changes in inorganic nitrogen concentrations before and after incubation, digestion rate, ammonification rate, nitrification rate, and net nitrogen mineralization rate were calculated. In addition, soil enzyme activities related to nitrogen transformation were measured to characterize the microbial potential driving soil nitrogen cycling.
The dataset also includes concurrently measured soil temperature and soil moisture, which were used to characterize environmental conditions under different treatments and to assess their regulatory effects on soil nitrogen mineralization processes. All variables were obtained using consistent sampling, analytical, and quality control procedures. Data are reported in internationally accepted units, and missing or abnormal values are clearly identified in the data files.
This dataset can be used to evaluate the mechanisms underlying soil nitrogen mineralization responses to warming and nitrogen addition under global change scenarios, and it provides a valuable data source for nitrogen cycling model parameterization, multi-factor interaction analyses, and cross-site comparative studies.
DOI: 10.5061/dryad.w0vt4b8w9
title: Topological kinetic crossover in a nanomagnet array

description: Common explanations of equilibrium thermodynamics depend critically on
 ergodicity, i.e., the ability to explore all available states, but ergodic
 kinetics can be constrained by a system’s topology. We report an
 experimental study of a model nanomagnetic array, in which such
 constraints visibly affect the behavior. In this system, magnetic
 excitations are connected in thermally active one-dimensional strings
 whose motion can be imaged in real-time. At high temperatures, the
 kinetics include the merging, breaking, and reconnecting of strings,
 resulting in the system transitioning between topologically distinct
 configurations. Below a crossover temperature, however, the string motion
 is dominated by simple changes in length and shape. In this
 low-temperature regime, the system is energetically stable because of the
 inability to explore the full set of possible topological configurations.
 This kinetic crossover suggests a generalizable conception of
 topologically broken ergodicity and limited equilibration.
description: To prepare the samples, we use electron-beam lithography to write
 patterns on Si/SiO x substrates with spin-coated bilayer resists. A layer
 of ~ 2.5 nm thick permalloy
 (Ni<sub>80</sub>Fe<sub>20</sub>) was then
 deposited into the patterns via ultrahigh vacuum electron beam
 evaporation, followed by aluminum capping layers of 2 nm thick to prevent
 oxidation of the underlying permalloy. The moment
 configuration in our samples was measured using x-ray magnetic circular
 dichroism photoemission electron microscopy (XMCD-PEEM), which yields
 real-space images of the island magnetic moment,  conducted at the PEEM-3
 endstation at beamline 11.0.1.1 of the Advanced Light Source, Lawrence
 Berkeley National Lab. We heated the sample from 250 K to 360 K in 5–10 K
 steps and took 100 PEEM images at the Fe L 3 absorption edge at each
 temperature point. The 100 PEEM images consisted of ten exposures with a
 left-circularly polarized X-ray beam followed by ten exposures with a
 right-circularly polarized beam, repeated five times. Each image takes 0.5
 seconds, and then there is a delay of 0.5 seconds between images (assuming
 polarization is not switched), so the time period of the imaging is 1
 second. The PEEM image field of view was set at 15  µm x 15 µm to 18  µm x
 18 µm and there were about 500 islands within each image. We used MATLAB
 and Python code to extract the moment direction for each island and
 emergent strings configuration from every PEEM image. By comparing the
 string configurations between successive PEEM images, we could therefore
 observe the thermal kinetics of this system. We have
 calculated the magnetostatic energy using the micromagnetic simulation
 program MuMax3. While doing the simulations, the saturation magnetization
 is set to be 8.6x10<sup>5</sup> A/m, the exchange constant is
 1.3x10<sup>-11</sup> J/m, and the magnetocrystalline
 anisotropy is negligible for permalloy. More detailed
 information about experimental methods can be found in the related
 publication of this dataset.
DOI: 10.17632/hkcyvhvmwp
title: SaurKshetra: A Dataset for Solar Farms Potential Site Mapping using Suitability parameters in India

description: This dataset tests the hypothesis that long-term reanalysis of environmental and solar irradiance variables can reliably indicate relative solar energy potential across India in the absence of dense ground measurements. Final Dataset.xlsx (Jan 2013–Dec 2022) contains sitewise time series of solar irradiance, air temperature, cloud cover, albedo, precipitation, latitude, longitude and timestamp, all extracted from NASA POWER, temporally aligned, unit-standardized, and cleaned for missing values. Exploratory analysis shows clear seasonal and regional patterns (monsoon-driven drops in irradiance, latitudinal gradients) useful for comparing sites and assessing inter-annual variability. Use the data for geospatial site screening, clustering, ML-based suitability or predictive models — treat values as indicative (regional/relative) and validate site-level conclusions with in-situ measurements where possible.
DOI: 10.17632/hkcyvhvmwp.5
title: SaurKshetra: A Dataset for Solar Farms Potential Site Mapping using Suitability parameters in India

description: This dataset tests the hypothesis that long-term reanalysis of environmental and solar irradiance variables can reliably indicate relative solar energy potential across India in the absence of dense ground measurements. Final Dataset.xlsx (Jan 2013–Dec 2022) contains sitewise time series of solar irradiance, air temperature, cloud cover, albedo, precipitation, latitude, longitude and timestamp, all extracted from NASA POWER, temporally aligned, unit-standardized, and cleaned for missing values. Exploratory analysis shows clear seasonal and regional patterns (monsoon-driven drops in irradiance, latitudinal gradients) useful for comparing sites and assessing inter-annual variability. Use the data for geospatial site screening, clustering, ML-based suitability or predictive models — treat values as indicative (regional/relative) and validate site-level conclusions with in-situ measurements where possible.
DOI: 10.5061/dryad.qjq2bvqkx
title: Multiple global change factors cause declines of a temperate bryophyte

description: Climate changes, nutrient enrichment, or land use have been predicted to
 affect bryophytes, yet factorial experiments on their effects are missing.
 We hypothesised that future climatic conditions, intense grazing, light
 limitation, nutrient enrichment have negative effects on the survival and
 photosynthetic condition of a common temperate bryophyte, and their joint
 effects become strong even if individual factors have only weak effects.
 We tested the effects of future climatic conditions, grazing, light
 limitation, and nutrient on a common temperate bryophyte (Brachythecium
 rutabulum) as well as the multiple stress hypothesis. We measured biomass
 and chlorophyll fluorescence of transplanted moss colonies after two
 growing seasons’ full-factorial treatments of fertilization, exclusion of
 sheep grazing, and light addition by LED lamps, replicated in ambient and
 future climate conditions arranged in a global change experimental
 facility. Future climate and fertilization had negligible effects on
 colony biomass and chlorophyll fluorescence of bryophyte colonies, whereas
 light addition and grazing exclusion had positive effects. Colony biomass
 and chlorophyll fluorescence decreased with the increasing number of
 global change factors. Supporting the multi-stress hypothesis, the effects
 of individually weak global change factors on bryophytes can become strong
 under multiple global change factors operating in concert.
description: ## Data description Bryophyte transplant colonies subjected to multiple
 global change treatments in GCEF experimental facility (Bad Lauchstdt,
 Germany) The \*.csv file contains variables describing the experimental
 design * blocking * climate change treatment (future, ambient) * nutrient
 addition treatment (fertilized, not fertilized) * grazing exclusion
 (fenced, unfenced) * light addition (added, not added) The response
 variables include chlorophyll fluorescence (Fv/Fm) and colony biomass (g
 DW) For further details of treatments, please see the original paper
DOI: 10.17632/zbd5ttpyv3
title: Dataset of Reasoning Abilities on Counting and Non-Counting Sequences in 80 Second-Grade Primary School Students

description: Hypothesis: We hypothesized that 2nd graders would perform better on familiar number patterns, that their reasoning skill would link to basic math skills but not anxiety, and that distinct ability profiles exist.

Data & Methods: 80 second-graders completed online tasks measuring: 1) Math Anxiety (questionnaire), 2) Number Sense (comparing numbers), 3) Arithmetic Fluency (speeded math facts), and 4) Numerical Inductive Reasoning (identifying rules in number sequences like "2,4,6,8").

Key Findings:

Rule Recognition: Accuracy was higher for familiar "counting" sequences (e.g., 2,4,6,8) than unfamiliar ones (e.g., 1,4,7,10).

Unique Pattern: For unfamiliar sequences, children were better at spotting slightly wrong sequences (e.g., 2,3,6,8) than correct ones. This suggests their ability to "check" for errors helps when calculating is hard.

Skill Links: Reasoning performance was positively correlated with Number Sense and Arithmetic Fluency. No link was found with Math Anxiety.

Four Profiles: Analysis revealed four distinct cognitive profiles among students:

Comprehensively Excellent (18%)

Number Sense Strong / Arithmetic Weak (39%)

Overall Difficulties (29%)

Severely Lagging Number Sense (15%)
These groups did not differ in their math anxiety levels.

Interpretation & Use:
The results show that even young children use different mental processes (recognizing, calculating, checking) for number reasoning. The lack of an anxiety link in this age group suggests early intervention can focus on cognitive skills. Most importantly, children have diverse cognitive strengths and weaknesses (profiles), which are not reflected in a single math score. This supports differentiated teaching: for example, providing extra fact practice for the "Arithmetic Weak" group, and foundational number concept support for the "Lagging Number Sense" group.
DOI: 10.5061/dryad.15dv41p1x
title: Data for: Determinants of natal dispersal distances in North American birds

description: Natal dispersal—the movement from birth site to first breeding
 site—determines demographic and population genetic dynamics and has
 important consequences for ecological and evolutionary processes. Recent
 work suggested that one of the main factors determining natal dispersal
 distances is the cost of locomotion. We evaluated this hypothesis using
 band-recovery data to estimate natal dispersal distances for 50 North
 American bird species. We then analysed the relationships between
 dispersal distances and a suite of morphological and ecological
 predictors, including proxies for the cost of locomotion (flight
 efficiency), using phylogenetic regression models. We found that flight
 efficiency, population size and habitat influence natal dispersal
 distances. We discuss how the effects of population size and habitat can
 also be related to mobility and locomotion. Our findings are consistent
 with a predominant effect of adaptations for mobility on dispersal
 distances.
DOI: 10.17632/zbd5ttpyv3.1
title: Dataset of Reasoning Abilities on Counting and Non-Counting Sequences in 80 Second-Grade Primary School Students

description: Hypothesis: We hypothesized that 2nd graders would perform better on familiar number patterns, that their reasoning skill would link to basic math skills but not anxiety, and that distinct ability profiles exist.

Data & Methods: 80 second-graders completed online tasks measuring: 1) Math Anxiety (questionnaire), 2) Number Sense (comparing numbers), 3) Arithmetic Fluency (speeded math facts), and 4) Numerical Inductive Reasoning (identifying rules in number sequences like "2,4,6,8").

Key Findings:

Rule Recognition: Accuracy was higher for familiar "counting" sequences (e.g., 2,4,6,8) than unfamiliar ones (e.g., 1,4,7,10).

Unique Pattern: For unfamiliar sequences, children were better at spotting slightly wrong sequences (e.g., 2,3,6,8) than correct ones. This suggests their ability to "check" for errors helps when calculating is hard.

Skill Links: Reasoning performance was positively correlated with Number Sense and Arithmetic Fluency. No link was found with Math Anxiety.

Four Profiles: Analysis revealed four distinct cognitive profiles among students:

Comprehensively Excellent (18%)

Number Sense Strong / Arithmetic Weak (39%)

Overall Difficulties (29%)

Severely Lagging Number Sense (15%)
These groups did not differ in their math anxiety levels.

Interpretation & Use:
The results show that even young children use different mental processes (recognizing, calculating, checking) for number reasoning. The lack of an anxiety link in this age group suggests early intervention can focus on cognitive skills. Most importantly, children have diverse cognitive strengths and weaknesses (profiles), which are not reflected in a single math score. This supports differentiated teaching: for example, providing extra fact practice for the "Arithmetic Weak" group, and foundational number concept support for the "Lagging Number Sense" group.
DOI: 10.17632/7mxbh6d68v
title: Whole-Genome Sequencing Identifies a Novel Deep Intronic ADAMTS17 Variant in Weill–Marchesani Syndrome 4

description: Background: Weill-Marchesani syndrome 4 (WMS4) is a rare autosomal recessive disorder caused by ADAMTS17 variants, yet deep intronic variants and their splicing-disruptive effects remain poorly explored, limiting diagnostic yields.
Results: Whole-genome sequencing identified one known variant (c.1655G&gt;A) and one novel deep intronic variant (c.450+38C&gt;A) in a WMS4 patient with classic ocular and skeletal phenotypes; splicing assays confirmed the intronic variant induces abnormal pre-mRNA processing and frameshift.
Conclusions: Our findings expand ADAMTS17’s mutational spectrum, underscore the value of integrating whole-genome sequencing with splicing validation, and provide critical insights for WMS4 molecular diagnosis.
DOI: 10.17632/7mxbh6d68v.1
title: Whole-Genome Sequencing Identifies a Novel Deep Intronic ADAMTS17 Variant in Weill–Marchesani Syndrome 4

description: Background: Weill-Marchesani syndrome 4 (WMS4) is a rare autosomal recessive disorder caused by ADAMTS17 variants, yet deep intronic variants and their splicing-disruptive effects remain poorly explored, limiting diagnostic yields.
Results: Whole-genome sequencing identified one known variant (c.1655G&gt;A) and one novel deep intronic variant (c.450+38C&gt;A) in a WMS4 patient with classic ocular and skeletal phenotypes; splicing assays confirmed the intronic variant induces abnormal pre-mRNA processing and frameshift.
Conclusions: Our findings expand ADAMTS17’s mutational spectrum, underscore the value of integrating whole-genome sequencing with splicing validation, and provide critical insights for WMS4 molecular diagnosis.
DOI: 10.5061/dryad.5x69p8d7j
title: Data for: Inadequate sampling of the soundscape leads to overoptimistic estimates of recogniser performance: A case study of two sympatric macaw species

description: Passive acoustic monitoring (PAM) offers the potential to dramatically
 increase the scale and robustness of species monitoring in rainforest
 ecosystems. PAM generates large volumes of data that require automated
 methods of target species detection. Species-specific recognisers, which
 often use supervised machine learning, can achieve this goal. However,
 they require a large training dataset of both target and non-target
 signals, which is time-consuming and challenging to create. Unfortunately,
 very little information about creating training datasets for supervised
 machine learning recognisers is available, especially for tropical
 ecosystems. Here we show an iterative approach to creating a training
 dataset that improved recogniser precision from 0.12 to 0.55. By sampling
 background noise using an initial small recogniser, we addressed one of
 the significant challenges of training dataset creation in acoustically
 diverse environments. Our work demonstrates that recognisers will likely
 fail in real-world settings unless the training dataset size is large
 enough and sufficiently representative of the ambient soundscape. We
 outline a simple workflow that can provide users with an accessible way to
 create a species-specific PAM recogniser that addresses these issues for
 tropical rainforest environments. Our work provides important lessons for
 PAM practitioners wanting to develop species-specific recognisers for
 acoustically diverse ecosystems.
description: Raw data used to create this dataset was collected from
 autonomous recording units in northern Costa Rica.  A
 template-matching process was used to identify candidate signals, then a
 one-second window was put around each candidate signal. We extracted a
 total of 113 acoustic features using the warbler package in R (R Core
 Team, 2020): 20 measurements of frequency, time, and amplitude parameters,
 and 93 Mel-frequency cepstral coefficients (MFCCs) (Araya‐Salas and
 Smith‐Vidaurre, 2017). This dataset also includes the
 results of manually checking detections that were the output of a trained
 random forest. These were initially output as selection tables, individual
 sound files were loaded in Raven Lite, selection tables were loaded, and
 each detection was manually checked and labelled. There
 is also the random forest model, which is a .rds format model created
 using tidymodels in R. 
description: Following the code associated with this data will require R; the
 outputs from the machine learning require Raven Lite to open. The raw
 recordings are not included in this dataset.